diff --git a/CHANGELOG.md b/CHANGELOG.md index e1e74c1..9d0078a 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,6 +2,21 @@ All notable changes to the Symposium package. +## [3.2.0] — 2026-11-01 (release candidate) + +- Refresh the public program candidate to 57 presentations and 18 explicit UTC + sessions, with 45 participant profiles projected from current affirmative consent. +- Regenerate dossiers, participant briefs, the UTC calendar, and draft PDF/Word + abstract books; retain pending abstracts and the recorded portrait omission. +- Support typed Docs datetime epochs as UTC seconds while rejecting malformed + epochs and continuing to require offsets on plain timestamp strings. +- Preserve submitted PDF glyphs with staged font bytes and reject missing-glyph + compiler diagnostics; keep prior failed renders as private review evidence. +- Show the source capture's UTC date and live-program revision authority in the + explorer; bind the optional snapshot date to capture time at public staging. +- Keep organizer approval of the exact candidate commit as the merge, tag, and + deployment gate. + ## [3.1.0] — 2026-10-27 - Add a validated catalog connecting nine methods/workflows to ten public repositories, diff --git a/README.md b/README.md index 875abfc..9932d93 100644 --- a/README.md +++ b/README.md @@ -241,7 +241,7 @@ mapping, approval checks, native abstract-book production, and deployment bounda @software{symposium2026, title = {Symposium: Research Analysis Tools for Active Inference Symposium}, author = {Active Inference Institute}, - version = {3.1.0}, + version = {3.2.0}, year = {2026}, url = {https://github.com/ActiveInferenceInstitute/Symposium} } diff --git a/data/public/2026/assets/portraits/00f3ab9b83d67462ccea7c6a47117ac7c5da72779236bc3c2257b8a5cfb04b70.jpg b/data/public/2026/assets/portraits/00f3ab9b83d67462ccea7c6a47117ac7c5da72779236bc3c2257b8a5cfb04b70.jpg new file mode 100644 index 0000000..8a0581b Binary files /dev/null and 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5903ad8..34e3225 100644 Binary files a/data/public/2026/book.docx and b/data/public/2026/book.docx differ diff --git a/data/public/2026/book.pdf b/data/public/2026/book.pdf index b112d0a..06e2448 100644 Binary files a/data/public/2026/book.pdf and b/data/public/2026/book.pdf differ diff --git a/data/public/2026/dossiers/talk-1176205d9547addb.md b/data/public/2026/dossiers/talk-1176205d9547addb.md new file mode 100644 index 0000000..b6f3b3e --- /dev/null +++ b/data/public/2026/dossiers/talk-1176205d9547addb.md @@ -0,0 +1,38 @@ +# GNN in 2026: Generalized Notation Notation as a Specification Layer for Applied Active Inference + +Presenter: Daniel Friedman + +Contribution: Pre-recorded Video presentation + +Public ID: talk-1176205d9547addb + +## Submitted abstract + +Generalized Notation Notation (GNN) was introduced in 2023 (Smekal & Friedman) as a text-based standard for writing Active Inference generative models, with a “Triple Play” of linguistic, visual and executable renderings. This talk reports where GNN stands in 2026 and what it enables for applied Active Inference. +A GNN file is plain Markdown: a state-space block declaring variables and dimensions, a connections graph, initial parameterization (e.g. A, B, C, D, E and related terms), equations, time settings and Active Inference Ontology annotations. The open-source GNN pipeline (v5, 25 steps) parses, type-checks and validates these files, visualizes and exports them, maps them to the ontology, renders them to backends including PyMDP, RxInfer.jl, ActiveInference.jl and JAX, executes the results, and reports each run against source hashes and configuration. The repository’s 38 exemplar models span static and dynamic perception, discrete POMDPs, the epistemic T-maze, continuous and hybrid models, hierarchical and deep temporal models, Dirichlet learning, precision and curiosity, and multi-agent stigmergic swarms. +The core is GNN as the specification layer for real projects. The Active Inference Language Model (AILM, released October 2026) uses GNN to specify each layer of a six-level hierarchical discrete language system (letters, phonemes, words, case roles, sentence types, document position), executed in pymdp with no transformer, neural network or learned embedding, and checked with a gate-audit method that tests claims against trivial baselines and deliberately broken variants and ties each reported number to its recorded run. Shorter case studies: deterministic codebase-to-GNN translation (COGANT), inference over assembly traces (Active Assembly), Battleship, and gratitude and model revisability (Thanks for Nothing). +Throughout, I distinguish what a GNN declaration establishes (an inspectable, reproducible model) from what needs separate evidence (backend support, numerical acceptance, empirical claims). I close with open problems and invitations for contributors: new exemplars, backends, ontology mappings and teaching materials. + +Source: [submitted source]() + +Abstract SHA256: d7c3fd3883153a0fe675a1237a52b9b027b56d7ab9058d94a4b4e1a517b61f04 + +## Organizer review checklist + +- [ ] Confirm the submitted title, presenter, and abstract with the presenter. +- [ ] Confirm contribution format and scheduling needs. +- [ ] Confirm public permissions for any additional material before publication. +- [ ] Prepare discussion questions grounded in the submitted abstract. + +## Public learning resources + +These approved event resources are starting points; topic relevance and author identity have not been inferred. + +- Active Inference Ontology: https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology +- Cognitive Agent Modeling: https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations +- GEO-INFER: https://github.com/ActiveInferenceInstitute/GEO_INFER +- Generalized Notation Notation: https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation +- pymdp: https://github.com/infer-actively/pymdp +- Reproducible publication template: https://github.com/docxology/template + +OpenAlex discovery-only search (not a verified identity or literature match): https://openalex.org/works?search=GNN%20in%202026%3A%20Generalized%20Notation%20Notation%20as%20a%20Specification%20Layer%20for%20Applied%20Active%20Inference diff --git a/data/public/2026/dossiers/talk-15d7c576be7d71da.md b/data/public/2026/dossiers/talk-15d7c576be7d71da.md new file mode 100644 index 0000000..2cad711 --- /dev/null +++ b/data/public/2026/dossiers/talk-15d7c576be7d71da.md @@ -0,0 +1,38 @@ +# What becomes available next? + +Presenter: Tiga Kailimai + +Contribution: Pre-recorded Video presentation + +Public ID: talk-15d7c576be7d71da + +## Submitted abstract + +I'm a Holistic Health Practitioner and independent researcher on Hawaiʻi Island. This talk brings one open question to the Active Inference community: when a living system learns, does it only move differently through a possibility space that already existed, or can learning reorganize the space itself, changing what becomes available to perceive, generate, or do next? +I come to the question from two directions. The first is physiology. In a 15-month longitudinal study of working first responders, still in design and not yet under review, the question is whether recovery changes which states and actions become available afterward, not only which state a person is in. Nothing has been measured yet. The second is method. In my own cross-domain research process, I carry a relation into another field, try to find where it breaks, and let that information change the next question. The history of that process seems to change which distinctions I can draw next. I present that as method, not as evidence about mechanism. +Same grammar, not yet the same mechanism. Several formal lenses meet here: viability theory and reachable sets work on how the landscape of reachable states is reshaped, the difference between changing what a system generates and what it selects, and recent Active Inference accounts of insight as non-local search. +So the talk ends on a request rather than a result: what observation would distinguish a system moving through a space from a system whose space has changed, and what does Active Inference make of that difference? + +Source: [submitted source]() + +Abstract SHA256: 9a4aaceb2abc6bcbd344fe9dcf6b70c7a2ae02d3ca789617f3edb53c2a7f5f4d + +## Organizer review checklist + +- [ ] Confirm the submitted title, presenter, and abstract with the presenter. +- [ ] Confirm contribution format and scheduling needs. +- [ ] Confirm public permissions for any additional material before publication. +- [ ] Prepare discussion questions grounded in the submitted abstract. + +## Public learning resources + +These approved event resources are starting points; topic relevance and author identity have not been inferred. + +- Active Inference Ontology: https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology +- Cognitive Agent Modeling: https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations +- GEO-INFER: https://github.com/ActiveInferenceInstitute/GEO_INFER +- Generalized Notation Notation: https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation +- pymdp: https://github.com/infer-actively/pymdp +- Reproducible publication template: https://github.com/docxology/template + +OpenAlex discovery-only search (not a verified identity or literature match): https://openalex.org/works?search=What%20becomes%20available%20next%3F diff --git a/data/public/2026/dossiers/talk-17082b710a933608.md b/data/public/2026/dossiers/talk-17082b710a933608.md new file mode 100644 index 0000000..bd3dd9b --- /dev/null +++ b/data/public/2026/dossiers/talk-17082b710a933608.md @@ -0,0 +1,35 @@ +# What bounds the surprisal? Zones of Bounded Surprisal, self-maintenance and the informational turn in anthropogenesis + +Presenter: Tao Gong + +Contribution: Pre-recorded Video presentation + +Public ID: talk-17082b710a933608 + +## Submitted abstract + +The zone of bounded surprisal (ZBS), introduced by Manrique and Walker and developed with Friston, describes how much surprising input an agent can absorb under active inference. A narrow zone has been proposed to limit the faithful copying of novel behaviour in great apes, and very precise priors, which narrow the zone, to characterise discriminatory minds. In active-inference terms the zone is the set of outcomes whose surprisal stays under a bound. What sets that bound, and why crossing it costs the agent anything, is left open. We take up this question using four papers published or accepted in 2026 that develop Terrence Deacon’s teleodynamic account of information. A self-produced Markov blanket institutes the epistemic cut, so the zone belongs to the system that draws its own boundary. Function, understood as the at-risk regeneration of a capacity, gives that system a stake. The bound can then be read as the largest surprisal an agent absorbs without putting this capacity at risk, while precision sets how wide the zone is beneath it. Read together with Walker’s Homo informatio model, the account explains why width pays: constructive information saves work later and for others, and only an agent with a wide zone can hold it long enough to learn its use. It also raises a testable tension. The constructive information that a broader zone made possible over evolution may narrow the zone within a life when it is held with high precision, and informational inbreeding may be its counterpart at group scale. We propose three measurements, including one that holds surprisal fixed while varying what it threatens, and close with questions for discussion. + +Source: [submitted source]() + +Abstract SHA256: b2b22c06394a743898a05cbe4a9c017bf0bf85d9f6ebd37966d2904e5e7909dd + +## Organizer review checklist + +- [ ] Confirm the submitted title, presenter, and abstract with the presenter. +- [ ] Confirm contribution format and scheduling needs. +- [ ] Confirm public permissions for any additional material before publication. +- [ ] Prepare discussion questions grounded in the submitted abstract. + +## Public learning resources + +These approved event resources are starting points; topic relevance and author identity have not been inferred. + +- Active Inference Ontology: https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology +- Cognitive Agent Modeling: https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations +- GEO-INFER: https://github.com/ActiveInferenceInstitute/GEO_INFER +- Generalized Notation Notation: https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation +- pymdp: https://github.com/infer-actively/pymdp +- Reproducible publication template: https://github.com/docxology/template + +OpenAlex discovery-only search (not a verified identity or literature match): https://openalex.org/works?search=What%20bounds%20the%20surprisal%3F%20Zones%20of%20Bounded%20Surprisal%2C%20self-maintenance%20and%20the%20informational%20turn%20in%20anthropogenesis diff --git a/data/public/2026/dossiers/talk-29a71ff7cce585e0.md b/data/public/2026/dossiers/talk-29a71ff7cce585e0.md new file mode 100644 index 0000000..3b8c605 --- /dev/null +++ b/data/public/2026/dossiers/talk-29a71ff7cce585e0.md @@ -0,0 +1,35 @@ +# Stimming as a Temporary Homeostatic Buffer and Modulator in Autism Spectrum Disorders + +Presenter: Diaconescu Adrian-Stelian + +Contribution: Pre-recorded Video presentation + +Public ID: talk-29a71ff7cce585e0 + +## Submitted abstract + +Repetitive and self-stimulatory behaviors have been re-evaluated over time, evolving from simple dysfunctions to adaptive self-regulatory strategies, however, a unified biological framework capable of explaining their contextual activation and underlying physiology remains incomplete. The present paper proposes a theoretical-hypothetical model (grounded in the Free Energy Principle and the Active Inference framework) that conceptualizes stimming as serving a dual role: both as a temporary homeostatic buffer, activated during states of prefrontal executive overload, and sensorimotor depletion and as a modulator of attentional processes and information processing. Distinctions are delineated between these forms of stimming and the non-creative flow ( FNC/NCF) state, alongside specific considerations regarding neurodevelopmental conditions. + +Source: [submitted source]() + +Abstract SHA256: 6c40e92cabf5348bae014263b3e03f158b272627c284218e063b3455a9e3469a + +## Organizer review checklist + +- [ ] Confirm the submitted title, presenter, and abstract with the presenter. +- [ ] Confirm contribution format and scheduling needs. +- [ ] Confirm public permissions for any additional material before publication. +- [ ] Prepare discussion questions grounded in the submitted abstract. + +## Public learning resources + +These approved event resources are starting points; topic relevance and author identity have not been inferred. + +- Active Inference Ontology: https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology +- Cognitive Agent Modeling: https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations +- GEO-INFER: https://github.com/ActiveInferenceInstitute/GEO_INFER +- Generalized Notation Notation: https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation +- pymdp: https://github.com/infer-actively/pymdp +- Reproducible publication template: https://github.com/docxology/template + +OpenAlex discovery-only search (not a verified identity or literature match): https://openalex.org/works?search=Stimming%20as%20a%20Temporary%20Homeostatic%20Buffer%20and%20Modulator%20in%20Autism%20Spectrum%20Disorders diff --git a/data/public/2026/dossiers/talk-877eaccf6e0007ec.md b/data/public/2026/dossiers/talk-29cbf4c7ed2a35fb.md similarity index 89% rename from data/public/2026/dossiers/talk-877eaccf6e0007ec.md rename to data/public/2026/dossiers/talk-29cbf4c7ed2a35fb.md index 00067c9..03de859 100644 --- a/data/public/2026/dossiers/talk-877eaccf6e0007ec.md +++ b/data/public/2026/dossiers/talk-29cbf4c7ed2a35fb.md @@ -1,12 +1,10 @@ -# ROIF and Active Inference: The Formal Boundary of History-Conditioned Anticipatory Systems. - -History-dependent reconfiguration, structured memory, and predictive preconfiguration in prestressed complex systems +# ROIF and Active Inference: The Formal Boundary of History-Conditioned Anticipatory Systems. ​ History-dependent reconfiguration, structured memory, and predictive preconfiguration in prestressed complex systems Presenter: Vitalii Shapovalov Contribution: Pre-recorded Video presentation -Public ID: talk-877eaccf6e0007ec +Public ID: talk-29cbf4c7ed2a35fb ## Submitted abstract @@ -37,4 +35,4 @@ These approved event resources are starting points; topic relevance and author i - pymdp: https://github.com/infer-actively/pymdp - Reproducible publication template: https://github.com/docxology/template -OpenAlex discovery-only search (not a verified identity or literature match): https://openalex.org/works?search=ROIF%20and%20Active%20Inference%3A%20The%20Formal%20Boundary%20of%20History-Conditioned%20Anticipatory%20Systems.%0A%0AHistory-dependent%20reconfiguration%2C%20structured%20memory%2C%20and%20predictive%20preconfiguration%20in%20prestressed%20complex%20systems +OpenAlex discovery-only search (not a verified identity or literature match): https://openalex.org/works?search=ROIF%20and%20Active%20Inference%3A%20The%20Formal%20Boundary%20of%20History-Conditioned%20Anticipatory%20Systems.%20%E2%80%8B%20History-dependent%20reconfiguration%2C%20structured%20memory%2C%20and%20predictive%20preconfiguration%20in%20prestressed%20complex%20systems diff --git a/data/public/2026/dossiers/talk-2f88a4d50509b911.md b/data/public/2026/dossiers/talk-2cb75f3d60e44286.md similarity index 65% rename from data/public/2026/dossiers/talk-2f88a4d50509b911.md rename to data/public/2026/dossiers/talk-2cb75f3d60e44286.md index 9e2db0c..751dd86 100644 --- a/data/public/2026/dossiers/talk-2f88a4d50509b911.md +++ b/data/public/2026/dossiers/talk-2cb75f3d60e44286.md @@ -1,19 +1,18 @@ -# Actve Inference, Bayesian Causal Learning and Empowerment in RL. +# Active Inference, Bayesian Causal Learning and Empowerment in RL. Presenter: Alison Gopnik Contribution: Livestreamed panel or group session -Public ID: talk-2f88a4d50509b911 +Public ID: talk-2cb75f3d60e44286 ## Submitted abstract - -Cognitive scientists have applied advances in our formal understanding of causation in philosophy and computer science, particularly within the Causal Bayes Net formalism, to understand human causal learning. This formalism is also closely related to ideas in active inference. In parallel, reinforcement learning researchers have developed the idea of an intrinsic reward signal called “empowerment”. An agent is rewarded for maximizing the mutual information between its actions and their outcomes, regardless of the external reward value of those outcomes. “Empowerment” may be an important bridge between classical Bayesian causal learning and reinforcement learning and may help to characterize causal learning in humans and enable it in machines +Cognitive scientists have applied advances in our formal understanding of causation in philosophy and computer science, particularly within the Causal Bayes Net formalism, to understand human causal learning. This formalism is also closely related to ideas in active inference. In parallel, reinforcement learning researchers have developed the idea of an intrinsic reward signal called “empowerment”. An agent is rewarded for maximizing the mutual information between its actions and their outcomes, regardless of the external reward value of those outcomes. “Empowerment” may be an important bridge between classical Bayesian causal learning and reinforcement learning and may help to characterize causal learning in humans and enable it in machines Source: [submitted source]() -Abstract SHA256: 72888682b1f2ff64f4e83e33530897248d8aedd1c4d529bcdf99d3009e1065a1 +Abstract SHA256: b15da3a9e306bf10b00e79eb1908884fcfcf0d93f097cf4ea2bef516e9a18271 ## Organizer review checklist @@ -33,4 +32,4 @@ These approved event resources are starting points; topic relevance and author i - pymdp: https://github.com/infer-actively/pymdp - Reproducible publication template: https://github.com/docxology/template -OpenAlex discovery-only search (not a verified identity or literature match): https://openalex.org/works?search=Actve%20Inference%2C%20Bayesian%20Causal%20Learning%20and%20Empowerment%20in%20RL. +OpenAlex discovery-only search (not a verified identity or literature match): https://openalex.org/works?search=Active%20Inference%2C%20Bayesian%20Causal%20Learning%20and%20Empowerment%20in%20RL. diff --git a/data/public/2026/dossiers/talk-2da4ceb8dad3a468.md b/data/public/2026/dossiers/talk-2da4ceb8dad3a468.md new file mode 100644 index 0000000..9884396 --- /dev/null +++ b/data/public/2026/dossiers/talk-2da4ceb8dad3a468.md @@ -0,0 +1,38 @@ +# Explicating and Comparing Prediction Architectures of Theories of Consciousness and its Application to INTREPID + +Presenter: Montijn van Loo + +Contribution: Livestreamed panel or group session + +Public ID: talk-2da4ceb8dad3a468 + +## Submitted abstract + +Consciousness: The Channel Framework and its Application to INTREPID +The Channel Framework treats theories of consciousness as relating physical and experiential events, with the minimal relation being predictive. The Channel Framework embeds this psychophysical relation within the full experimental prediction architecture, connecting physical processes, conscious content, introspective access, and observable outcomes. Expressed through probabilistic channels, these architectures can be interpreted as generative models of experimental predictions, providing a common language in which different theories can be explicated and compared. +I apply the framework to INTREPID’s adversarial comparison of Integrated Information Theory and predictive-processing theories of consciousness, including Neurorepresentationalism and the Active Inference theory of consciousness (AI-C). The framework asks whether their prediction architectures can be faithfully aligned, which theoretical differences actually generate their contrasting experimental predictions, and whether these differences carry the particular claims the experiments aim to test. +Building on this analysis, the Channel Standard develops criteria for attributing evidence from competing theory predictions to specific theoretical claims. The broader aim is to move beyond theory-level comparison toward methodological concensus. + +Source: [submitted source]() + +Abstract SHA256: 74e8299085acd15f5c631e629533eb94c3c284d07b3c37cfaa7aa283bde832db + +## Organizer review checklist + +- [ ] Confirm the submitted title, presenter, and abstract with the presenter. +- [ ] Confirm contribution format and scheduling needs. +- [ ] Confirm public permissions for any additional material before publication. +- [ ] Prepare discussion questions grounded in the submitted abstract. + +## Public learning resources + +These approved event resources are starting points; topic relevance and author identity have not been inferred. + +- Active Inference Ontology: https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology +- Cognitive Agent Modeling: https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations +- GEO-INFER: https://github.com/ActiveInferenceInstitute/GEO_INFER +- Generalized Notation Notation: https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation +- pymdp: https://github.com/infer-actively/pymdp +- Reproducible publication template: https://github.com/docxology/template + +OpenAlex discovery-only search (not a verified identity or literature match): https://openalex.org/works?search=Explicating%20and%20Comparing%20Prediction%20Architectures%20of%20Theories%20of%20Consciousness%20and%20its%20Application%20to%20INTREPID diff --git a/data/public/2026/dossiers/talk-34c1d546c1122ef8.md b/data/public/2026/dossiers/talk-34c1d546c1122ef8.md new file mode 100644 index 0000000..a7ffbd8 --- /dev/null +++ b/data/public/2026/dossiers/talk-34c1d546c1122ef8.md @@ -0,0 +1,35 @@ +# FEP and Active Inference through the lens of Winnicott’s Potential Space + +Presenter: Alexander Sabine + +Contribution: Pre-recorded Video presentation + +Public ID: talk-34c1d546c1122ef8 + +## Submitted abstract + +Winnicott held that the infant’s transitional object must never be questioned - did you create it or find it? The paradox is to be held, not resolved, because play, creativity and culture live in the space it opens. This talk argues that active inference carries the same paradox. Perception is jointly authored by the generative model and the evidence, and action shapes the world the agent then finds. I draw thinking from the pioneers of early childhood education, philosophy of time and contemporary discussions. I explore some possible consequences, treating the unresolved paradox as a design principle. + +Source: [submitted source]() + +Abstract SHA256: 0171e69c3ca2e83399ea58689ef86fad953c5821cdc1cd265d5248506e2f6bc3 + +## Organizer review checklist + +- [ ] Confirm the submitted title, presenter, and abstract with the presenter. +- [ ] Confirm contribution format and scheduling needs. +- [ ] Confirm public permissions for any additional material before publication. +- [ ] Prepare discussion questions grounded in the submitted abstract. + +## Public learning resources + +These approved event resources are starting points; topic relevance and author identity have not been inferred. + +- Active Inference Ontology: https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology +- Cognitive Agent Modeling: https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations +- GEO-INFER: https://github.com/ActiveInferenceInstitute/GEO_INFER +- Generalized Notation Notation: https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation +- pymdp: https://github.com/infer-actively/pymdp +- Reproducible publication template: https://github.com/docxology/template + +OpenAlex discovery-only search (not a verified identity or literature match): https://openalex.org/works?search=FEP%20and%20Active%20Inference%20through%20the%20lens%20of%20Winnicott%E2%80%99s%20Potential%20Space diff --git a/data/public/2026/dossiers/talk-36b60916ca76bc85.md b/data/public/2026/dossiers/talk-36b60916ca76bc85.md new file mode 100644 index 0000000..4b6afaf --- /dev/null +++ b/data/public/2026/dossiers/talk-36b60916ca76bc85.md @@ -0,0 +1,38 @@ +# Unlocking Robotic Dexterity + +Presenter: Matthew Brown + +Contribution: Pre-recorded Video presentation + +Public ID: talk-36b60916ca76bc85 + +## Submitted abstract + +Most real-world work is messy, variable, and full of contact: cables that flex, parts that don’t quite fit, valves that stick. Traditional robots excel only where conditions are controlled and repeatable, and today’s leading AI approaches (vision-language-action models, diffusion policies, and embodied foundation models) struggle to close the gap. They depend on massive datasets that don’t exist for physical interaction, require heavy compute at inference time, and break down when reality drifts from their training distribution. +This talk argues that dexterity is not primarily a data problem but an adaptation problem. Nature doesn’t solve manipulation by training once offline and hoping it generalizes. It learns continuously at the point of contact, with perception, action, and learning operating as a single loop. We contrast the dominant paradigm of universal function approximation, which is retrospective, data-dependent, and time-agnostic, with enactive synchronization: a prospective, time-aware process in which an agent continuously adjusts to maintain predictive coherence with its environment. +Tracing a line from W. Ross Ashby’s cybernetics and his Homeostat through the Free Energy Principle and Active Inference, we show how ultrastable networks can form a minimum-prediction-error synchronization manifold between a robot and the world it touches. Sensors and motors become the interface through which goals are expressed as preferred states, and behavior emerges from the network’s drive to stay in equilibrium. +We then show what this looks like in practice: compact models that run on a single CPU at the edge, update in under a millisecond, and learn tasks in minutes rather than weeks, illustrated through new demonstrations of adaptive dexterous manipulation on real robotic hardware. + +Source: [submitted source]() + +Abstract SHA256: 53deffec496e2322285e7d15c559b85937810dac1b14b43291e5fdc4f6a6dc63 + +## Organizer review checklist + +- [ ] Confirm the submitted title, presenter, and abstract with the presenter. +- [ ] Confirm contribution format and scheduling needs. +- [ ] Confirm public permissions for any additional material before publication. +- [ ] Prepare discussion questions grounded in the submitted abstract. + +## Public learning resources + +These approved event resources are starting points; topic relevance and author identity have not been inferred. + +- Active Inference Ontology: https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology +- Cognitive Agent Modeling: https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations +- GEO-INFER: https://github.com/ActiveInferenceInstitute/GEO_INFER +- Generalized Notation Notation: https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation +- pymdp: https://github.com/infer-actively/pymdp +- Reproducible publication template: https://github.com/docxology/template + +OpenAlex discovery-only search (not a verified identity or literature match): https://openalex.org/works?search=Unlocking%20Robotic%20Dexterity diff --git a/data/public/2026/dossiers/talk-411510fca178636f.md b/data/public/2026/dossiers/talk-411510fca178636f.md new file mode 100644 index 0000000..5750b19 --- /dev/null +++ b/data/public/2026/dossiers/talk-411510fca178636f.md @@ -0,0 +1,40 @@ +# Today’s Dialogue, Tomorrow’s Inheritance: Active Inference for Networks That Learn Together. + +Presenter: Michael Lennon + +Contribution: Pre-recorded Video presentation + +Public ID: talk-411510fca178636f + +## Submitted abstract + +Imagine the Economics Nobel in 2036 is awarded for the 1st time to a community: the Active Inference Community. Scientists, communities, and institutions now tackle tangled social and ecological challenges more reliably, because they learn together within, between, and beyond their disciplines. What did we build to get there? +This talk works backwards from that future, building on last year’s invitation to grow the learning capacity of our networks. It proposes machine-assisted collective learning infrastructure enabled by active inference: an organ through which diverse networks sense, make sense, infer, compass, and coordinate adaptive decisions across ways of knowing. +Its architecture is a loop of Evidence, Dialogue, and Adapt, circling a field of memory three layers deep at every scale. The map holds what is measured and recorded. The compass holds interior perceptions and shared hopes that orient action, even in the dark. The ground holds what we inherit, biologically and culturally, from immune memory to language and norms. Each layer is evidence of a different kind, and none certifies another. Friedman’s meta-pragmatic mechanisms, defined for a single agent and extended here to groups, arbitrate how far to trust each layer and when to revise it. +Stories from development practice, ecology, and network science serve as modelling targets: project dialects that bind teams and wall them off, lessons trapped at the wrong scale, forests managed for what could be measured. Today’s dialogue, it turns out, is tomorrow’s inheritance. +The talk is frank about AI’s double edge: pre-trained models are canned, but the medium around them can learn, and the same tools can homogenise dialogue, breed confident wrongness, or let agents narrate their own history. +Listeners will leave with a flipped version of an old parable, fresh metaphors for explaining active inference, and an invitation to help build the organ. + +Source: [submitted source]() + +Abstract SHA256: a697794f407c85b1c79b57371e9acc883d99e36b0ff0fddb4241da2085a33b64 + +## Organizer review checklist + +- [ ] Confirm the submitted title, presenter, and abstract with the presenter. +- [ ] Confirm contribution format and scheduling needs. +- [ ] Confirm public permissions for any additional material before publication. +- [ ] Prepare discussion questions grounded in the submitted abstract. + +## Public learning resources + +These approved event resources are starting points; topic relevance and author identity have not been inferred. + +- Active Inference Ontology: https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology +- Cognitive Agent Modeling: https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations +- GEO-INFER: https://github.com/ActiveInferenceInstitute/GEO_INFER +- Generalized Notation Notation: https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation +- pymdp: https://github.com/infer-actively/pymdp +- Reproducible publication template: https://github.com/docxology/template + +OpenAlex discovery-only search (not a verified identity or literature match): https://openalex.org/works?search=Today%E2%80%99s%20Dialogue%2C%20Tomorrow%E2%80%99s%20Inheritance%3A%20Active%20Inference%20for%20Networks%20That%20Learn%20Together. diff --git a/data/public/2026/dossiers/talk-414af3e53e96ece8.md b/data/public/2026/dossiers/talk-414af3e53e96ece8.md new file mode 100644 index 0000000..405aa2e --- /dev/null +++ b/data/public/2026/dossiers/talk-414af3e53e96ece8.md @@ -0,0 +1,35 @@ +# CogNarr Story Graph: A Formal Semantics for Narrative as a Belief Model + +Presenter: John Boik, Glen EP Ropella + +Contribution: Livestreamed panel or group session + +Public ID: talk-414af3e53e96ece8 + +## Submitted abstract + +People naturally express their beliefs about a topic, situation, or problem in the form of narrative. CogNarr is a soon-to-be-released, open-source, interactive tool that lets a writer transform narrative into an explicit, formal semantic graph, which they can inspect and correct until it faithfully reflects their belief model. A CogNarr story graph captures a narrative's argument structure, its timing, and the modality of its claims, including predictions and conditionals, together with the uncertainty attached to each. In general it expresses the verifiable meaning of what a person wishes to say, while reducing the ambiguity of natural language. As such, it could be of use in modeling the beliefs that active-inference processes operate over, and as a framework within which active-inference methods could be employed to infer semantic meaning from text. Because it makes a person's beliefs explicit, transparent, and shareable, CogNarr has potential wherever consequential beliefs are expressed as narrative, from medicine and governance to public polling and accident reporting. As well, CogNarr story graphs support accurate agent-agent communication. The collective-cognition aim behind the project is alignment of shared belief models during decision-making processes in the large-group setting. The talk will be a preview of features and capabilities, with some discussion of potential applications. + +Source: [submitted source]() + +Abstract SHA256: bd1c5a829a23b0d6596a0afaec580fc1b0cccccbc5d90c313603267ed871417f + +## Organizer review checklist + +- [ ] Confirm the submitted title, presenter, and abstract with the presenter. +- [ ] Confirm contribution format and scheduling needs. +- [ ] Confirm public permissions for any additional material before publication. +- [ ] Prepare discussion questions grounded in the submitted abstract. + +## Public learning resources + +These approved event resources are starting points; topic relevance and author identity have not been inferred. + +- Active Inference Ontology: https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology +- Cognitive Agent Modeling: https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations +- GEO-INFER: https://github.com/ActiveInferenceInstitute/GEO_INFER +- Generalized Notation Notation: https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation +- pymdp: https://github.com/infer-actively/pymdp +- Reproducible publication template: https://github.com/docxology/template + +OpenAlex discovery-only search (not a verified identity or literature match): https://openalex.org/works?search=CogNarr%20Story%20Graph%3A%20A%20Formal%20Semantics%20for%20Narrative%20as%20a%20Belief%20Model diff --git a/data/public/2026/dossiers/talk-42235dc73596d92f.md b/data/public/2026/dossiers/talk-42235dc73596d92f.md new file mode 100644 index 0000000..97f2d84 --- /dev/null +++ b/data/public/2026/dossiers/talk-42235dc73596d92f.md @@ -0,0 +1,35 @@ +# Solving the Relevance Problem: A General Relevance Criterion in Active Inference + +Presenter: Montijn van Loo + +Contribution: Pre-recorded Video presentation + +Public ID: talk-42235dc73596d92f + +## Submitted abstract + +This paper proposes a simpler account of generalized relevance realization within active inference, responding to challenges to existing approaches by specifying an explicit relevance-selection criterion. The generalized relevance problem concerns how agents identify information and action consequences worth considering without exhaustive examination. The proposed criterion is that a recognized state becomes relevant when it activates a learned prediction of an outcome that would satisfy or frustrate an existing preference. Because such predictions may concern actions other than those currently pursued, relevance selection can precede current-policy evaluation. Recruited state information subsequently enters ordinary active-inference policy evaluation, while continuous learning establishes new predictors and predictive relationships. This account explains how learned predictions can recruit relevant information without requiring higher-order preference-precision regulation, while allowing flexible behavior under relatively stable preferences. + +Source: [submitted source]() + +Abstract SHA256: 80b19871d6bb2c5da0042d7c2badc813aff363c3ffa3aaf8c7a35581595198f7 + +## Organizer review checklist + +- [ ] Confirm the submitted title, presenter, and abstract with the presenter. +- [ ] Confirm contribution format and scheduling needs. +- [ ] Confirm public permissions for any additional material before publication. +- [ ] Prepare discussion questions grounded in the submitted abstract. + +## Public learning resources + +These approved event resources are starting points; topic relevance and author identity have not been inferred. + +- Active Inference Ontology: https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology +- Cognitive Agent Modeling: https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations +- GEO-INFER: https://github.com/ActiveInferenceInstitute/GEO_INFER +- Generalized Notation Notation: https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation +- pymdp: https://github.com/infer-actively/pymdp +- Reproducible publication template: https://github.com/docxology/template + +OpenAlex discovery-only search (not a verified identity or literature match): https://openalex.org/works?search=Solving%20the%20Relevance%20Problem%3A%20A%20General%20Relevance%20Criterion%20in%20Active%20Inference diff --git a/data/public/2026/dossiers/talk-42ee83a32b9468cb.md b/data/public/2026/dossiers/talk-42ee83a32b9468cb.md index 457a116..2fc60f8 100644 --- a/data/public/2026/dossiers/talk-42ee83a32b9468cb.md +++ b/data/public/2026/dossiers/talk-42ee83a32b9468cb.md @@ -8,11 +8,11 @@ Public ID: talk-42ee83a32b9468cb ## Submitted abstract -Come see the beauty of mathematics. Its just not some fancy add on to life. It is life. +Come see the beauty of mathematics. Its just not some fancy add on to life. It is life. Source: [submitted source]() -Abstract SHA256: 310d7b89e4a65aaeaae2c5c8bf6801a857c2fb36a7b7cf5291202c2e61c49a45 +Abstract SHA256: d899063be7a8506cd83b94c4296f5fb29ccd76343d750ba966be9c32ee76dd20 ## Organizer review checklist diff --git a/data/public/2026/dossiers/talk-49c86444406fb4d6.md b/data/public/2026/dossiers/talk-49c86444406fb4d6.md new file mode 100644 index 0000000..b655aec --- /dev/null +++ b/data/public/2026/dossiers/talk-49c86444406fb4d6.md @@ -0,0 +1,37 @@ +# Adaptive Prosociality Through Allostatic Regulation in Social Dynamics: A Simulation Study + +Presenter: Hyojun Choi + +Contribution: Pre-recorded Video presentation + +Public ID: talk-49c86444406fb4d6 + +## Submitted abstract + +How can a prosocial agent reconcile self-protection with convergence toward mutual cooperation? Classic reactive strategies, such as Tit-for-Tat (TFT) and Win-Stay Lose-Shift (WSLS), perform well only under restrictive conditions. Recent neurocomputational studies show that the brain dynamically updates beliefs over hidden intentions for adaptive social inference. Meanwhile, emerging accounts of social neuroscience suggest that social valuation may be regulated with reference to an internal setpoint, as an agent balances internalized prosocial motives against its current resource state. +Building on both, we propose an active inference agent for the iterated Prisoner’s Dilemma where prosociality is endogenously regulated. This includes (i) perspective-taking, approximated via particle filter Bayesian inference over the opponent’s model parameters; (ii) distributional reinforcement learning, implemented as quantile regression temporal difference learning and Dyna planning with generative replay; (iii) prosociality regulator, which dynamically controls the weight placed on the other’s expected utility relative to a setpoint; and (iv) a self-model, which memorizes partner-specific social expectations and learned reward distributions across interactions. +In preliminary simulations, our agent robustly tracks fluctuating opponent intentions and differentiates reciprocating from exploitative partners. Under non-stationary payoffs in mixed memory-one populations, it achieves higher long-run average payoffs than any other tested strategy after a learning phase, while maintaining mutual cooperation at a level above TFT and WSLS yet below ALLC and GTFT. These findings highlight setpoint-referenced regulation as a candidate computational foundation for adaptive prosociality, offering mechanistic insights into how self-protection and mutual cooperation can be jointly realized and an extensible blueprint for socially aligned embodied agents. + +Source: [submitted source]() + +Abstract SHA256: 161af41147ed6bde7d7618a3ee16d0125b7348cfd3484cf188eded4c3ce4f7e4 + +## Organizer review checklist + +- [ ] Confirm the submitted title, presenter, and abstract with the presenter. +- [ ] Confirm contribution format and scheduling needs. +- [ ] Confirm public permissions for any additional material before publication. +- [ ] Prepare discussion questions grounded in the submitted abstract. + +## Public learning resources + +These approved event resources are starting points; topic relevance and author identity have not been inferred. + +- Active Inference Ontology: https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology +- Cognitive Agent Modeling: https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations +- GEO-INFER: https://github.com/ActiveInferenceInstitute/GEO_INFER +- Generalized Notation Notation: https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation +- pymdp: https://github.com/infer-actively/pymdp +- Reproducible publication template: https://github.com/docxology/template + +OpenAlex discovery-only search (not a verified identity or literature match): https://openalex.org/works?search=Adaptive%20Prosociality%20Through%20Allostatic%20Regulation%20in%20Social%20Dynamics%3A%20A%20Simulation%20Study diff --git a/data/public/2026/dossiers/talk-528af36ab3c9a9d4.md b/data/public/2026/dossiers/talk-528af36ab3c9a9d4.md new file mode 100644 index 0000000..37810a5 --- /dev/null +++ b/data/public/2026/dossiers/talk-528af36ab3c9a9d4.md @@ -0,0 +1,38 @@ +# The Mark of the Moral and the Shared Author Hypothesis: An Active Inference Approach to Moral Objectivity + +Presenter: Montijn van Loo + +Contribution: Pre-recorded Video presentation + +Public ID: talk-528af36ab3c9a9d4 + +## Submitted abstract + +Philosophy is characterized by having something to talk about, yet no consensus on how to talk about it. Mathematics helped physics emerge from philosophy into a cumulative science. Active Inference, potentially a unifying theory of psychology, may mark a comparable philosophical turning point. Psychology and sociology are already flourishing in this mathematical direction, but its consequences may reach further, into philosophical problems that have resisted resolution for centuries. This paper applies that prospect to the mark of the moral and the longstanding tension between moral subjectivity and objectivity. +The proposal is that the mark of the moral is the best predicted global outcome under a role-invariant desire. Active Inference represents choice as the evaluation of predicted outcomes through beliefs and desires. Outcomes are relationally represented according to one’s role within them. An authorial desire is invariant under such role changes: it values the same global outcome equally regardless of which participant’s perspective is taken. Moral action consequently maximizes expected authorial value. +This distinction clarifies moral disagreement. Individuals may share an authorial desire yet choose differently because their worldviews generate different predictions. Even agreement about immediate consequences need not establish agreement about their wider significance. The Shared Author Hypothesis goes further, proposing that all role-invariant desires are one and the same. Under this hypothesis, moral disagreement ultimately concerns differences in worldview rather than competing moral standards. +The framework thus offers a conditional resolution of the subjectivity–objectivity problem: moral choices depend on individual beliefs, while their underlying standard of evaluation remains shared. It also redirects moral and political disagreement toward determining which worldview best fits the available evidence. + +Source: [submitted source]() + +Abstract SHA256: 46149f1f3f5dcd4be689ab05db73e90bec29e879e207e53f6595039738e3a2dd + +## Organizer review checklist + +- [ ] Confirm the submitted title, presenter, and abstract with the presenter. +- [ ] Confirm contribution format and scheduling needs. +- [ ] Confirm public permissions for any additional material before publication. +- [ ] Prepare discussion questions grounded in the submitted abstract. + +## Public learning resources + +These approved event resources are starting points; topic relevance and author identity have not been inferred. + +- Active Inference Ontology: https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology +- Cognitive Agent Modeling: https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations +- GEO-INFER: https://github.com/ActiveInferenceInstitute/GEO_INFER +- Generalized Notation Notation: https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation +- pymdp: https://github.com/infer-actively/pymdp +- Reproducible publication template: https://github.com/docxology/template + +OpenAlex discovery-only search (not a verified identity or literature match): https://openalex.org/works?search=The%20Mark%20of%20the%20Moral%20and%20the%20Shared%20Author%20Hypothesis%3A%20An%20Active%20Inference%20Approach%20to%20Moral%20Objectivity diff --git a/data/public/2026/dossiers/talk-59d5a0672890ee22.md b/data/public/2026/dossiers/talk-59d5a0672890ee22.md index 178e28b..880b044 100644 --- a/data/public/2026/dossiers/talk-59d5a0672890ee22.md +++ b/data/public/2026/dossiers/talk-59d5a0672890ee22.md @@ -8,13 +8,13 @@ Public ID: talk-59d5a0672890ee22 ## Submitted abstract -The highly developed human sense of self is one of our most distinctive traits. We each acquire our sense of self in conversations, as we learn how conversational partners regard us. Our primary sense of self is not ‘myself as I experience myself’, but ‘myself as I think other people see me’ – a second-hand view of myself. In private verbal thought, with nobody else physically present, other people are represented as a shadow audience in our minds – an audience which sees what we do or say, and judges us for it. The shadow audience is the source of human emotion and motivation. - +The highly developed human sense of self is one of our most distinctive traits. We each acquire our sense of self in conversations, as we learn how conversational partners regard us. Our primary sense of self is not ‘myself as I experience myself’, but ‘myself as I think other people see me’ – a second-hand view of myself. In private verbal thought, with nobody else physically present, other people are represented as a shadow audience in our minds – an audience which sees what we do or say, and judges us for it. The shadow audience is the source of human emotion and motivation. + Language started in conversational pragmatics, before there was complex syntax or semantics. Pragmatics requires us to mind-read, to infer what a conversational partner thinks, wants or needs. In this way we infer what or conversational partners think about us – how they assess our social status. In any conversation, we are concerned about our own social status in the eyes of a conversational partner, or of a shadow audience. This is illustrated by the pragmatic phenomenon of conversational irony. Source: [submitted source]() -Abstract SHA256: c3e821957c0b490a549dc3e4f90a62c49c42cad0fdcd90c676dbdab041549add +Abstract SHA256: b2facde42a54466980e529920d2c7648c21c37fe1a1c4d321a1dac8cd4ded7d9 ## Organizer review checklist diff --git a/data/public/2026/dossiers/talk-5f1d4f1776db95c2.md b/data/public/2026/dossiers/talk-5f1d4f1776db95c2.md index 11d0a9a..a70c7d1 100644 --- a/data/public/2026/dossiers/talk-5f1d4f1776db95c2.md +++ b/data/public/2026/dossiers/talk-5f1d4f1776db95c2.md @@ -11,11 +11,14 @@ Public ID: talk-5f1d4f1776db95c2 The Free Energy Principle offers a physically grounded account of intentional properties in mind and life, framed within Dynamical Systems Theory. Because of this generality, it was proposed as a formal basis to study biological autonomy, autopoiesis, and enactive cognition. Yet, those approaches are defined by a relational understanding of mind and life, centred on the description of engagement with the world as a constructive process where agents are “laying down a path in walking”. Building from Rosen’s relational biology, we argue that such processes are by construction outside a “Newtonian” description in terms of well-defined observables and their dynamics. Rather, we draw from Fields’ demonstration of the emergence of contextuality – the participatory production of observables against which the agent regulates their activity – in classical, thermodynamically bounded processes of observation. We suggest a complementarity between Friston’s classical FEP and Fields et al.’s “quantum” (or, in our account, relational) FEP, describing respectively the dynamics within a biological / cognitive space of relevant observables and the physical processes underlying the construction of that space. Finally, we propose a structural realist interpretation of this complementarity within the “closure of constraints” account of biological autonomy, where both principles are understood to act on a physically embedded system of biological constraints rather than an abstract mathematical space assumed to fully represent biological / cognitive organization. This approach foregrounds the synthesis between mathematical and theoretical approaches to biological autonomy, thus supporting a principled approach to both analyzing the organization of natural systems and attempting the implementation of artificial autonomy. -This presentation is adapted from Guénin--Carlut, Avel. 2026. “A Tale of Two Principles - Towards a Relational Perspective on the FEP.” https://hal.science/hal-05697510., currently under review at the Entropy Special Issue “Active Inference in Cognitive Neuroscience”. + +This presentation is adapted from Guénin--Carlut, Avel. 2026. “A Tale of Two Principles - Towards a Relational Perspective on the FEP.” https://hal.science/hal-05697510 +? +., currently under review at the Entropy Special Issue “Active Inference in Cognitive Neuroscience”. Source: [submitted source]() -Abstract SHA256: 059387f4d15fe2e1057fbd24d182cb5053d346ced3cd03029ac6e472ba395df8 +Abstract SHA256: c170e3299f8292d8fefbd89206cd6527953048a9b86da362d1115f3cebba9269 ## Organizer review checklist diff --git a/data/public/2026/dossiers/talk-6515495f47af63f0.md b/data/public/2026/dossiers/talk-64d975092642fb38.md similarity index 96% rename from data/public/2026/dossiers/talk-6515495f47af63f0.md rename to data/public/2026/dossiers/talk-64d975092642fb38.md index 3a135a2..93e66c1 100644 --- a/data/public/2026/dossiers/talk-6515495f47af63f0.md +++ b/data/public/2026/dossiers/talk-64d975092642fb38.md @@ -1,10 +1,10 @@ -# Active inference and design +# Active inference and design Presenter: Luca Possati Contribution: Pre-recorded Video presentation -Public ID: talk-6515495f47af63f0 +Public ID: talk-64d975092642fb38 ## Submitted abstract @@ -34,4 +34,4 @@ These approved event resources are starting points; topic relevance and author i - pymdp: https://github.com/infer-actively/pymdp - Reproducible publication template: https://github.com/docxology/template -OpenAlex discovery-only search (not a verified identity or literature match): https://openalex.org/works?search=Active%20inference%20and%20design%20 +OpenAlex discovery-only search (not a verified identity or literature match): https://openalex.org/works?search=Active%20inference%20and%20design diff --git a/data/public/2026/dossiers/talk-662b4152a55d7304.md b/data/public/2026/dossiers/talk-662b4152a55d7304.md new file mode 100644 index 0000000..4db4f36 --- /dev/null +++ b/data/public/2026/dossiers/talk-662b4152a55d7304.md @@ -0,0 +1,38 @@ +# Percepia: Perceptual Phenotyping from Conversation + +Presenter: Patricia Craja + +Contribution: Pre-recorded Video presentation + +Public ID: talk-662b4152a55d7304 + +## Submitted abstract + +Psychological taxonomies, from the Big Five to the DSM, are descriptive, not mechanistic: they classify how people differ without modelling why. Active inference offers a mechanistic account: it formalises a small set of generative parameters that govern how a mind perceives, learns and acts under uncertainty. Chief among them is precision: the confidence a mind places in different sources of information. Sensory and prior precision govern whether new evidence or existing expectations dominate belief updating; policy precision, how confidently a mind selects goal-directed action over habit; preference precision, how it balances reward-seeking against information-seeking. Their configuration constitutes what we call a mind’s perceptual phenotype. Meta-precision, a mind’s capacity to monitor and revise its own precision assignments, has been proposed as the mechanism underlying epistemic depth — one of three conditions the theory identifies for consciousness (Laukkonen et al. 2025). Today first-order precision parameters are estimated from laboratory tasks; meta-precision has no established paradigm. +This talk introduces Percepia, a perceptual phenotyping layer between a user and a large language model that tracks probability distributions over personality and beliefs, and aims to reverse-infer the precision parameters from them. +I present two experiments with synthetic LLM personas, where parameters are known by construction. In the first, 625 personas were prompted to vary sensory, prior and policy precision in conversation; Percepia inferred their Big Five from the dialogue, testing whether precision leaves a recoverable signature in personality, as Safron and DeYoung (2021) propose. In the second, personas varied in meta-precision, and Percepia tracked their stage of adult development (Kegan 1994). +As the personas were primed with the theory, these experiments test whether a reverse-inference channel from language to precision parameters is viable, not whether the mapping holds in humans: a first step toward measuring perceptual phenotypes at scale. + +Source: [submitted source]() + +Abstract SHA256: 3b0e104b27b606531d74a8cab2f3c0fc5a1a9d19ad7346550437ff4bf1436d65 + +## Organizer review checklist + +- [ ] Confirm the submitted title, presenter, and abstract with the presenter. +- [ ] Confirm contribution format and scheduling needs. +- [ ] Confirm public permissions for any additional material before publication. +- [ ] Prepare discussion questions grounded in the submitted abstract. + +## Public learning resources + +These approved event resources are starting points; topic relevance and author identity have not been inferred. + +- Active Inference Ontology: https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology +- Cognitive Agent Modeling: https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations +- GEO-INFER: https://github.com/ActiveInferenceInstitute/GEO_INFER +- Generalized Notation Notation: https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation +- pymdp: https://github.com/infer-actively/pymdp +- Reproducible publication template: https://github.com/docxology/template + +OpenAlex discovery-only search (not a verified identity or literature match): https://openalex.org/works?search=Percepia%3A%20Perceptual%20Phenotyping%20from%20Conversation diff --git a/data/public/2026/dossiers/talk-6cf1d4c15e70a930.md b/data/public/2026/dossiers/talk-6cf1d4c15e70a930.md new file mode 100644 index 0000000..73ae60a --- /dev/null +++ b/data/public/2026/dossiers/talk-6cf1d4c15e70a930.md @@ -0,0 +1,41 @@ +# Artificial Sentience Project + +Presenter: Cleber Gomes + +Contribution: Pre-recorded Video presentation + +Public ID: talk-6cf1d4c15e70a930 + +## Submitted abstract + +This project introduces an innovative computational architecture that bridges cognitive neuroscience and interactive simulation by integrating na Active Inference cognitive core directly into UI engines. Grounded in the Free Energy Principle, the system continuously minimizes Variational Free Energy (VFE) to drive autonomous agent behavior, dynamic navigation, and adaptive decision-making within complex, changing environments. +Unlike traditional rule-based non-player characters (NPCs) or standard robotic controllers, the agent maintains an internal affective engine that maps multi-dimensional emotional telemetry—such as curiosity, motivation, stress, and fear—directly to behavioral drives, energy management, and rich audio-visual expressions. These affective states are rendered in real time through belief-versus-reality spatial overlays, floating emotional telemetry indicators, and adaptive state vocalizations that mirrors the agent's psychological trajectory. +By unifying autonomous perception-action loops with transparent emotional expression, this framework offers powerful capabilities for powering both advanced games and autonomous robots. Its potential applications span multiple domains: +Entertainment: Powering next-generation autonomous game characters and dynamic simulations with emergent, lifelike behaviors. +Education: Serving as an interactive pedagogical platform for exploring cognitive architectures, cybernetics, and affective computing. +Companionship & Emotional Support: Creating empathetic digital companions and social robots capable of contextual awareness and authentic emotional resonance to foster meaningful human-agent interaction and support human well-being. +Ultimately, this project demonstrates that Active Inference provides a robust, biologically inspired foundation for artificial systems that not only navigate physical spaces intelligently but also communicate and connect through shared affective experiences. + +Source: [submitted source]() + +Abstract SHA256: c89f12fbbcf76da2412aaca5e51050015750446fe15a755d4c3d4ec620119dbf + +## Organizer review checklist + +- [ ] Confirm the submitted title, presenter, and abstract with the presenter. +- [ ] Confirm contribution format and scheduling needs. +- [ ] Confirm public permissions for any additional material before publication. +- [ ] Prepare discussion questions grounded in the submitted abstract. + +## Public learning resources + +These approved event resources are starting points; topic relevance and author identity have not been inferred. + +- Active Inference Ontology: https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology +- Cognitive Agent Modeling: https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations +- GEO-INFER: https://github.com/ActiveInferenceInstitute/GEO_INFER +- Generalized Notation Notation: https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation +- pymdp: https://github.com/infer-actively/pymdp +- Reproducible publication template: https://github.com/docxology/template + +OpenAlex discovery-only search (not a verified identity or literature match): https://openalex.org/works?search=Artificial%20Sentience%20Project diff --git a/data/public/2026/dossiers/talk-7013ce9d23d8fd4d.md b/data/public/2026/dossiers/talk-7013ce9d23d8fd4d.md new file mode 100644 index 0000000..23515e8 --- /dev/null +++ b/data/public/2026/dossiers/talk-7013ce9d23d8fd4d.md @@ -0,0 +1,38 @@ +# Inferring the Unseen + +Presenter: Anna Pereira + +Contribution: Pre-recorded Video presentation + +Public ID: talk-7013ce9d23d8fd4d + +## Submitted abstract + +As AI systems increasingly mediate access to scientific knowledge, how they communicate complex theories becomes consequential for public understanding. Active inference presents a distinctive challenge: its mathematical and computational foundations offer explanatory resources, yet accessible accounts must distinguish formal results, empirical evidence, and emerging interpretations. Simplification can support understanding, but can also obscure uncertainty or encourage misleading conclusions about personal experience. +Building on Pereira and Chamberlin’s manuscript, this foundational study will explore recommendations for large AI companies communicating active inference to general audiences. Its central question is how AI communication can preserve scientific integrity while helping people understand both the framework's core concepts and their potential relevance to everyday life. Particular attention will be given to the difference between being able to repeat an explanation and developing an understanding that supports reflection, judgment, and appropriate application. +The project will draw on literature spanning active inference, science communication, human–AI interaction, and learning. It will examine candidate communication approaches, including layered explanations, everyday examples, explicit statements of uncertainty, and prompts that connect abstract concepts to users' existing knowledge. A potential evaluation framework could consider conceptual accuracy, accessibility, perceived personal relevance, and the ability to distinguish supported implications from speculation. This framework could guide empirical evaluation of approaches. +The proposed contribution is an initial set of communication principles and research questions for AI developers, researchers, and educators. Rather than assuming that greater accessibility necessarily produces better understanding, the study will examine the tensions between clarity, scientific fidelity, personal relevance, and unwarranted certainty. The broader aim is to support communication that enables people to engage meaningfully with complex science without presenting a theoretical framework as a comprehensive explanation of their lives. This thirty minute presentation will outline the rationale, proposed framework, and recommendations emerging from the work, alongside priorities for further research and evaluation. + +Source: [submitted source]() + +Abstract SHA256: fcfc11fc0b871107bf6574eb14579fbabe42a31981b7fae1eebeb05e491c8c6e + +## Organizer review checklist + +- [ ] Confirm the submitted title, presenter, and abstract with the presenter. +- [ ] Confirm contribution format and scheduling needs. +- [ ] Confirm public permissions for any additional material before publication. +- [ ] Prepare discussion questions grounded in the submitted abstract. + +## Public learning resources + +These approved event resources are starting points; topic relevance and author identity have not been inferred. + +- Active Inference Ontology: https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology +- Cognitive Agent Modeling: https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations +- GEO-INFER: https://github.com/ActiveInferenceInstitute/GEO_INFER +- Generalized Notation Notation: https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation +- pymdp: https://github.com/infer-actively/pymdp +- Reproducible publication template: https://github.com/docxology/template + +OpenAlex discovery-only search (not a verified identity or literature match): https://openalex.org/works?search=Inferring%20the%20Unseen diff --git a/data/public/2026/dossiers/talk-80262f867d75698a.md b/data/public/2026/dossiers/talk-79ba0396518881ec.md similarity index 88% rename from data/public/2026/dossiers/talk-80262f867d75698a.md rename to data/public/2026/dossiers/talk-79ba0396518881ec.md index abf5075..934f4b8 100644 --- a/data/public/2026/dossiers/talk-80262f867d75698a.md +++ b/data/public/2026/dossiers/talk-79ba0396518881ec.md @@ -1,21 +1,21 @@ -# Build a curious rocket: the hands-on cpomdp tutorial for the Applied Active Inference Symposium +# Build a curious rocket: a hands-on continuous-state AIF session with cpomdp -Presenter: Dan Corva +Presenter: Dan Corva, Kevin Backhouse Contribution: Interactive Workshop -Public ID: talk-80262f867d75698a +Public ID: talk-79ba0396518881ec ## Submitted abstract -In the 1960s, Rudolf Kalman wrote down a filter that would go on to navigate Apollo to the moon. It has been the workhorse of continuous-state estimation ever since, and if you have ever built a continuous active inference agent in the linear-Gaussian regime, you have built one too, whether you intended to or not. Bolt a goal onto it and you get a very good controller that is not curious in the slightest. Koudahl, Kouw and de Vries proved why in 2021: under a fixed sensor the epistemic value is identical for every action, so expected free energy quietly reduces to Linear Quadratic Regulator (LQR). -In this workshop, in about ten lines of Python that will look familiar to anyone who has used pymdp, we are going to build a rocket! Then, we add the interesting part, curiosity, by making a surgical, biologically realistic change: adding sensor noise that varies based on the rocket's position. We will see how the flight path bends when we add artifacts like a navigation beacon or a blackout zone, and explore the science as to why. You will write every line yourself in a Colab notebook and do not worry there will be plenty of scaffolding from the cpomdp team. Code hints will drop in on a timer, and a reference cell for anyone who gets stuck or would rather just see the finished steps. +In the 1960s, Rudolf Kalman wrote down a filter that would go on to navigate Apollo to the moon. It has been the workhorse of continuous-state estimation ever since, and if you have ever built a continuous active inference agent in the linear-Gaussian regime, you have built one too, whether you intended to or not. Bolt a goal onto it and you get a very good controller that is not curious in the slightest. Koudahl, Kouw and de Vries proved why in 2021: under a fixed sensor the epistemic value is identical for every action, so expected free energy quietly reduces to Linear Quadratic Regulator (LQR). +In this workshop, in about ten lines of Python that will look familiar to anyone who has used pymdp, we are going to build a rocket! Then, we add the interesting part, curiosity, by making a surgical, biologically realistic change: adding sensor noise that varies based on the rocket's position. We will see how the flight path bends when we add artifacts like a navigation beacon or a blackout zone, and explore the science as to why. You will write every line yourself in a Colab notebook and do not worry there will be plenty of scaffolding from the cpomdp team. Code hints will drop in on a timer, and a reference cell for anyone who gets stuck or would rather just see the finished steps. We will build this in three steps: the 1960 rocket, a nav beacon it can fix its position by, and a blackout zone. Everything we use runs on the PyPI release of cpomdp and anything you build in the notebook is yours to keep and continue working on. Prerequisites: comfortable editing a few lines of Python. Familiarity with pymdp helps but is not required. Nothing to install; a browser and a google account is enough. If you would like to use your own IDE feel free but we may not be able to provide 1-on-1 support if you get stuck. Source: [submitted source]() -Abstract SHA256: 8d2e1b9353c6c0730a9a5fd5a253822fbfc29c8147f8ee62cb7bdd1916f01896 +Abstract SHA256: d931a84a8cc4febe9f64cc6ac8033382dc79d8c1ff49314e54da5f5db1ed0fd3 ## Organizer review checklist @@ -35,4 +35,4 @@ These approved event resources are starting points; topic relevance and author i - pymdp: https://github.com/infer-actively/pymdp - Reproducible publication template: https://github.com/docxology/template -OpenAlex discovery-only search (not a verified identity or literature match): https://openalex.org/works?search=Build%20a%20curious%20rocket%3A%20the%20hands-on%20cpomdp%20tutorial%20for%20the%20Applied%20Active%20Inference%20Symposium +OpenAlex discovery-only search (not a verified identity or literature match): https://openalex.org/works?search=Build%20a%20curious%20rocket%3A%20a%20hands-on%20continuous-state%20AIF%20session%20with%20cpomdp diff --git a/data/public/2026/dossiers/talk-7bfba1b90b06334d.md b/data/public/2026/dossiers/talk-7bfba1b90b06334d.md index 1066616..d08734b 100644 --- a/data/public/2026/dossiers/talk-7bfba1b90b06334d.md +++ b/data/public/2026/dossiers/talk-7bfba1b90b06334d.md @@ -8,7 +8,7 @@ Public ID: talk-7bfba1b90b06334d ## Submitted abstract -Most agent architectures collapse two things that phenomenology keeps apart: what the agent should do immediately, and what kind of world the agent takes itself to be in. This session introduces “perspective” as a distinct architectural layer that carries the second. +Most agent architectures collapse two things that phenomenology keeps apart: what the agent should do immediately, and what kind of world the agent takes itself to be in. This session introduces “perspective” as a distinct architectural layer that carries the second. Perspective is a slowly evolving, history-sensitive latent state that gates how observations are encoded. Importantly, it is decoupled from policy gradients and is never itself an optimization target. Because it is decoupled, it is portable and can be attached on top of an existing policy-optimizing agent rather than requiring a bespoke design. The first part of the session develops the conceptual frame: how a phenomenologically motivated condition translates into a concrete architectural commitment, and why that commitment is orthogonal to belief-state inference and policy selection. I then trace the capabilities that this single layer has been shown to support across a sequence of studies, including directional hysteresis, history-dependent perceptual reorganization, and body-grounded conative orientation, treating these as unified aspects of a single portable module. There will then be a live demonstration attaching the perspective layer to an active inference agent, such as a hierarchical Bayesian / discrete state-space model, and running it in parallel with its unaugmented counterpart in the same environment, so participants can see exactly what the layer adds and where it plugs in. @@ -16,7 +16,7 @@ The session closes with a structured discussion in which participants are invite Source: [submitted source]() -Abstract SHA256: 52989bb421dddd9c78c2b4a2d77e98d7e180d842e16e392fbfad17a36a1a7413 +Abstract SHA256: 69a7502ac6873d79a8117eb33edb8a4cbda4e779c9dd8b1db7634094809096b3 ## Organizer review checklist diff --git a/data/public/2026/dossiers/talk-81e4f2c68471e90d.md b/data/public/2026/dossiers/talk-81e4f2c68471e90d.md index 18da9b0..1c311a0 100644 --- a/data/public/2026/dossiers/talk-81e4f2c68471e90d.md +++ b/data/public/2026/dossiers/talk-81e4f2c68471e90d.md @@ -8,14 +8,14 @@ Public ID: talk-81e4f2c68471e90d ## Submitted abstract -The Rosales-Gheuens framework challenges the assumption that brute-force energy alone saves open systems from collapse. In this session, we will introduce a testable, normalized physics layer that merges the Rosales congruence principle with Gheuens’ interaction geometry. Rather than replacing established physical laws, this framework acts as a powerful diagnostic tool that sits cleanly on top of them. Our core master equation posits that a system's survival requires geometrically aligned effective recovery multiplied by a structural congruence gate, minus total perturbation. +The Rosales-Gheuens framework challenges the assumption that brute-force energy alone saves open systems from collapse. In this session, we will introduce a testable, normalized physics layer that merges the Rosales congruence principle with Gheuens’ interaction geometry. Rather than replacing established physical laws, this framework acts as a powerful diagnostic tool that sits cleanly on top of them. Our core master equation posits that a system's survival requires geometrically aligned effective recovery multiplied by a structural congruence gate, minus total perturbation. Crucially, we will explore how this model bridges directly to Karl Friston’s Free Energy Principle. Friston dictates that systems persist by minimizing variational free energy, or "surprise." We will demonstrate how our framework translates this active inference into hard physical variables, mathematically formalizing how a system must continuously minimize structural mismatch and physical dissipation to avoid shattering. -To ground the theory empirically, we will use Quantum Error Correction (QEC) as our primary physical anchor, mapping code decoders and physical error rates directly to aligned recovery and perturbation. +To ground the theory empirically, we will use Quantum Error Correction (QEC) as our primary physical anchor, mapping code decoders and physical error rates directly to aligned recovery and perturbation. Interactive Aspect:The session will conclude with a collaborative exercise where attendees can apply the framework's "falsification matrix" to a theoretical open system, testing how our bounded composite equation predicts system survival compared to simpler Markovian baselines. Source: [submitted source]() -Abstract SHA256: ea87a50a101d45ee662cd27396a9c1b6aec25174405a8cbd50171fc98e0f8b89 +Abstract SHA256: c11e491acb2cbdc1168560dd59fd8b34c93730cdb13ff63640eafc847c9d7c85 ## Organizer review checklist diff --git a/data/public/2026/dossiers/talk-862add1fbafe9ec5.md b/data/public/2026/dossiers/talk-862add1fbafe9ec5.md new file mode 100644 index 0000000..8da599b --- /dev/null +++ b/data/public/2026/dossiers/talk-862add1fbafe9ec5.md @@ -0,0 +1,37 @@ +# Commongrounds AI: alife-inspired virtual environments for embodied active inference agents + +Presenter: Nick Hay, Adam Safron + +Contribution: Pre-recorded Video presentation + +Public ID: talk-862add1fbafe9ec5 + +## Submitted abstract + +Common Grounds AI is making virtual worlds where humans and AIs can inhabit simulated bodies and playfully pursue open-ended goals together. Each world is a rich but simplified analog of our own, using material physics simulations to ground core aspects of human experience. +Today’s AIs are largely trained on fragmented, ungrounded text that conveys a distorted, partial version of human conceptual ontology. We are closing this ontological gap by building rich virtual playgrounds where AIs and humans can share social and physical experiences, grounding language in embodied interactions that mirror young children’s cognitive development. Our most recent prototype models temperature and chemical reactions. It also includes experimental examples of native creatures that consume environmental free energy and move using muscle analogues, with plans for action-perception loops guided by Active Inference principles. We plan to explore whether incorporating embodiment yields systems that have ontologies more compatible with human cognition, both by fine-tuning LLMs on embodied experience and by building hybrid architectures merging the environment’s native creatures with LLMs. +Our virtual worlds will be broadly accessible (playable in a browser, no installation needed), released as open-source software, and (we hope) fun to play. We want these worlds to serve as a common ground for humans and many forms of AI and AI research to meet. + +Source: [submitted source]() + +Abstract SHA256: f5122156b0817958d2affb4736cd65137fa506451ce4578d5f61a97904cae213 + +## Organizer review checklist + +- [ ] Confirm the submitted title, presenter, and abstract with the presenter. +- [ ] Confirm contribution format and scheduling needs. +- [ ] Confirm public permissions for any additional material before publication. +- [ ] Prepare discussion questions grounded in the submitted abstract. + +## Public learning resources + +These approved event resources are starting points; topic relevance and author identity have not been inferred. + +- Active Inference Ontology: https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology +- Cognitive Agent Modeling: https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations +- GEO-INFER: https://github.com/ActiveInferenceInstitute/GEO_INFER +- Generalized Notation Notation: https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation +- pymdp: https://github.com/infer-actively/pymdp +- Reproducible publication template: https://github.com/docxology/template + +OpenAlex discovery-only search (not a verified identity or literature match): https://openalex.org/works?search=Commongrounds%20AI%3A%20alife-inspired%20virtual%20environments%20for%20embodied%20active%20inference%20agents diff --git a/data/public/2026/dossiers/talk-926ae4cbac21b98d.md b/data/public/2026/dossiers/talk-926ae4cbac21b98d.md new file mode 100644 index 0000000..e98a046 --- /dev/null +++ b/data/public/2026/dossiers/talk-926ae4cbac21b98d.md @@ -0,0 +1,38 @@ +# VAMAI: Variational Affordance Mapping and Active Inference for Training-Free UAV Navigation + +Presenter: Harshil Shah, Satyaki Maitra + +Contribution: Pre-recorded Video presentation + +Public ID: talk-926ae4cbac21b98d + +## Submitted abstract + +We present VAMAI (Variational Affordance Mapping and Active Inference), a training-free hierarchical framework for autonomous UAV navigation in partially observed environments where reliable GPS may be unavailable. VAMAI brings together affordance theory, probabilistic environmental modeling, and active inference to address how an autonomous agent can navigate while continually learning about its surroundings. +VAMAI connects geometric perception to the question of which actions the environment affords the drone. It maintains probabilistic beliefs over possible obstacle configurations, updated through LiDAR and depth-sensing observations. From these beliefs, the system derives regional suitability estimates describing whether passages are blocked, constrained, or sufficiently open for traversal. +For decision-making under uncertainty, Expected Free Energy (EFE)-based planning evaluates candidate movements using goal progress, regional suitability, and the expected information gain of future observations across spatial regions. At the local level, a separate motion-planning and control system translates selected intentions into dynamically feasible trajectories, accounting for obstacles, vehicle motion, and evolving environmental beliefs. +We will illustrate this process through simulation examples, tracing how sensor observations influence environmental beliefs, regional navigation decisions, and continuous flight execution. We will also discuss the computational and architectural challenges of coordinating perception, inference, planning, and control as independently progressing components, and how these considerations shape the development of a scalable active inference navigation system. + +Source: [submitted source]() + +Abstract SHA256: de462961da948aa635ce4424887e812ec325edb9952a55082776735f2755e86f + +## Organizer review checklist + +- [ ] Confirm the submitted title, presenter, and abstract with the presenter. +- [ ] Confirm contribution format and scheduling needs. +- [ ] Confirm public permissions for any additional material before publication. +- [ ] Prepare discussion questions grounded in the submitted abstract. + +## Public learning resources + +These approved event resources are starting points; topic relevance and author identity have not been inferred. + +- Active Inference Ontology: https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology +- Cognitive Agent Modeling: https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations +- GEO-INFER: https://github.com/ActiveInferenceInstitute/GEO_INFER +- Generalized Notation Notation: https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation +- pymdp: https://github.com/infer-actively/pymdp +- Reproducible publication template: https://github.com/docxology/template + +OpenAlex discovery-only search (not a verified identity or literature match): https://openalex.org/works?search=VAMAI%3A%20Variational%20Affordance%20Mapping%20and%20Active%20Inference%20for%20Training-Free%20UAV%20Navigation diff --git a/data/public/2026/dossiers/talk-97efa98e1c62e734.md b/data/public/2026/dossiers/talk-97efa98e1c62e734.md new file mode 100644 index 0000000..91bb7da --- /dev/null +++ b/data/public/2026/dossiers/talk-97efa98e1c62e734.md @@ -0,0 +1,35 @@ +# Knowthyself — A code-free active inference workbench + +Presenter: Ted J. Vrioni + +Contribution: Livestreamed panel or group session + +Public ID: talk-97efa98e1c62e734 + +## Submitted abstract + +If pymdp was an engine, Knowthyself is an attempt to give it a chassis and wheels. The whole point is to promote a workbench to build your active inference agent and environments without writing a single line of code. It’s beginner- and expert-friendly, fostering education, research and development, and maybe the idea of a “hub of digital twins” for currently existing literature. It comes in progressive levels of “difficulty” which unlock various tools and functionalities. You become a particular chemist, authoring all sort of “herds of strange things”. + +Source: [submitted source]() + +Abstract SHA256: 87457746f4b47009112acf66f14942725b9434b766908dbeb206642c35a59ecb + +## Organizer review checklist + +- [ ] Confirm the submitted title, presenter, and abstract with the presenter. +- [ ] Confirm contribution format and scheduling needs. +- [ ] Confirm public permissions for any additional material before publication. +- [ ] Prepare discussion questions grounded in the submitted abstract. + +## Public learning resources + +These approved event resources are starting points; topic relevance and author identity have not been inferred. + +- Active Inference Ontology: https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology +- Cognitive Agent Modeling: https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations +- GEO-INFER: https://github.com/ActiveInferenceInstitute/GEO_INFER +- Generalized Notation Notation: https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation +- pymdp: https://github.com/infer-actively/pymdp +- Reproducible publication template: https://github.com/docxology/template + +OpenAlex discovery-only search (not a verified identity or literature match): https://openalex.org/works?search=Knowthyself%20%E2%80%94%20A%20code-free%20active%20inference%20workbench diff --git a/data/public/2026/dossiers/talk-bb62d4494869e307.md b/data/public/2026/dossiers/talk-98b78d49f79fcb14.md similarity index 57% rename from data/public/2026/dossiers/talk-bb62d4494869e307.md rename to data/public/2026/dossiers/talk-98b78d49f79fcb14.md index dcac4b8..148cff5 100644 --- a/data/public/2026/dossiers/talk-bb62d4494869e307.md +++ b/data/public/2026/dossiers/talk-98b78d49f79fcb14.md @@ -1,18 +1,18 @@ -# Working Groups Sessions (3 sessions: Opening, Charter Session, Outcomes and Next Steps) +# Working Groups Sessions (2 sessions: Opening & Charter; Outcomes & Next Steps) Presenter: Ed Ober Contribution: Livestreamed panel or group session -Public ID: talk-bb62d4494869e307 +Public ID: talk-98b78d49f79fcb14 ## Submitted abstract -AII proposes to facilitate / host active inference working groups as explained in our paper “Coordinating the Field”, with a field group and one or more domain groups. The first symposium session will introduce the concept, recap the paper and reason for the groups and solicit suggested domains and participants. The second session will review the proposed charter and consider amendments, with versioning per group, considering nominations for chair(s) and secretary(ies), and a goal of adopting the charter(s) during the symposium if possible and establish post-symposium meeting cadence and work plan. The third session will report back to the main session recap what was accomplished during the symposium and announce any working groups formed and their first year goals established. +AII proposes to facilitate / host active inference working groups as explained in our paper “Coordinating the Field”, with a field group and one or more domain groups. The first symposium session (Opening & Charter, November 12) will introduce the concept, recap the paper and reason for the groups, and solicit suggested domains and participants. It will also review the proposed charter and consider amendments, with versioning per group, considering nominations for chair(s) and secretary(ies), with a goal of adopting the charter(s) during the symposium if possible and establishing post-symposium meeting cadence and work plan. The second session (Outcomes & Next Steps, November 13) will report back to the main session, recap what was accomplished during the symposium and announce any working groups formed and their first year goals established. Source: [submitted source]() -Abstract SHA256: 31a335e107dfddefde2d3a94c2abb6796d6f3864ddaf9aa70815a9791ff5406e +Abstract SHA256: 3a7015d9ea0c9ea278f5e6b301fc762f96b17e592e3e91d4fbd295641ee4cfb6 ## Organizer review checklist @@ -32,4 +32,4 @@ These approved event resources are starting points; topic relevance and author i - pymdp: https://github.com/infer-actively/pymdp - Reproducible publication template: https://github.com/docxology/template -OpenAlex discovery-only search (not a verified identity or literature match): https://openalex.org/works?search=Working%20Groups%20Sessions%20%283%20sessions%3A%20Opening%2C%20Charter%20Session%2C%20Outcomes%20and%20Next%20Steps%29 +OpenAlex discovery-only search (not a verified identity or literature match): https://openalex.org/works?search=Working%20Groups%20Sessions%20%282%20sessions%3A%20Opening%20%26%20Charter%3B%20Outcomes%20%26%20Next%20Steps%29 diff --git a/data/public/2026/dossiers/talk-c43c58f58bdbaa14.md b/data/public/2026/dossiers/talk-c43c58f58bdbaa14.md new file mode 100644 index 0000000..6df25dd --- /dev/null +++ b/data/public/2026/dossiers/talk-c43c58f58bdbaa14.md @@ -0,0 +1,35 @@ +# Maladaptive Daydreaming as a Precision Trap: An Active Inference Framework, Its Testable Core, and Evidence from Daily Diaries + +Presenter: Bo Lee + +Contribution: Pre-recorded Video presentation + +Public ID: talk-c43c58f58bdbaa14 + +## Submitted abstract + +Maladaptive daydreaming (MD) involves prolonged immersion in self-generated fantasy and difficulty disengaging despite awareness that the experience is imagined. We propose a testable active-inference framework in which the precision assigned to internal predictions relative to external sensory evidence may contribute to self-sustaining fantasy. This precision-feedback mechanism is a hypothesis, not an established clinical explanation. Using analytic counterexamples and 115,200 synthetic datasets, we show why finite-time persistence and hysteresis-like observations alone do not identify bistability, and examine how ramp-and-hold experimental designs can improve discrimination among competing models. Exploratory secondary analyses of daily-diary data are discussed at their actual timescale, without treating daily associations as proof of an episode-level mechanism. The talk connects active inference, computational psychiatry, experimental design, and open science. It concludes with falsifiable predictions and a research agenda focused on measuring internal and sensory precision, tracking within-episode dynamics, comparing alternative models, and testing feasible interventions. We welcome discussion of practical measurements and alternative explanations. + +Source: [submitted source]() + +Abstract SHA256: 64ae2fa262858b682678ae45ea44c066914791e44d8dc79fd15cc8279fc748a8 + +## Organizer review checklist + +- [ ] Confirm the submitted title, presenter, and abstract with the presenter. +- [ ] Confirm contribution format and scheduling needs. +- [ ] Confirm public permissions for any additional material before publication. +- [ ] Prepare discussion questions grounded in the submitted abstract. + +## Public learning resources + +These approved event resources are starting points; topic relevance and author identity have not been inferred. + +- Active Inference Ontology: https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology +- Cognitive Agent Modeling: https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations +- GEO-INFER: https://github.com/ActiveInferenceInstitute/GEO_INFER +- Generalized Notation Notation: https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation +- pymdp: https://github.com/infer-actively/pymdp +- Reproducible publication template: https://github.com/docxology/template + +OpenAlex discovery-only search (not a verified identity or literature match): https://openalex.org/works?search=Maladaptive%20Daydreaming%20as%20a%20Precision%20Trap%3A%20An%20Active%20Inference%20Framework%2C%20Its%20Testable%20Core%2C%20and%20Evidence%20from%20Daily%20Diaries diff --git a/data/public/2026/dossiers/talk-cd9f12051e1897ff.md b/data/public/2026/dossiers/talk-cd9f12051e1897ff.md new file mode 100644 index 0000000..7407cc0 --- /dev/null +++ b/data/public/2026/dossiers/talk-cd9f12051e1897ff.md @@ -0,0 +1,52 @@ +# The Minimal Necessary Basis for Primitive State-of-Affairs Inquiry + +Presenter: Bruce Tisler + +Contribution: Pre-recorded Video presentation + +Public ID: talk-cd9f12051e1897ff + +## Submitted abstract + +Discovering whether there is a minimum structure required for inquiry to be complete. +The paper begins with six familiar questions: Who, What, Where, When, How, and Why. +But the research is asking something deeper: Are these six categories simply a useful convention, or does each represent something fundamentally necessary about what can be known? +Consider the bridge-collapse example. +Two bridge collapses occur. For both, the involved parties are identified +(WHO: the same engineering firm and inspection authority in both cases). The event is +specified (WHAT: structural failure of a suspension bridge). The locations are of the +same type (WHERE: bridges of identical design crossing rivers of similar flow rate). The +time and sequence of failure are identical (WHEN: both collapse within a three-minute +window following the same triggering load). The mechanical failure sequence is +identical in both cases (HOW: the same crack propagation pattern, the same load +transfer failure, the same sequence of component failures). +Even with all five answers fixed and identical across both collapses, the explanation +differs: in the first case, deferred maintenance created metal fatigue that made the +bridge susceptible to normal load; in the second, a design flaw in the original +specifications made the bridge inherently susceptible under identical conditions. The +identical HOW does not determine WHY. The joint answer set does not determine +WHY. Therefore WHY is independent of WHO, WHAT, WHERE, WHEN, and HOW. + +Source: [submitted source]() + +Abstract SHA256: 821eba25fae79757eeed59a976333b863934dc3bd29dd71d399b536da134a886 + +## Organizer review checklist + +- [ ] Confirm the submitted title, presenter, and abstract with the presenter. +- [ ] Confirm contribution format and scheduling needs. +- [ ] Confirm public permissions for any additional material before publication. +- [ ] Prepare discussion questions grounded in the submitted abstract. + +## Public learning resources + +These approved event resources are starting points; topic relevance and author identity have not been inferred. + +- Active Inference Ontology: https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology +- Cognitive Agent Modeling: https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations +- GEO-INFER: https://github.com/ActiveInferenceInstitute/GEO_INFER +- Generalized Notation Notation: https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation +- pymdp: https://github.com/infer-actively/pymdp +- Reproducible publication template: https://github.com/docxology/template + +OpenAlex discovery-only search (not a verified identity or literature match): https://openalex.org/works?search=The%20Minimal%20Necessary%20Basis%20for%20Primitive%20State-of-Affairs%20Inquiry diff --git a/data/public/2026/dossiers/talk-d6fc5ed19b44f3aa.md b/data/public/2026/dossiers/talk-d6fc5ed19b44f3aa.md new file mode 100644 index 0000000..22e09b0 --- /dev/null +++ b/data/public/2026/dossiers/talk-d6fc5ed19b44f3aa.md @@ -0,0 +1,38 @@ +# A derived self + +Presenter: Viraj Deshwal + +Contribution: Pre-recorded Video presentation + +Public ID: talk-d6fc5ed19b44f3aa + +## Submitted abstract + +A substrate that derives its own body in its own camera through published retinal biology running on hardware. The reafference ladder M0 to M3 is the engineering sequence: discretize action with a move-stop policy, let the body report its own arrival through proprioceptive timing, make arrival predictable, and identify the body as a place through sensitize-adapt dynamics. +The retinal circuit is the one measured in salamander retina by Kastner and Baccus (2013) and others, with thirteen published constants carried at their measured values. Nothing in the chain is tuned. The release time constant arrives on our camera at 2.35 seconds against the paper’s 2.40, over 98 continuous movement offsets in a novel room with direct sun and moving shadow. The body falls out as the region of the image that stays still while everything touching it moves. No markers, no CAD model, no training set. +The talk will present the implementation, the pre-registered falsification conditions, and the honest limits. τ_on fails against the instrument ceiling. One parameter is not derived. The core observation worth the room’s attention is that the policy chosen at M0 for credit assignment also solves self-localization at M3. One policy. Two problems usually treated as separate. Active inference predicts this in principle through information-maximizing policy selection, but the specific case of move-stop resolving both at once appears to be new to the literature. +Pre-recorded with embedded demonstration video. Live Q&A after broadcast if the symposium runs one. + +Source: [submitted source]() + +Abstract SHA256: be7c7c71a12fb0a97c5ca87c58c0ef202b7305634cd307d0ee34dd5351a543b2 + +## Organizer review checklist + +- [ ] Confirm the submitted title, presenter, and abstract with the presenter. +- [ ] Confirm contribution format and scheduling needs. +- [ ] Confirm public permissions for any additional material before publication. +- [ ] Prepare discussion questions grounded in the submitted abstract. + +## Public learning resources + +These approved event resources are starting points; topic relevance and author identity have not been inferred. + +- Active Inference Ontology: https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology +- Cognitive Agent Modeling: https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations +- GEO-INFER: https://github.com/ActiveInferenceInstitute/GEO_INFER +- Generalized Notation Notation: https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation +- pymdp: https://github.com/infer-actively/pymdp +- Reproducible publication template: https://github.com/docxology/template + +OpenAlex discovery-only search (not a verified identity or literature match): https://openalex.org/works?search=A%20derived%20self diff --git a/data/public/2026/dossiers/talk-dba92aa493206478.md b/data/public/2026/dossiers/talk-dba92aa493206478.md index 2cb0168..d2c4fe5 100644 --- a/data/public/2026/dossiers/talk-dba92aa493206478.md +++ b/data/public/2026/dossiers/talk-dba92aa493206478.md @@ -8,11 +8,11 @@ Public ID: talk-dba92aa493206478 ## Submitted abstract -Tiny social units of Pliocene Hominina fostered exchange among and between adults, youngsters and babies, about information gleaned and garnered by Active Inference. Henceforth, human evolution would involve the prehistory of "Homo informatio" whose biological, social, technological, and cultural legacy is an outcome of evolutionary 'snakes and ladders' autopoiesis and self-evidencing, indicated by archaeological irregular regularities and regular irrrgularities in the worldwide Pleistocene spatiotemporal record (as only is to be expected by the itinerancy of stochastic markovian processes), and of uneven imperceptible network-spread between social units, individuals, and, naturally, their cerebral neuronal microcircuits. Spreading, deep, informational networks characterised and underpinned human evolution during four million years. They do so still. "Mighty oaks from litte acorns grow", Geoffrey Chaucer, dixit in "Troilus and Creysede" in 1374. +Tiny social units of Pliocene Hominina fostered exchange among and between adults, youngsters and babies, about information gleaned and garnered by Active Inference. Henceforth, human evolution would involve the prehistory of "Homo informatio" whose biological, social, technological, and cultural legacy is an outcome of evolutionary 'snakes and ladders' autopoiesis and self-evidencing, indicated by archaeological irregular regularities and regular irrrgularities in the worldwide Pleistocene spatiotemporal record (as only is to be expected by the itinerancy of stochastic markovian processes), and of uneven imperceptible network-spread between social units, individuals, and, naturally, their cerebral neuronal microcircuits. Spreading, deep, informational networks characterised and underpinned human evolution during four million years. They do so still. "Mighty oaks from litte acorns grow", Geoffrey Chaucer, dixit in "Troilus and Creysede" in 1374. Source: [submitted source]() -Abstract SHA256: 7c8da7406616421f522639d32cab02420c3042b0ff5bc548b61157c81d446631 +Abstract SHA256: e3f54f3143f768df41d3c729129911f42e4422f32dc2b7661679dd33c5bff57c ## Organizer review checklist diff --git a/data/public/2026/dossiers/talk-6b718c5ddd797287.md b/data/public/2026/dossiers/talk-ddaf5c37722dfa85.md similarity index 97% rename from data/public/2026/dossiers/talk-6b718c5ddd797287.md rename to data/public/2026/dossiers/talk-ddaf5c37722dfa85.md index 6f49bd3..111d3fd 100644 --- a/data/public/2026/dossiers/talk-6b718c5ddd797287.md +++ b/data/public/2026/dossiers/talk-ddaf5c37722dfa85.md @@ -1,10 +1,10 @@ # Recent advances in empirical applications of active inference in computational psychiatry -Presenter: Ryan Smith +Presenter: Ryan Smith Contribution: Livestreamed panel or group session -Public ID: talk-6b718c5ddd797287 +Public ID: talk-ddaf5c37722dfa85 ## Submitted abstract diff --git a/data/public/2026/dossiers/talk-e0738f0a19674084.md b/data/public/2026/dossiers/talk-e0738f0a19674084.md new file mode 100644 index 0000000..25b0969 --- /dev/null +++ b/data/public/2026/dossiers/talk-e0738f0a19674084.md @@ -0,0 +1,37 @@ +# The Screen as a Markov Blanket: An Active Inference Account of Feed-Driven Narrowing, and a Personal Instrument to Measure It + +Presenter: Rajkaushik Borgohain + +Contribution: Pre-recorded Video presentation + +Public ID: talk-e0738f0a19674084 + +## Submitted abstract + +Short-form video feeds are now a major channel of belief formation, yet cognitive security is often studied at the level of institutions and populations rather than individuals. Albarracin et al. (2022) showed with active inference how echo chambers can emerge among peers. We take a first step toward the individual case: one person and an engagement-optimising recommender. +We treat the phone screen as a Markov blanket. What a person is shown forms the sensory states, and what they do forms the active states. We sketch a minimal user generative model in GNN and couple it with a simple recommender in pymdp, to explore when such a loop keeps a feed diverse and when it narrows or escalates. +We present early simulations and outline a privacy-preserving, on-device instrument through which Worldview aims to make these dynamics visible to people: awareness as a product. + +Source: [submitted source]() + +Abstract SHA256: e04a11951161a02f7a174fdf8c2d28b199ec9c1601a378834a9fe16d8f7b4413 + +## Organizer review checklist + +- [ ] Confirm the submitted title, presenter, and abstract with the presenter. +- [ ] Confirm contribution format and scheduling needs. +- [ ] Confirm public permissions for any additional material before publication. +- [ ] Prepare discussion questions grounded in the submitted abstract. + +## Public learning resources + +These approved event resources are starting points; topic relevance and author identity have not been inferred. + +- Active Inference Ontology: https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology +- Cognitive Agent Modeling: https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations +- GEO-INFER: https://github.com/ActiveInferenceInstitute/GEO_INFER +- Generalized Notation Notation: https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation +- pymdp: https://github.com/infer-actively/pymdp +- Reproducible publication template: https://github.com/docxology/template + +OpenAlex discovery-only search (not a verified identity or literature match): https://openalex.org/works?search=The%20Screen%20as%20a%20Markov%20Blanket%3A%20An%20Active%20Inference%20Account%20of%20Feed-Driven%20Narrowing%2C%20and%20a%20Personal%20Instrument%20to%20Measure%20It diff --git a/data/public/2026/dossiers/talk-e486df293cd09fef.md b/data/public/2026/dossiers/talk-e486df293cd09fef.md new file mode 100644 index 0000000..c81362a --- /dev/null +++ b/data/public/2026/dossiers/talk-e486df293cd09fef.md @@ -0,0 +1,35 @@ +# Coupling energy-system models with a seam you can read and an agent you can audit: Active Inference in the NEST programme + +Presenter: Holly Grimm, John H. Clippinger, Daniel Ari Friedman + +Contribution: Pre-recorded Video presentation + +Public ID: talk-e486df293cd09fef + +## Submitted abstract + +No program abstract has been published for this presentation yet. + +Source: [submitted source]() + +Abstract SHA256: e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855 + +## Organizer review checklist + +- [ ] Confirm the submitted title, presenter, and abstract with the presenter. +- [ ] Confirm contribution format and scheduling needs. +- [ ] Confirm public permissions for any additional material before publication. +- [ ] Prepare discussion questions grounded in the submitted abstract. + +## Public learning resources + +These approved event resources are starting points; topic relevance and author identity have not been inferred. + +- Active Inference Ontology: https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology +- Cognitive Agent Modeling: https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations +- GEO-INFER: https://github.com/ActiveInferenceInstitute/GEO_INFER +- Generalized Notation Notation: https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation +- pymdp: https://github.com/infer-actively/pymdp +- Reproducible publication template: https://github.com/docxology/template + +OpenAlex discovery-only search (not a verified identity or literature match): https://openalex.org/works?search=Coupling%20energy-system%20models%20with%20a%20seam%20you%20can%20read%20and%20an%20agent%20you%20can%20audit%3A%20Active%20Inference%20in%20the%20NEST%20programme diff --git a/data/public/2026/dossiers/talk-eb137baf8fa9df10.md b/data/public/2026/dossiers/talk-eb137baf8fa9df10.md new file mode 100644 index 0000000..0da3a30 --- /dev/null +++ b/data/public/2026/dossiers/talk-eb137baf8fa9df10.md @@ -0,0 +1,35 @@ +# Modelling Language Comprehension with Embodied Active Inference + +Presenter: Vitória de Souza + +Contribution: Pre-recorded Video presentation + +Public ID: talk-eb137baf8fa9df10 + +## Submitted abstract + +How do we understand sentences like “The boy is opening a can of soda”? Accounts of grounded and embodied cognition propose that comprehension is supported by the partial reactivation of sensorimotor states. For instance, understanding the “The boy is opening a can of soda” entails reactivating motor and visual perceptual states congruent with the action described (i.e., pinching). While verbal theories have been offered to account for sensorimotor contributions to comprehension, mathematical and mechanistic models are scant. To address this gap, this study provides a proof-of-concept, hierarchical active inference model of sensorimotor contributions to sentence comprehension. In our study, we simulate sensorimotor simulations in a seminal sentence-picture verification task in which participants read a sentence and then are shown a picture of an agent performing an action that either matches or mismatches the shape of the action implied by the sentence. Behaviourally, participants are slower at deciding that the picture reflects the concepts of the sentence when the shape mismatches, implicating sensorimotor reactivation. Our model simulations show that mismatching shapes cause extended epistemic foraging, leading to slower reaction times. This model provides a mathematical and mechanistic account of embodied cognition, extending verbal theories. + +Source: [submitted source]() + +Abstract SHA256: 1e969c3efb7264e47282ed391dc6758c7bad65a02bdf18007573a19e4148c73d + +## Organizer review checklist + +- [ ] Confirm the submitted title, presenter, and abstract with the presenter. +- [ ] Confirm contribution format and scheduling needs. +- [ ] Confirm public permissions for any additional material before publication. +- [ ] Prepare discussion questions grounded in the submitted abstract. + +## Public learning resources + +These approved event resources are starting points; topic relevance and author identity have not been inferred. + +- Active Inference Ontology: https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology +- Cognitive Agent Modeling: https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations +- GEO-INFER: https://github.com/ActiveInferenceInstitute/GEO_INFER +- Generalized Notation Notation: https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation +- pymdp: https://github.com/infer-actively/pymdp +- Reproducible publication template: https://github.com/docxology/template + +OpenAlex discovery-only search (not a verified identity or literature match): https://openalex.org/works?search=Modelling%20Language%20Comprehension%20with%20Embodied%20Active%20Inference diff --git a/data/public/2026/dossiers/talk-f41341ff72644ef5.md b/data/public/2026/dossiers/talk-f41341ff72644ef5.md index a88208d..1289728 100644 --- a/data/public/2026/dossiers/talk-f41341ff72644ef5.md +++ b/data/public/2026/dossiers/talk-f41341ff72644ef5.md @@ -8,11 +8,11 @@ Public ID: talk-f41341ff72644ef5 ## Submitted abstract -This Symposium 2026 tutorial covers Active Inference from various angles based on the experience over several years with teaching and applying Active Inference. It begins with a brief descriptive rehearsal of fundamental ideas and principles akin to other previous tutorials to establish a foundation for the audience. However, this will be followed by a tour through various textbook materials and publications, past and present, to emphasize crucial areas for understanding modelling and application areas. This tutorial will include visual and discursive illustrations of a range of model classes for different use-cases; discussions of existing and under-development software libraries; and some frontiers in modelling for identifying directions for further research applications which can be especially valuable for the broader research community in 2026 onward. Topics will include, non-exhaustively, the Bayesian brain hypothesis and behavioralism versus partial observability and modelling internal states; principles of exploration, learning, and novelty; fundamental discrete and continuous models in Active Inference and their relationship; computational psychiatry and its significance to other domains; the FEP and its historical and practical relationship to Bayesian model comparison; software libraries including SPM, pymdp, RxInfer, RGMs, and other developments; hierarchical and hybrid models; and structure learning. +This Symposium 2026 tutorial covers Active Inference from various angles based on the experience over several years with teaching and applying Active Inference. It begins with a brief descriptive rehearsal of fundamental ideas and principles akin to other previous tutorials to establish a foundation for the audience. However, this will be followed by a tour through various textbook materials and publications, past and present, to emphasize crucial areas for understanding modelling and application areas. This tutorial will include visual and discursive illustrations of a range of model classes for different use-cases; discussions of existing and under-development software libraries; and some frontiers in modelling for identifying directions for further research applications which can be especially valuable for the broader research community in 2026 onward. Topics will include, non-exhaustively, the Bayesian brain hypothesis and behavioralism versus partial observability and modelling internal states; principles of exploration, learning, and novelty; fundamental discrete and continuous models in Active Inference and their relationship; computational psychiatry and its significance to other domains; the FEP and its historical and practical relationship to Bayesian model comparison; software libraries including SPM, pymdp, RxInfer, RGMs, and other developments; hierarchical and hybrid models; and structure learning. Source: [submitted source]() -Abstract SHA256: 7ab6bfb3626aecfdc813fffcdcacac28ff84fdf2486f0001a4d022c5ef910895 +Abstract SHA256: 1acf9b424265b2530a9825e52fe89e9718323b0fe9daa84aff0736c5120d7eb0 ## Organizer review checklist diff --git a/data/public/2026/dossiers/talk-f86214f68688f98b.md b/data/public/2026/dossiers/talk-f86214f68688f98b.md new file mode 100644 index 0000000..cfdd901 --- /dev/null +++ b/data/public/2026/dossiers/talk-f86214f68688f98b.md @@ -0,0 +1,40 @@ +# A New Politics: From Linear to Sorted Politics Through the Policy Map + +Presenter: Montijn van Loo + +Contribution: Livestreamed panel or group session + +Public ID: talk-f86214f68688f98b + +## Submitted abstract + +Political disagreement often reflects competing worldviews: different beliefs about how the world works and what will happen under alternative policies. Yet political debate proceeds linearly, through proposals, responses, and counterresponses. The underlying reasoning and evidence remain fragmented, assumptions can escape scrutiny, and criticism loses its target as debate moves on. Consequently, incompatible worldviews can persist without their disagreements being resolved. +This talk proposes a new politics, motivated by Active Inference, which provides the mathematical object around which political debate can be organized: the policy map. This map expresses a worldview through predicted outcomes under alternative policies. Making it the persistent object of debate allows criticism to retain a stable address rather than disappear in the rhetoric of successive exchanges. +The mathematics of the policy map distinguishes three types of criticism: belief criticism, challenging beliefs about current states or the consequences of policies; outcome incompleteness, identifying omitted consequences; and policy incompleteness, identifying missing alternatives. These provide the horizontal sorting of political debate. +Vertical sorting organizes the reasoning beneath each prediction, allowing criticism to reach its underlying assumptions and evidence. Unresolved objections remain visible until answered or incorporated through revision, potentially changing the predicted outcomes and preferred policies. +In this way, Active Inference offers a normative application that goes beyond describing politics as if political actors performed Bayesian inference. It motivates transforming the very structure of political debate, replacing rhetorical exchanges with cumulative criticism and incentivizing a shared search for the worldview that best fits the available evidence. +The ultimate aim is a politics in which criticism can actually bite, transforming not only political debates, but the world shaped by its decisions. For the only path forward is critique without exception. + +Source: [submitted source]() + +Abstract SHA256: b76d74006fd64c15a12c8d920751256bd858769154f576a9d2af9a5f2d41c6ed + +## Organizer review checklist + +- [ ] Confirm the submitted title, presenter, and abstract with the presenter. +- [ ] Confirm contribution format and scheduling needs. +- [ ] Confirm public permissions for any additional material before publication. +- [ ] Prepare discussion questions grounded in the submitted abstract. + +## Public learning resources + +These approved event resources are starting points; topic relevance and author identity have not been inferred. + +- Active Inference Ontology: https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology +- Cognitive Agent Modeling: https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations +- GEO-INFER: https://github.com/ActiveInferenceInstitute/GEO_INFER +- Generalized Notation Notation: https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation +- pymdp: https://github.com/infer-actively/pymdp +- Reproducible publication template: https://github.com/docxology/template + +OpenAlex discovery-only search (not a verified identity or literature match): https://openalex.org/works?search=A%20New%20Politics%3A%20From%20Linear%20to%20Sorted%20Politics%20Through%20the%20Policy%20Map diff --git a/data/public/2026/participants/person-0be473093b66db0b.md b/data/public/2026/participants/person-0be473093b66db0b.md new file mode 100644 index 0000000..1cce2e2 --- /dev/null +++ b/data/public/2026/participants/person-0be473093b66db0b.md @@ -0,0 +1,55 @@ +# Montijn van Loo + +These are self-reported registration answers, not an assessment or a verified identity. + +## Background + +I am an econometrist gone rogue. Instead of throwing mathematics at finance, I throw Active Inference mathematics at philosophy. See for the consciousness, morality, politics, gender, free will and God applications, with the poem uniting them all. + +## Active Inference Application + +All philosophical domains. + +## Learning Needs + +No answer provided. + +## Provided resources + +Starting points supplied by the organizers; relevance and suitability have not been assessed. + +Generalized Notation Notation + +A text-based notation system for communicating generative models and Active Inference systems that provides a shared language for describing models in a human-readable and machine-processable way. + +https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation + +pymdp + +Community Python library implementing Active Inference for POMDPs. + +https://github.com/infer-actively/pymdp + +Active Inference Ontology + +Maintains and develops the public ontology for Active Inference and decentralized science with structured, machine-readable definitions. + +https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology + +GEO-INFER + +Geospatial modeling using Active Inference and spatial data analysis techniques. + +https://github.com/ActiveInferenceInstitute/GEO_INFER + +Cognitive Agent Modeling + +Developing minimal cognitive agent models using Active Inference principles. + +https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations + +Reproducible publication template + +Manuscript infrastructure used to render this Symposium abstract book as PDF and Word documents. + +https://github.com/docxology/template diff --git a/data/public/2026/participants/person-0c22f37c62cc1e49.md b/data/public/2026/participants/person-0c22f37c62cc1e49.md new file mode 100644 index 0000000..56c39ed --- /dev/null +++ b/data/public/2026/participants/person-0c22f37c62cc1e49.md @@ -0,0 +1,55 @@ +# Giuseppe Pagnoni + +These are self-reported registration answers, not an assessment or a verified identity. + +## Background + +No answer provided. + +## Active Inference Application + +No answer provided. + +## Learning Needs + +Tutorials about how to go from the descripción of a system to deciding an AIF model for that system. + +## Provided resources + +Starting points supplied by the organizers; relevance and suitability have not been assessed. + +Generalized Notation Notation + +A text-based notation system for communicating generative models and Active Inference systems that provides a shared language for describing models in a human-readable and machine-processable way. + +https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation + +pymdp + +Community Python library implementing Active Inference for POMDPs. + +https://github.com/infer-actively/pymdp + +Active Inference Ontology + +Maintains and develops the public ontology for Active Inference and decentralized science with structured, machine-readable definitions. + +https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology + +GEO-INFER + +Geospatial modeling using Active Inference and spatial data analysis techniques. + +https://github.com/ActiveInferenceInstitute/GEO_INFER + +Cognitive Agent Modeling + +Developing minimal cognitive agent models using Active Inference principles. + +https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations + +Reproducible publication template + +Manuscript infrastructure used to render this Symposium abstract book as PDF and Word documents. + +https://github.com/docxology/template diff --git a/data/public/2026/participants/person-21da00c35d3168e6.md b/data/public/2026/participants/person-21da00c35d3168e6.md new file mode 100644 index 0000000..b1e66c0 --- /dev/null +++ b/data/public/2026/participants/person-21da00c35d3168e6.md @@ -0,0 +1,55 @@ +# Carrie Girstsntas + +These are self-reported registration answers, not an assessment or a verified identity. + +## Background + +No answer provided. + +## Active Inference Application + +No answer provided. + +## Learning Needs + +collaboration + +## Provided resources + +Starting points supplied by the organizers; relevance and suitability have not been assessed. + +Generalized Notation Notation + +A text-based notation system for communicating generative models and Active Inference systems that provides a shared language for describing models in a human-readable and machine-processable way. + +https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation + +pymdp + +Community Python library implementing Active Inference for POMDPs. + +https://github.com/infer-actively/pymdp + +Active Inference Ontology + +Maintains and develops the public ontology for Active Inference and decentralized science with structured, machine-readable definitions. + +https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology + +GEO-INFER + +Geospatial modeling using Active Inference and spatial data analysis techniques. + +https://github.com/ActiveInferenceInstitute/GEO_INFER + +Cognitive Agent Modeling + +Developing minimal cognitive agent models using Active Inference principles. + +https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations + +Reproducible publication template + +Manuscript infrastructure used to render this Symposium abstract book as PDF and Word documents. + +https://github.com/docxology/template diff --git a/data/public/2026/participants/person-2d90b7351589fc79.md b/data/public/2026/participants/person-2d90b7351589fc79.md new file mode 100644 index 0000000..7ec8b1f --- /dev/null +++ b/data/public/2026/participants/person-2d90b7351589fc79.md @@ -0,0 +1,57 @@ +# Yukiko NAKATSU + +These are self-reported registration answers, not an assessment or a verified identity. + +## Background + +I work on industrial AI orchestration on the basis of the Free Energy Principle (FEP), from two sides: research and standards. +Research: I am a doctoral student working on FEP as a foundation for next-generation AI infrastructure. My recent paper “How Trust in AI Erodes Expert Judgement”, built on Active Inference, is accepted at the International Conference on Information Management, Information Technology and Artificial Intelligence 2026. +Standards: As an standardization expert mainly at ITU-T, I’m currently working for multiple technical reports on embodied AI and on AI-enabled cities. One of the work connects the process model of the Systems-Theoretic Accident Model and Processes, a controller's belief about what it controls, with the generative model of Active Inference. I also serve as an expert in ISO/IEC committees on smart manufacturing and smart communities. + +## Active Inference Application + +Domain: industrial AI orchestration and physical AI, with cognitive security as my priority topic. Stage: research and pre-standardization. I model how AI orchestration shifts the precision weights of human experts, and I am writing the link between systems-safety analysis and Active Inference into an ITU-T technical report. There is no industrial deployment yet. The next step is to test whether a GNN-style specification can describe a real orchestration problem, with people who can run it. + +## Learning Needs + +Three things would help most. First, open worked examples from industry (control, scheduling, robot fleets) with code and a stated comparison against reinforcement-learning and rule-based baselines. Second, a generative-model specification that several toolchains can execute, so that a result in pymdp can be reproduced in RxInfer.jl without rewriting it. Generalized Notation Notation (GNN) is the natural candidate, and its maintenance and versioning should be settled. Third, a place where people who write standards and regulation can ask practising researchers, in engineering terms, what the theory claims and what it does not. + +## Provided resources + +Starting points supplied by the organizers; relevance and suitability have not been assessed. + +Generalized Notation Notation + +A text-based notation system for communicating generative models and Active Inference systems that provides a shared language for describing models in a human-readable and machine-processable way. + +https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation + +pymdp + +Community Python library implementing Active Inference for POMDPs. + +https://github.com/infer-actively/pymdp + +Active Inference Ontology + +Maintains and develops the public ontology for Active Inference and decentralized science with structured, machine-readable definitions. + +https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology + +GEO-INFER + +Geospatial modeling using Active Inference and spatial data analysis techniques. + +https://github.com/ActiveInferenceInstitute/GEO_INFER + +Cognitive Agent Modeling + +Developing minimal cognitive agent models using Active Inference principles. + +https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations + +Reproducible publication template + +Manuscript infrastructure used to render this Symposium abstract book as PDF and Word documents. + +https://github.com/docxology/template diff --git a/data/public/2026/participants/person-318f9db4d9c80273.md b/data/public/2026/participants/person-318f9db4d9c80273.md new file mode 100644 index 0000000..6d7af01 --- /dev/null +++ b/data/public/2026/participants/person-318f9db4d9c80273.md @@ -0,0 +1,55 @@ +# Andrew Owens + +These are self-reported registration answers, not an assessment or a verified identity. + +## Background + +I was a clinical academic for 14 years investigating the bidirectional relationship between the CNS and ANS and digital biomarkers in dementia. Been in industry for 4 years as Jaguar Land Rover’s cognitive neuroscientist. Worked with Karl ~10 years ago at UCL in interoceptive inference (Owens AP, Allen M, Ondobaka S, Friston KJ (2018). Interoceptive inference: from computational neuroscience to clinic. Neuroscience and Biobehavioral Rev:174-183; Owens AP, Friston KJ, Low DA, Mathias CJ, Critchley HD (2018). Investigating the relationship between cardiac interoception and autonomic cardiac control using a predictive coding framework. Autonomic Neuroscience: Basic and Clinical;210:65-71). + +## Active Inference Application + +Industry + +## Learning Needs + +Resource, tool, or community development, especially practical easy-to-understand guidance on running my own Active Inference (AI) projects in industry, with no day-to-day onsite AI support. + +## Provided resources + +Starting points supplied by the organizers; relevance and suitability have not been assessed. + +Generalized Notation Notation + +A text-based notation system for communicating generative models and Active Inference systems that provides a shared language for describing models in a human-readable and machine-processable way. + +https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation + +pymdp + +Community Python library implementing Active Inference for POMDPs. + +https://github.com/infer-actively/pymdp + +Active Inference Ontology + +Maintains and develops the public ontology for Active Inference and decentralized science with structured, machine-readable definitions. + +https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology + +GEO-INFER + +Geospatial modeling using Active Inference and spatial data analysis techniques. + +https://github.com/ActiveInferenceInstitute/GEO_INFER + +Cognitive Agent Modeling + +Developing minimal cognitive agent models using Active Inference principles. + +https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations + +Reproducible publication template + +Manuscript infrastructure used to render this Symposium abstract book as PDF and Word documents. + +https://github.com/docxology/template diff --git a/data/public/2026/participants/person-373763c50c908454.md b/data/public/2026/participants/person-373763c50c908454.md new file mode 100644 index 0000000..2bdb6bb --- /dev/null +++ b/data/public/2026/participants/person-373763c50c908454.md @@ -0,0 +1,55 @@ +# Luke (Alok) Singh + +These are self-reported registration answers, not an assessment or a verified identity. + +## Background + +No answer provided. + +## Active Inference Application + +No answer provided. + +## Learning Needs + +No answer provided. + +## Provided resources + +Starting points supplied by the organizers; relevance and suitability have not been assessed. + +Generalized Notation Notation + +A text-based notation system for communicating generative models and Active Inference systems that provides a shared language for describing models in a human-readable and machine-processable way. + +https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation + +pymdp + +Community Python library implementing Active Inference for POMDPs. + +https://github.com/infer-actively/pymdp + +Active Inference Ontology + +Maintains and develops the public ontology for Active Inference and decentralized science with structured, machine-readable definitions. + +https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology + +GEO-INFER + +Geospatial modeling using Active Inference and spatial data analysis techniques. + +https://github.com/ActiveInferenceInstitute/GEO_INFER + +Cognitive Agent Modeling + +Developing minimal cognitive agent models using Active Inference principles. + +https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations + +Reproducible publication template + +Manuscript infrastructure used to render this Symposium abstract book as PDF and Word documents. + +https://github.com/docxology/template diff --git a/data/public/2026/participants/person-63f3f0fdc6d424e0.md b/data/public/2026/participants/person-63f3f0fdc6d424e0.md new file mode 100644 index 0000000..443d2b7 --- /dev/null +++ b/data/public/2026/participants/person-63f3f0fdc6d424e0.md @@ -0,0 +1,55 @@ +# Matt Wiesenfarth + +These are self-reported registration answers, not an assessment or a verified identity. + +## Background + +No answer provided. + +## Active Inference Application + +No answer provided. + +## Learning Needs + +No answer provided. + +## Provided resources + +Starting points supplied by the organizers; relevance and suitability have not been assessed. + +Generalized Notation Notation + +A text-based notation system for communicating generative models and Active Inference systems that provides a shared language for describing models in a human-readable and machine-processable way. + +https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation + +pymdp + +Community Python library implementing Active Inference for POMDPs. + +https://github.com/infer-actively/pymdp + +Active Inference Ontology + +Maintains and develops the public ontology for Active Inference and decentralized science with structured, machine-readable definitions. + +https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology + +GEO-INFER + +Geospatial modeling using Active Inference and spatial data analysis techniques. + +https://github.com/ActiveInferenceInstitute/GEO_INFER + +Cognitive Agent Modeling + +Developing minimal cognitive agent models using Active Inference principles. + +https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations + +Reproducible publication template + +Manuscript infrastructure used to render this Symposium abstract book as PDF and Word documents. + +https://github.com/docxology/template diff --git a/data/public/2026/participants/person-64e4618748001f98.md b/data/public/2026/participants/person-64e4618748001f98.md new file mode 100644 index 0000000..3c4d45c --- /dev/null +++ b/data/public/2026/participants/person-64e4618748001f98.md @@ -0,0 +1,70 @@ +# Mikhail P. Shestakov + +These are self-reported registration answers, not an assessment or a verified identity. + +## Background + +I am a Professor and Leading Researcher at the Federal Science Center of Physical Culture and Sport (VNIIFK), Moscow, with a background in sports sciences and motor control. My work applies Active Inference to sensorimotor development and elite sport. +Key publications: +Shestakov, M., Godina, E., Abramova, T., & Korchagin, A. (2026). The effects of different sports activities on body scheme in preschoolers and primary school children: an experimental and theoretical analysis. Journal of Physiological Anthropology, 45(1), 6. https://doi.org/10.1186/s40101-026-00422-0 +— N = 692 children, 3 sports, AIF model of body schema. +Shestakov, M.P., & Abramova, T.F. (2026). Changes in hierarchical dominance in the generative model of movement control in boys aged 7–9. (in press). +— N = 52 longitudinal, CLPM + AIF metrics, hierarchical inversion under dynamic uncertainty. +Shestakov, M.P. (2023). Perception of sports movements. Taganrog: SFU Press. +— Monograph on AIF and sport. +Current work: hybrid AIF–TOC–TDA architecture for motor learning across the full range — from children to elite athletes; OpenSim + stabilometry data across 10+ sports, including national teams and world-class performers. + +## Active Inference Application + +Domain: Sport science and motor development — from children (5–9 years) to elite athletes (national teams, world-class). +Stage: Empirical validation and architecture development. +Empirical: AIF inversion of stabilometric and OpenSim data across 10+ sports; longitudinal studies of hierarchical dominance inversion in children. +Architecture: hybrid AIF–TOC–TDA framework, three timescales (reflex, body schema, planning), POMDP planner at the top level. +Verification: Stage G4 — full verification of the hybrid architecture, including elite kinematics and sensitivity analyses. + +## Learning Needs + +Three things would help most: +(1) Standardized empirical benchmarks for AIF in motor control. Our field lacks shared datasets and metrics — we have built our own (stabilometry + OpenSim across 10+ sports), but a community benchmark would accelerate comparison across models. +(2) Scalable POMDP solvers for continuous state spaces. Our Level-3 planner is the computational bottleneck. A community-maintained library (or a shared recipe for point-based methods at scale) would lower the barrier for applied researchers. +(3) A community of practice for AIF in sport and rehabilitation. Most AIF work is in neuroscience or robotics; applied sport science is a natural but underserved domain. Regular exchanges (workshops, shared code) would help bridge the gap. + +## Provided resources + +Starting points supplied by the organizers; relevance and suitability have not been assessed. + +Generalized Notation Notation + +A text-based notation system for communicating generative models and Active Inference systems that provides a shared language for describing models in a human-readable and machine-processable way. + +https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation + +pymdp + +Community Python library implementing Active Inference for POMDPs. + +https://github.com/infer-actively/pymdp + +Active Inference Ontology + +Maintains and develops the public ontology for Active Inference and decentralized science with structured, machine-readable definitions. + +https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology + +GEO-INFER + +Geospatial modeling using Active Inference and spatial data analysis techniques. + +https://github.com/ActiveInferenceInstitute/GEO_INFER + +Cognitive Agent Modeling + +Developing minimal cognitive agent models using Active Inference principles. + +https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations + +Reproducible publication template + +Manuscript infrastructure used to render this Symposium abstract book as PDF and Word documents. + +https://github.com/docxology/template diff --git a/data/public/2026/participants/person-7e6156e5c03e16f9.md b/data/public/2026/participants/person-7e6156e5c03e16f9.md new file mode 100644 index 0000000..f815301 --- /dev/null +++ b/data/public/2026/participants/person-7e6156e5c03e16f9.md @@ -0,0 +1,55 @@ +# Ted J. Vrioni + +These are self-reported registration answers, not an assessment or a verified identity. + +## Background + +physics, cognition, computation, art — my vibe tends to be more of a generalist, although music is really what I really peak at in terms of “performance”. I see it as “engineering consciousness”, but in the first person pov… and it’s not about creating signal: it’s about filtering noise. Dances of symbols all along. So the work is internal so far, but it is trying to find a way out. I am also working on a few papers though, about physical agents (involving Chladni patterns), making physics with stamps (involving dynamic causal modelling and “ignorance modelling”), consciousness (involving implications of Noether’s theorem for electromagnetic waves), and philosophy (translating Bertrand Russell’s Nobel Lecture’s lessons into active inference exercises — his speech is on Youtube, you’ll love it). It’s private and solo-work so far… slowly but surely. + +## Active Inference Application + +early-mid stage; developing a code-free workbench for agent/env creation for any domain, I hope + +## Learning Needs + +Community, art, and time above all… + +## Provided resources + +Starting points supplied by the organizers; relevance and suitability have not been assessed. + +Generalized Notation Notation + +A text-based notation system for communicating generative models and Active Inference systems that provides a shared language for describing models in a human-readable and machine-processable way. + +https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation + +pymdp + +Community Python library implementing Active Inference for POMDPs. + +https://github.com/infer-actively/pymdp + +Active Inference Ontology + +Maintains and develops the public ontology for Active Inference and decentralized science with structured, machine-readable definitions. + +https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology + +GEO-INFER + +Geospatial modeling using Active Inference and spatial data analysis techniques. + +https://github.com/ActiveInferenceInstitute/GEO_INFER + +Cognitive Agent Modeling + +Developing minimal cognitive agent models using Active Inference principles. + +https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations + +Reproducible publication template + +Manuscript infrastructure used to render this Symposium abstract book as PDF and Word documents. + +https://github.com/docxology/template diff --git a/data/public/2026/participants/person-83708fa21b89d9d5.md b/data/public/2026/participants/person-83708fa21b89d9d5.md new file mode 100644 index 0000000..2eaade8 --- /dev/null +++ b/data/public/2026/participants/person-83708fa21b89d9d5.md @@ -0,0 +1,55 @@ +# Parsa Yousefi + +These are self-reported registration answers, not an assessment or a verified identity. + +## Background + +No answer provided. + +## Active Inference Application + +No answer provided. + +## Learning Needs + +No answer provided. + +## Provided resources + +Starting points supplied by the organizers; relevance and suitability have not been assessed. + +Generalized Notation Notation + +A text-based notation system for communicating generative models and Active Inference systems that provides a shared language for describing models in a human-readable and machine-processable way. + +https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation + +pymdp + +Community Python library implementing Active Inference for POMDPs. + +https://github.com/infer-actively/pymdp + +Active Inference Ontology + +Maintains and develops the public ontology for Active Inference and decentralized science with structured, machine-readable definitions. + +https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology + +GEO-INFER + +Geospatial modeling using Active Inference and spatial data analysis techniques. + +https://github.com/ActiveInferenceInstitute/GEO_INFER + +Cognitive Agent Modeling + +Developing minimal cognitive agent models using Active Inference principles. + +https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations + +Reproducible publication template + +Manuscript infrastructure used to render this Symposium abstract book as PDF and Word documents. + +https://github.com/docxology/template diff --git a/data/public/2026/participants/person-9f1eef64030ad3eb.md b/data/public/2026/participants/person-9f1eef64030ad3eb.md new file mode 100644 index 0000000..d3eb466 --- /dev/null +++ b/data/public/2026/participants/person-9f1eef64030ad3eb.md @@ -0,0 +1,55 @@ +# Olivier CAPRA + +These are self-reported registration answers, not an assessment or a verified identity. + +## Background + +Cognitive Neuroscience + +## Active Inference Application + +No answer provided. + +## Learning Needs + +No answer provided. + +## Provided resources + +Starting points supplied by the organizers; relevance and suitability have not been assessed. + +Generalized Notation Notation + +A text-based notation system for communicating generative models and Active Inference systems that provides a shared language for describing models in a human-readable and machine-processable way. + +https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation + +pymdp + +Community Python library implementing Active Inference for POMDPs. + +https://github.com/infer-actively/pymdp + +Active Inference Ontology + +Maintains and develops the public ontology for Active Inference and decentralized science with structured, machine-readable definitions. + +https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology + +GEO-INFER + +Geospatial modeling using Active Inference and spatial data analysis techniques. + +https://github.com/ActiveInferenceInstitute/GEO_INFER + +Cognitive Agent Modeling + +Developing minimal cognitive agent models using Active Inference principles. + +https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations + +Reproducible publication template + +Manuscript infrastructure used to render this Symposium abstract book as PDF and Word documents. + +https://github.com/docxology/template diff --git a/data/public/2026/participants/person-c18079de9ece0062.md b/data/public/2026/participants/person-c18079de9ece0062.md new file mode 100644 index 0000000..b5e96e0 --- /dev/null +++ b/data/public/2026/participants/person-c18079de9ece0062.md @@ -0,0 +1,55 @@ +# Cleber Gomes + +These are self-reported registration answers, not an assessment or a verified identity. + +## Background + +No answer provided. + +## Active Inference Application + +No answer provided. + +## Learning Needs + +No answer provided. + +## Provided resources + +Starting points supplied by the organizers; relevance and suitability have not been assessed. + +Generalized Notation Notation + +A text-based notation system for communicating generative models and Active Inference systems that provides a shared language for describing models in a human-readable and machine-processable way. + +https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation + +pymdp + +Community Python library implementing Active Inference for POMDPs. + +https://github.com/infer-actively/pymdp + +Active Inference Ontology + +Maintains and develops the public ontology for Active Inference and decentralized science with structured, machine-readable definitions. + +https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology + +GEO-INFER + +Geospatial modeling using Active Inference and spatial data analysis techniques. + +https://github.com/ActiveInferenceInstitute/GEO_INFER + +Cognitive Agent Modeling + +Developing minimal cognitive agent models using Active Inference principles. + +https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations + +Reproducible publication template + +Manuscript infrastructure used to render this Symposium abstract book as PDF and Word documents. + +https://github.com/docxology/template diff --git a/data/public/2026/participants/person-c49945494d69395f.md b/data/public/2026/participants/person-c49945494d69395f.md new file mode 100644 index 0000000..b47012b --- /dev/null +++ b/data/public/2026/participants/person-c49945494d69395f.md @@ -0,0 +1,55 @@ +# Carl Gay + +These are self-reported registration answers, not an assessment or a verified identity. + +## Background + +No answer provided. + +## Active Inference Application + +No answer provided. + +## Learning Needs + +No answer provided. + +## Provided resources + +Starting points supplied by the organizers; relevance and suitability have not been assessed. + +Generalized Notation Notation + +A text-based notation system for communicating generative models and Active Inference systems that provides a shared language for describing models in a human-readable and machine-processable way. + +https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation + +pymdp + +Community Python library implementing Active Inference for POMDPs. + +https://github.com/infer-actively/pymdp + +Active Inference Ontology + +Maintains and develops the public ontology for Active Inference and decentralized science with structured, machine-readable definitions. + +https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology + +GEO-INFER + +Geospatial modeling using Active Inference and spatial data analysis techniques. + +https://github.com/ActiveInferenceInstitute/GEO_INFER + +Cognitive Agent Modeling + +Developing minimal cognitive agent models using Active Inference principles. + +https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations + +Reproducible publication template + +Manuscript infrastructure used to render this Symposium abstract book as PDF and Word documents. + +https://github.com/docxology/template diff --git a/data/public/2026/participants/person-c668b17f763bdfbe.md b/data/public/2026/participants/person-c668b17f763bdfbe.md new file mode 100644 index 0000000..67f5066 --- /dev/null +++ b/data/public/2026/participants/person-c668b17f763bdfbe.md @@ -0,0 +1,55 @@ +# Brad Shepherd + +These are self-reported registration answers, not an assessment or a verified identity. + +## Background + +Subjective, anecdotal, no formal education. + +## Active Inference Application + +Yes, not sure what a domain stage is. + +## Learning Needs + +Abstracts of each talks in 50,000 words in a txt format + +## Provided resources + +Starting points supplied by the organizers; relevance and suitability have not been assessed. + +Generalized Notation Notation + +A text-based notation system for communicating generative models and Active Inference systems that provides a shared language for describing models in a human-readable and machine-processable way. + +https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation + +pymdp + +Community Python library implementing Active Inference for POMDPs. + +https://github.com/infer-actively/pymdp + +Active Inference Ontology + +Maintains and develops the public ontology for Active Inference and decentralized science with structured, machine-readable definitions. + +https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology + +GEO-INFER + +Geospatial modeling using Active Inference and spatial data analysis techniques. + +https://github.com/ActiveInferenceInstitute/GEO_INFER + +Cognitive Agent Modeling + +Developing minimal cognitive agent models using Active Inference principles. + +https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations + +Reproducible publication template + +Manuscript infrastructure used to render this Symposium abstract book as PDF and Word documents. + +https://github.com/docxology/template diff --git a/data/public/2026/participants/person-e7b4cae1566a2b24.md b/data/public/2026/participants/person-e7b4cae1566a2b24.md new file mode 100644 index 0000000..88b0133 --- /dev/null +++ b/data/public/2026/participants/person-e7b4cae1566a2b24.md @@ -0,0 +1,55 @@ +# Candice Koolhaas + +These are self-reported registration answers, not an assessment or a verified identity. + +## Background + +PhD student in developmental cognitive science + +## Active Inference Application + +Just curious about it + +## Learning Needs + +Tutorial for beginners/syllabus of required or recommended knowledge (e.g., what math is needed to fully understand and implement) + +## Provided resources + +Starting points supplied by the organizers; relevance and suitability have not been assessed. + +Generalized Notation Notation + +A text-based notation system for communicating generative models and Active Inference systems that provides a shared language for describing models in a human-readable and machine-processable way. + +https://github.com/ActiveInferenceInstitute/Generalized_Notation_Notation + +pymdp + +Community Python library implementing Active Inference for POMDPs. + +https://github.com/infer-actively/pymdp + +Active Inference Ontology + +Maintains and develops the public ontology for Active Inference and decentralized science with structured, machine-readable definitions. + +https://github.com/ActiveInferenceInstitute/Active_Inference_Ontology + +GEO-INFER + +Geospatial modeling using Active Inference and spatial data analysis techniques. + +https://github.com/ActiveInferenceInstitute/GEO_INFER + +Cognitive Agent Modeling + +Developing minimal cognitive agent models using Active Inference principles. + +https://github.com/ActiveInferenceInstitute/ActiveInferenceImplementations + +Reproducible publication template + +Manuscript infrastructure used to render this Symposium abstract book as PDF and Word documents. + +https://github.com/docxology/template diff --git a/data/public/2026/program.ics b/data/public/2026/program.ics index 6272e45..699c1ea 100644 --- a/data/public/2026/program.ics +++ b/data/public/2026/program.ics @@ -5,7 +5,7 @@ BEGIN:VEVENT SUMMARY:An elementary form of agency at planetary-scale DTSTART:20261112T120000Z DTEND:20261112T130000Z -DTSTAMP:20261027T022753Z +DTSTAMP:20261101T230331Z UID:symposium-2026-session-c96e37c40f2f6008 DESCRIPTION:Presenter: Sergio Rubin END:VEVENT @@ -13,7 +13,7 @@ BEGIN:VEVENT SUMMARY:Opening Keynote and Panel DTSTART:20261112T130000Z DTEND:20261112T140000Z -DTSTAMP:20261027T022753Z +DTSTAMP:20261101T230331Z UID:symposium-2026-session-980c7fe3b12846e9 DESCRIPTION:Presenter: Karl Friston END:VEVENT @@ -22,7 +22,7 @@ SUMMARY:Recent advances in empirical applications of active inference in c omputational psychiatry DTSTART:20261112T140000Z DTEND:20261112T150000Z -DTSTAMP:20261027T022753Z +DTSTAMP:20261101T230331Z UID:symposium-2026-session-ac78a95c400be1eb DESCRIPTION:Presenter: Ryan Smith END:VEVENT @@ -30,16 +30,24 @@ BEGIN:VEVENT SUMMARY:Root Frequency Theory: An Integrative Framework for the Continuity of Lived Experience DTSTART:20261112T150000Z -DTEND:20261112T160000Z -DTSTAMP:20261027T022753Z +DTEND:20261112T153000Z +DTSTAMP:20261101T230331Z UID:symposium-2026-session-980a444c33f7622d DESCRIPTION:Presenter: Bianca Avanzo END:VEVENT BEGIN:VEVENT +SUMMARY:Knowthyself — A code-free active inference workbench +DTSTART:20261112T153000Z +DTEND:20261112T160000Z +DTSTAMP:20261101T230331Z +UID:symposium-2026-session-1c5899f2b33b194d +DESCRIPTION:Presenter: Ted J. Vrioni +END:VEVENT +BEGIN:VEVENT SUMMARY:Working Groups Session 1 ~ Opening & Charter DTSTART:20261112T160000Z DTEND:20261112T170000Z -DTSTAMP:20261027T022753Z +DTSTAMP:20261101T230331Z UID:symposium-2026-session-b196df2921dc6a17 DESCRIPTION:Presenter: Ed Ober END:VEVENT @@ -47,7 +55,7 @@ BEGIN:VEVENT SUMMARY:Active Inference\, Bayesian Causal Learning and Empowerment in RL. DTSTART:20261112T170000Z DTEND:20261112T180000Z -DTSTAMP:20261027T022753Z +DTSTAMP:20261101T230331Z UID:symposium-2026-session-921579c396db78dc DESCRIPTION:Presenter: Alison Gopnik END:VEVENT @@ -55,16 +63,34 @@ BEGIN:VEVENT SUMMARY:A Modular Perspective Layer for Active Inference Agents DTSTART:20261112T180000Z DTEND:20261112T193000Z -DTSTAMP:20261027T022753Z +DTSTAMP:20261101T230331Z UID:symposium-2026-session-ac9b13c80a37f31e DESCRIPTION:Presenter: Hongju Pae END:VEVENT BEGIN:VEVENT +SUMMARY:CogNarr Story Graph: A Formal Semantics for Narrative as a Belief + Model +DTSTART:20261112T193000Z +DTEND:20261112T203000Z +DTSTAMP:20261101T230331Z +UID:symposium-2026-session-f043ab2fc8467053 +DESCRIPTION:Presenter: John Boik\, Glen EP Ropella +END:VEVENT +BEGIN:VEVENT +SUMMARY:Explicating and Comparing Prediction Architectures of Theories of + Consciousness and its Application to INTREPID +DTSTART:20261112T203000Z +DTEND:20261112T213000Z +DTSTAMP:20261101T230331Z +UID:symposium-2026-session-80a7aee761af128e +DESCRIPTION:Presenter: Montijn van Loo +END:VEVENT +BEGIN:VEVENT SUMMARY:Reverse engineering generative models from zebrafish decision-maki ng DTSTART:20261113T120000Z DTEND:20261113T130000Z -DTSTAMP:20261027T022753Z +DTSTAMP:20261101T230331Z UID:symposium-2026-session-643951a3731bf120 DESCRIPTION:Presenter: Takuya Isomura END:VEVENT @@ -73,7 +99,7 @@ SUMMARY:From Self-Evidencing to Global Governance: Active Inference and th e Dynamics of Justice DTSTART:20261113T130000Z DTEND:20261113T140000Z -DTSTAMP:20261027T022753Z +DTSTAMP:20261101T230331Z UID:symposium-2026-session-0b80cf1a50dd81f2 DESCRIPTION:Presenter: Majid D. Beni END:VEVENT @@ -81,42 +107,51 @@ BEGIN:VEVENT SUMMARY:Active Inference and the Evolution of Mind and Society DTSTART:20261113T140000Z DTEND:20261113T150000Z -DTSTAMP:20261027T022753Z +DTSTAMP:20261101T230331Z UID:symposium-2026-session-5ae2ee1184879762 DESCRIPTION:Presenter: Luca Possati END:VEVENT BEGIN:VEVENT -SUMMARY:Session title not yet published +SUMMARY:“Evolution\, Emergence\, Prediction.” DTSTART:20261113T150000Z DTEND:20261113T170000Z -DTSTAMP:20261027T022753Z -UID:symposium-2026-session-dba90c3d26be06bb +DTSTAMP:20261101T230331Z +UID:symposium-2026-session-a5374a2c8e3f94db DESCRIPTION:Presenter: Mike Levin END:VEVENT BEGIN:VEVENT -SUMMARY:Build a curious rocket: the hands-on cpomdp tutorial for the Appli - ed Active Inference Symposium +SUMMARY:Build a curious rocket: a hands-on continuous-state AIF session wi + th cpomdp DTSTART:20261113T170000Z DTEND:20261113T190000Z -DTSTAMP:20261027T022753Z -UID:symposium-2026-session-06ebf625f802e5cc -DESCRIPTION:Presenter: Dan Corva +DTSTAMP:20261101T230331Z +UID:symposium-2026-session-e29c9aff1d3dec95 +DESCRIPTION:Presenter: Dan Corva\, Kevin Backhouse END:VEVENT BEGIN:VEVENT SUMMARY:An in-depth discussion on “World models in natural and artificia - l intelligence + l intelligence” DTSTART:20261113T190000Z DTEND:20261113T203000Z -DTSTAMP:20261027T022753Z -UID:symposium-2026-session-703a83883944eb1d +DTSTAMP:20261101T230331Z +UID:symposium-2026-session-f937cae76e26866e DESCRIPTION:Presenter: Bradly Alicea END:VEVENT BEGIN:VEVENT SUMMARY:Working Groups Session 2 ~ Outcomes & Next Steps DTSTART:20261113T203000Z DTEND:20261113T213000Z -DTSTAMP:20261027T022753Z +DTSTAMP:20261101T230331Z UID:symposium-2026-session-ce8d22b44223b8a3 DESCRIPTION:Presenter: Ed Ober END:VEVENT +BEGIN:VEVENT +SUMMARY:A New Politics: From Linear to Sorted Politics Through the Policy + Map +DTSTART:20261113T213000Z +DTEND:20261113T220000Z +DTSTAMP:20261101T230331Z +UID:symposium-2026-session-319fbfcbb775215e +DESCRIPTION:Presenter: Montijn van Loo +END:VEVENT END:VCALENDAR diff --git a/data/public/2026/program.json b/data/public/2026/program.json index 239ce1d..9569f64 100644 --- a/data/public/2026/program.json +++ b/data/public/2026/program.json @@ -17,6 +17,7 @@ "participant_registration_url": "https://registersymposium2026.activeinference.institute/", "presenter_registration_url": "https://2026present.activeinference.institute/", "program_url": "https://2026symposium.activeinference.institute/", + "snapshot_date_utc": "2026-11-01", "start_date": "2026-11-12", "timezone": "UTC", "title": "6th Applied Active Inference Symposium", @@ -75,6 +76,19 @@ "orcid": "0000-0002-4262-305X", "pragmatic_value": "" }, + { + "active_inference_application": "Industry", + "affiliations": "Jaguar Land Rover", + "background": "I was a clinical academic for 14 years investigating the bidirectional relationship between the CNS and ANS and digital biomarkers in dementia. Been in industry for 4 years as Jaguar Land Rover’s cognitive neuroscientist. Worked with Karl ~10 years ago at UCL in interoceptive inference (Owens AP, Allen M, Ondobaka S, Friston KJ (2018). Interoceptive inference: from computational neuroscience to clinic. Neuroscience and Biobehavioral Rev:174-183; Owens AP, Friston KJ, Low DA, Mathias CJ, Critchley HD (2018). Investigating the relationship between cardiac interoception and autonomic cardiac control using a predictive coding framework. Autonomic Neuroscience: Basic and Clinical;210:65-71).", + "challenges": "Working in this area on my own in an industry setting and trying to find the free time to study how to design my own AI studies", + "epistemic_value": "Designing and analysing AI studies", + "future_impact": "Potential superior use cases, e.g., AI VS LLMs", + "id": "person-318f9db4d9c80273", + "learning_needs": "Resource, tool, or community development, especially practical easy-to-understand guidance on running my own Active Inference (AI) projects in industry, with no day-to-day onsite AI support.", + "name": "Andrew Owens", + "orcid": "0000-0003-1645-0164", + "pragmatic_value": "Practical easy-to-understand guidance on running my own AI projects with minimal support. I’m also interested in linking AI with Dual Process Theory and The Extended Mind Thesis" + }, { "active_inference_application": "", "affiliations": "CIMC", @@ -101,6 +115,58 @@ "orcid": "", "pragmatic_value": "" }, + { + "active_inference_application": "Yes, not sure what a domain stage is.", + "affiliations": "", + "background": "Subjective, anecdotal, no formal education.", + "challenges": "Not having formal education", + "epistemic_value": "Application to a skill set to help Claude manage my second brain.", + "future_impact": "Open source Second brain utilizing free tier llm and active inference decisions through ClickUp and Claude", + "id": "person-c668b17f763bdfbe", + "learning_needs": "Abstracts of each talks in 50,000 words in a txt format", + "name": "Brad Shepherd", + "orcid": "", + "pragmatic_value": "Keep my mouth shut" + }, + { + "active_inference_application": "Just curious about it", + "affiliations": "University of Massachusetts, Boston", + "background": "PhD student in developmental cognitive science", + "challenges": "Understanding the computational/modeling aspects", + "epistemic_value": "Whether and to what extent active inference is relevant for developmental cognitive science", + "future_impact": "Maybe a podcast?", + "id": "person-e7b4cae1566a2b24", + "learning_needs": "Tutorial for beginners/syllabus of required or recommended knowledge (e.g., what math is needed to fully understand and implement)", + "name": "Candice Koolhaas", + "orcid": "0000-0003-4319-8486", + "pragmatic_value": "Recorded talks" + }, + { + "active_inference_application": "", + "affiliations": "", + "background": "", + "challenges": "", + "epistemic_value": "", + "future_impact": "", + "id": "person-c49945494d69395f", + "learning_needs": "", + "name": "Carl Gay", + "orcid": "", + "pragmatic_value": "" + }, + { + "active_inference_application": "", + "affiliations": "University of Virginia", + "background": "", + "challenges": "", + "epistemic_value": "", + "future_impact": "", + "id": "person-21da00c35d3168e6", + "learning_needs": "collaboration", + "name": "Carrie Girstsntas", + "orcid": "", + "pragmatic_value": "" + }, { "active_inference_application": "Information architecture/AI applications", "affiliations": "", @@ -114,6 +180,19 @@ "orcid": "", "pragmatic_value": "a handbook to help me onboard contextually to the topics being discussed" }, + { + "active_inference_application": "", + "affiliations": "", + "background": "", + "challenges": "", + "epistemic_value": "", + "future_impact": "", + "id": "person-c18079de9ece0062", + "learning_needs": "", + "name": "Cleber Gomes", + "orcid": "", + "pragmatic_value": "" + }, { "active_inference_application": "Informally exploring implications for other fields of enquiry. I am considering writing a book exploring issues of epistemology and axiology from an Active Inference perspective.", "affiliations": "", @@ -179,6 +258,19 @@ "orcid": "", "pragmatic_value": "More exposure to mathematics, techniques and applications" }, + { + "active_inference_application": "", + "affiliations": "University of Modena & Reggio Emilia", + "background": "", + "challenges": "", + "epistemic_value": "", + "future_impact": "", + "id": "person-0c22f37c62cc1e49", + "learning_needs": "Tutorials about how to go from the descripción of a system to deciding an AIF model for that system.", + "name": "Giuseppe Pagnoni", + "orcid": "0000-0002-8272-8091", + "pragmatic_value": "" + }, { "active_inference_application": "", "affiliations": "", @@ -231,6 +323,58 @@ "orcid": "", "pragmatic_value": "" }, + { + "active_inference_application": "", + "affiliations": "", + "background": "", + "challenges": "", + "epistemic_value": "", + "future_impact": "", + "id": "person-373763c50c908454", + "learning_needs": "", + "name": "Luke (Alok) Singh", + "orcid": "", + "pragmatic_value": "" + }, + { + "active_inference_application": "", + "affiliations": "", + "background": "", + "challenges": "", + "epistemic_value": "", + "future_impact": "I am writing to you from an entirely unorthodox background, and I wanted to be upfront about that: my early professional path was spent as a go-to-market headhunter for early-stage Silicon Valley startups, before a profound shift pulled me toward psychosomatic health and specialized education. Today, I work as a behavioral aide at a transitional high school for autistic students in Sacramento, spending my early mornings and late nights formalizing theoretical frameworks in biophysics and active inference.\nBecause my resume is unusual in a way that doesnt neatly clear traditional academic hurdles, I have found it difficult to find an entry point into formal research. But I have spent the last year wrestling deeply with a structural blind spot in how we model systemic maintenance specifically, how standard Bayesian mechanics and free energy formulations elegantly recover hidden states, yet remain structurally blind to the absolute housekeeping dissipation required to hold a state against nonconservative forcing without altering steady-state density.\nIn my recent theoretical writing, I explore boundary glycosylation surfaces as a physical Markov blanket where fast lateral rearrangement and slow material turnover provide a frequency-domain window into broken detailed balance and structural maintenance costs.\nGiven your recent work on scale-free active inference and the thermodynamic imperatives of living systems, I wanted to ask your thoughts on whether this framing holds water, or if you happen to know of any labs, postdocs, or groups investigating the thermodynamic costs of boundary maintenance that might be open to an unconventional apprentice willing to do hard, unglamorous legwork.", + "id": "person-63f3f0fdc6d424e0", + "learning_needs": "", + "name": "Matt Wiesenfarth", + "orcid": "", + "pragmatic_value": "" + }, + { + "active_inference_application": "Domain: Sport science and motor development — from children (5–9 years) to elite athletes (national teams, world-class).\nStage: Empirical validation and architecture development.\nEmpirical: AIF inversion of stabilometric and OpenSim data across 10+ sports; longitudinal studies of hierarchical dominance inversion in children.\nArchitecture: hybrid AIF–TOC–TDA framework, three timescales (reflex, body schema, planning), POMDP planner at the top level.\nVerification: Stage G4 — full verification of the hybrid architecture, including elite kinematics and sensitivity analyses.", + "affiliations": "Federal Science Center of Physical Culture and Sport (VNIIFK), Moscow, Russia", + "background": "I am a Professor and Leading Researcher at the Federal Science Center of Physical Culture and Sport (VNIIFK), Moscow, with a background in sports sciences and motor control. My work applies Active Inference to sensorimotor development and elite sport.\nKey publications:\nShestakov, M., Godina, E., Abramova, T., & Korchagin, A. (2026). The effects of different sports activities on body scheme in preschoolers and primary school children: an experimental and theoretical analysis. Journal of Physiological Anthropology, 45(1), 6. https://doi.org/10.1186/s40101-026-00422-0\n— N = 692 children, 3 sports, AIF model of body schema.\nShestakov, M.P., & Abramova, T.F. (2026). Changes in hierarchical dominance in the generative model of movement control in boys aged 7–9. (in press).\n— N = 52 longitudinal, CLPM + AIF metrics, hierarchical inversion under dynamic uncertainty.\nShestakov, M.P. (2023). Perception of sports movements. Taganrog: SFU Press.\n— Monograph on AIF and sport.\nCurrent work: hybrid AIF–TOC–TDA architecture for motor learning across the full range — from children to elite athletes; OpenSim + stabilometry data across 10+ sports, including national teams and world-class performers.", + "challenges": "Three hurdles stand out:\n(1) The gap between theory and empirical validation. AIF is mathematically elegant, but empirical validation in real-world motor tasks is still sparse. Our work is one attempt to close this gap — but the field needs many more.\n(2) Computational scalability. POMDP planning with continuous state spaces remains expensive. For applied work — sport, rehabilitation, robotics — we need solvers that run in reasonable time on reasonable hardware.\n(3) Standardization of metrics. Different groups use different metrics (PPE, precision, free energy). Without shared benchmarks, comparison across studies is difficult. This is especially acute in applied domains like sport science.", + "epistemic_value": "Three topics:\n(1) Structural learning in AIF. How do others formalize changes in the generative model — not just parameter updates, but changes in structure? Our work on sensitive periods and growth spurts touches this, but we want to learn from other formalizations.\n(2) Topological methods in AIF. TDA is still rare in AIF. We use persistent homology to extract invariant motor signatures, and we are curious how others approach invariants and \"Platonic\" levels.\n(3) Precision and heterochrony. How do others model the asynchronous maturation of sensory channels? Our data show it matters — but we want to see alternative approaches.", + "future_impact": "(1) Personalized motor rehabilitation. AIF-based models make it possible to identify sensitive periods and the specific movement characteristics of individual patients. This allows for the development of rehabilitation protocols tailored to a person’s unique traits, rather than relying on population averages.\n(2) Elite sports as a testing ground. World-class athletes push the human motor system to its absolute limits. AIF models, validated using the kinematics of elite athletes, can reveal what distinguishes the good from the great and assist in designing training programs at the highest level.\n(3) Skill transfer from humans to robots—including robots created (or \"grown\") from biological material. The same architecture used to model how a child learns complex movements can be applied to teaching robots how to move; this has significant implications for prosthetics, exoskeletons, and collaborative robotics.\n(4) Space exploration and long-term stays in extraterrestrial environments—potentially even the birth of humans off-Earth—will likely lead to shifts in consciousness, given the intrinsic link between consciousness and motor activity. This necessitates large-scale forecasting, involving the simulation of various environmental conditions and their impact on human development.", + "id": "person-64e4618748001f98", + "learning_needs": "Three things would help most:\n(1) Standardized empirical benchmarks for AIF in motor control. Our field lacks shared datasets and metrics — we have built our own (stabilometry + OpenSim across 10+ sports), but a community benchmark would accelerate comparison across models.\n(2) Scalable POMDP solvers for continuous state spaces. Our Level-3 planner is the computational bottleneck. A community-maintained library (or a shared recipe for point-based methods at scale) would lower the barrier for applied researchers.\n(3) A community of practice for AIF in sport and rehabilitation. Most AIF work is in neuroscience or robotics; applied sport science is a natural but underserved domain. Regular exchanges (workshops, shared code) would help bridge the gap.", + "name": "Mikhail P. Shestakov", + "orcid": "", + "pragmatic_value": "Direct feedback from the AIF community on two things:\n(1) Validation strategy. We have empirical data across the full range — from 5-year-olds to world-class athletes — but we want the community's input on how to validate a hybrid AIF–TOC–TDA architecture against elite kinematics.\n(2) Collaboration. We are looking for partners with expertise in POMDP scalability, topological data analysis, or pediatric rehabilitation to extend the framework beyond our current scope." + }, + { + "active_inference_application": "All philosophical domains.", + "affiliations": "Tilburg University", + "background": "I am an econometrist gone rogue. Instead of throwing mathematics at finance, I throw Active Inference mathematics at philosophy. See for the consciousness, morality, politics, gender, free will and God applications, with the poem uniting them all.", + "challenges": "", + "epistemic_value": "", + "future_impact": "A New Politics!", + "id": "person-0be473093b66db0b", + "learning_needs": "", + "name": "Montijn van Loo", + "orcid": "0009-0003-0249-3240", + "pragmatic_value": "" + }, { "active_inference_application": "My grasp of enactive Inference provides a framework for understanding embodied cognition. It helps to explain the relation of mind brain and body at a granular level as I work as a somatic therapist, teacher, and scholar seeking to understand the ancient pursuit of a physics of life. In particular the perceptual dimension of the framework is quite helpful. A fully developed physics of life involves the mechanics of physical movement as a felt, intuitive activity. We should be able to name this clearly, in kinesiological terms—as a matter of common sense. Can you? Understanding the mechanics and their historical context in the way Professor Friston suggests is very helpful. The active inference framework provides needed first principles but lacks an understanding of motor plant. This is a major problem. For this reason it remains limited to a computational approach. Professor Friston and the general consensus seem only to grasp a mental form of embodiment and enactive inference. I am seeking to point out that major lacunae and offer the simple anatomy as we understand it today for discussion and afferent self confirmation.", "affiliations": "Independent scholar/practitioner", @@ -244,6 +388,32 @@ "orcid": "", "pragmatic_value": "I would like direct feedback on my embodied anatomy approach to enactive inference and its significance for cerebellar precision tracking and alignment. I would like to see the not inconsiderable wisdom of this group directed at the real problems of life and a genuine, frank discussion of active inference that is not about computation, computers and technology." }, + { + "active_inference_application": "", + "affiliations": "Neurethic-Lab (ICL-Lille-France)", + "background": "Cognitive Neuroscience", + "challenges": "", + "epistemic_value": "", + "future_impact": "", + "id": "person-9f1eef64030ad3eb", + "learning_needs": "", + "name": "Olivier CAPRA", + "orcid": "0000-0002-1812-6061", + "pragmatic_value": "" + }, + { + "active_inference_application": "", + "affiliations": "University of Fribourg", + "background": "", + "challenges": "", + "epistemic_value": "", + "future_impact": "", + "id": "person-83708fa21b89d9d5", + "learning_needs": "", + "name": "Parsa Yousefi", + "orcid": "", + "pragmatic_value": "" + }, { "active_inference_application": "", "affiliations": "Shanghai University of Sports", @@ -361,6 +531,19 @@ "orcid": "0000-0002-4415-5448", "pragmatic_value": "Theory on converting local models to Active Inference; specifically mean field functional connectomes, incorporating hierarchical inputs/outputs" }, + { + "active_inference_application": "early-mid stage; developing a code-free workbench for agent/env creation for any domain, I hope", + "affiliations": "Junior Consultant at Vrioni Consulting", + "background": "physics, cognition, computation, art — my vibe tends to be more of a generalist, although music is really what I really peak at in terms of “performance”. I see it as “engineering consciousness”, but in the first person pov… and it’s not about creating signal: it’s about filtering noise. Dances of symbols all along. So the work is internal so far, but it is trying to find a way out. I am also working on a few papers though, about physical agents (involving Chladni patterns), making physics with stamps (involving dynamic causal modelling and “ignorance modelling”), consciousness (involving implications of Noether’s theorem for electromagnetic waves), and philosophy (translating Bertrand Russell’s Nobel Lecture’s lessons into active inference exercises — his speech is on Youtube, you’ll love it). It’s private and solo-work so far… slowly but surely.", + "challenges": "I’ll take the side of those that never heard of it, or only distantly: it’s about its concepts, intuitions on non-realism, and the code barrier. l", + "epistemic_value": "seeing real-world applications, meeting people, exchanging ideas", + "future_impact": "This really applies to every year to come: demonstrating new ways of doing physics, engineering, biology, cognition… Also, if we could demonstrate that we can make AI that has 0% chance of mass-extinction, by design, that would put the AI giants in their place, and shift attention to this wonderful community.", + "id": "person-7e6156e5c03e16f9", + "learning_needs": "Community, art, and time above all…", + "name": "Ted J. Vrioni", + "orcid": "", + "pragmatic_value": "making contacts, finding support" + }, { "active_inference_application": "In intelligence layers", "affiliations": "", @@ -400,6 +583,19 @@ "orcid": "", "pragmatic_value": "Recent works/introductory essays by the presenters, openly available to attendees" }, + { + "active_inference_application": "Domain: industrial AI orchestration and physical AI, with cognitive security as my priority topic. Stage: research and pre-standardization. I model how AI orchestration shifts the precision weights of human experts, and I am writing the link between systems-safety analysis and Active Inference into an ITU-T technical report. There is no industrial deployment yet. The next step is to test whether a GNN-style specification can describe a real orchestration problem, with people who can run it.", + "affiliations": "Japan Advanced Institute of Science and Technology, PhD Student", + "background": "I work on industrial AI orchestration on the basis of the Free Energy Principle (FEP), from two sides: research and standards.\nResearch: I am a doctoral student working on FEP as a foundation for next-generation AI infrastructure. My recent paper “How Trust in AI Erodes Expert Judgement”, built on Active Inference, is accepted at the International Conference on Information Management, Information Technology and Artificial Intelligence 2026. \nStandards: As an standardization expert mainly at ITU-T, I’m currently working for multiple technical reports on embodied AI and on AI-enabled cities. One of the work connects the process model of the Systems-Theoretic Accident Model and Processes, a controller's belief about what it controls, with the generative model of Active Inference. I also serve as an expert in ISO/IEC committees on smart manufacturing and smart communities.", + "challenges": "The theory has run ahead of the route to use it. An engineer who must specify an agent's beliefs, preferences and constraints in a way that a customer, an auditor or a regulator can check has no shared notation, no agreed vocabulary, and few like-for-like comparisons with reinforcement learning or rule-based control on realistic tasks. The hardest gap is assurance: there is no accepted way to show that an agent's beliefs still track the actual situation, which is what a safety case needs. And the neuroscience vocabulary keeps many engineers from reading the literature at all.", + "epistemic_value": "I would like to understand how precision gets set in systems that combine people and AI agents: when does an agent's confidence in its own model stop tracking reality, and what do people who build safety cases look for before it does? I am also curious how different groups handle constraints, as prior preferences inside the generative model or as guarantees kept outside it. An international standard has to say which, and I would like to hear the arguments from people who have tried both.", + "future_impact": "In 2027, Active Inference could become the first theory of intelligence that an engineer, an auditor and a regulator can read from the same page. For that, a model has to be specified once and run by many tools, as GNN already points toward. One real testbed is needed, such as a production line or robot fleet orchestrated by agents whose beliefs and constraints can be inspected, run against a conventional baseline, with the failures published. And precision has to become something we can monitor, meaning the weight a system gives its own predictions against a person's input, so that human experts are not slowly overruled by systems that are usually right. Japan's AI robotics strategy targets about 10 million robots by 2040. If they keep learning while they act, someone must be able to say what they are allowed to believe.", + "id": "person-2d90b7351589fc79", + "learning_needs": "Three things would help most. First, open worked examples from industry (control, scheduling, robot fleets) with code and a stated comparison against reinforcement-learning and rule-based baselines. Second, a generative-model specification that several toolchains can execute, so that a result in pymdp can be reproduced in RxInfer.jl without rewriting it. Generalized Notation Notation (GNN) is the natural candidate, and its maintenance and versioning should be settled. Third, a place where people who write standards and regulation can ask practising researchers, in engineering terms, what the theory claims and what it does not.", + "name": "Yukiko NAKATSU", + "orcid": "", + "pragmatic_value": "I would like to meet three kinds of people: those who have run Active Inference outside the lab, in robots, vehicles, drones or factories, and will say what broke; GNN users who can tell me which parts are stable enough to propose to a standards body; and others working on how human–AI systems should be monitored so that experts' judgement is not slowly eroded." + }, { "active_inference_application": "I am starting with simple signal detection type task in vestibular neuroscience. The work was part of my PhD, which I recently completed.", "affiliations": "Center for vestibular neurology, Imperial College London", @@ -432,24 +628,24 @@ "title": "An in-depth discussion on “World models in natural and artificial intelligence”" }, { - "abstract": "In the 1960s, Rudolf Kalman wrote down a filter that would go on to navigate Apollo to the moon. It has been the workhorse of continuous-state estimation ever since, and if you have ever built a continuous active inference agent in the linear-Gaussian regime, you have built one too, whether you intended to or not. Bolt a goal onto it and you get a very good controller that is not curious in the slightest. Koudahl, Kouw and de Vries proved why in 2021: under a fixed sensor the epistemic value is identical for every action, so expected free energy quietly reduces to Linear Quadratic Regulator (LQR). \nIn this workshop, in about ten lines of Python that will look familiar to anyone who has used pymdp, we are going to build a rocket! Then, we add the interesting part, curiosity, by making a surgical, biologically realistic change: adding sensor noise that varies based on the rocket's position. We will see how the flight path bends when we add artifacts like a navigation beacon or a blackout zone, and explore the science as to why. You will write every line yourself in a Colab notebook and do not worry there will be plenty of scaffolding from the cpomdp team. Code hints will drop in on a timer, and a reference cell for anyone who gets stuck or would rather just see the finished steps. \nWe will build this in three steps: the 1960 rocket, a nav beacon it can fix its position by, and a blackout zone. Everything we use runs on the PyPI release of cpomdp and anything you build in the notebook is yours to keep and continue working on.\nPrerequisites: comfortable editing a few lines of Python. Familiarity with pymdp helps but is not required. Nothing to install; a browser and a google account is enough. If you would like to use your own IDE feel free but we may not be able to provide 1-on-1 support if you get stuck.", - "abstract_sha256": "8d2e1b9353c6c0730a9a5fd5a253822fbfc29c8147f8ee62cb7bdd1916f01896", + "abstract": "In the 1960s, Rudolf Kalman wrote down a filter that would go on to navigate Apollo to the moon. It has been the workhorse of continuous-state estimation ever since, and if you have ever built a continuous active inference agent in the linear-Gaussian regime, you have built one too, whether you intended to or not. Bolt a goal onto it and you get a very good controller that is not curious in the slightest. Koudahl, Kouw and de Vries proved why in 2021: under a fixed sensor the epistemic value is identical for every action, so expected free energy quietly reduces to Linear Quadratic Regulator (LQR).\nIn this workshop, in about ten lines of Python that will look familiar to anyone who has used pymdp, we are going to build a rocket! Then, we add the interesting part, curiosity, by making a surgical, biologically realistic change: adding sensor noise that varies based on the rocket's position. We will see how the flight path bends when we add artifacts like a navigation beacon or a blackout zone, and explore the science as to why. You will write every line yourself in a Colab notebook and do not worry there will be plenty of scaffolding from the cpomdp team. Code hints will drop in on a timer, and a reference cell for anyone who gets stuck or would rather just see the finished steps.\nWe will build this in three steps: the 1960 rocket, a nav beacon it can fix its position by, and a blackout zone. Everything we use runs on the PyPI release of cpomdp and anything you build in the notebook is yours to keep and continue working on.\nPrerequisites: comfortable editing a few lines of Python. Familiarity with pymdp helps but is not required. Nothing to install; a browser and a google account is enough. If you would like to use your own IDE feel free but we may not be able to provide 1-on-1 support if you get stuck.", + "abstract_sha256": "d931a84a8cc4febe9f64cc6ac8033382dc79d8c1ff49314e54da5f5db1ed0fd3", "contribution_type": "Interactive Workshop", - "id": "talk-80262f867d75698a", + "id": "talk-79ba0396518881ec", "pictures": [ { - "alt": "Dan Corva — submitted presenter image", + "alt": "Dan Corva, Kevin Backhouse — submitted presenter image", "path": "assets/portraits/e35c8b45b0dbeb062a12698857e5107aff5c381edcbfac345953f1695bf6ade4.jpg", "sha256": "e35c8b45b0dbeb062a12698857e5107aff5c381edcbfac345953f1695bf6ade4" } ], - "presenter": "Dan Corva", + "presenter": "Dan Corva, Kevin Backhouse", "source_url": "https://2026symposium.activeinference.institute/", - "title": "Build a curious rocket: the hands-on cpomdp tutorial for the Applied Active Inference Symposium" + "title": "Build a curious rocket: a hands-on continuous-state AIF session with cpomdp" }, { - "abstract": "Most agent architectures collapse two things that phenomenology keeps apart: what the agent should do immediately, and what kind of world the agent takes itself to be in. This session introduces “perspective” as a distinct architectural layer that carries the second. \nPerspective is a slowly evolving, history-sensitive latent state that gates how observations are encoded. Importantly, it is decoupled from policy gradients and is never itself an optimization target. Because it is decoupled, it is portable and can be attached on top of an existing policy-optimizing agent rather than requiring a bespoke design.\nThe first part of the session develops the conceptual frame: how a phenomenologically motivated condition translates into a concrete architectural commitment, and why that commitment is orthogonal to belief-state inference and policy selection. I then trace the capabilities that this single layer has been shown to support across a sequence of studies, including directional hysteresis, history-dependent perceptual reorganization, and body-grounded conative orientation, treating these as unified aspects of a single portable module.\nThere will then be a live demonstration attaching the perspective layer to an active inference agent, such as a hierarchical Bayesian / discrete state-space model, and running it in parallel with its unaugmented counterpart in the same environment, so participants can see exactly what the layer adds and where it plugs in.\nThe session closes with a structured discussion in which participants are invited to identify candidate insertion points for a perspective layer in their own active inference agents, whether built in PyMDP, RxInfer, or custom frameworks. The aim is to leave participants able to reason about whether and how a subjective perspective could be added.", - "abstract_sha256": "52989bb421dddd9c78c2b4a2d77e98d7e180d842e16e392fbfad17a36a1a7413", + "abstract": "Most agent architectures collapse two things that phenomenology keeps apart: what the agent should do immediately, and what kind of world the agent takes itself to be in. This session introduces “perspective” as a distinct architectural layer that carries the second.\nPerspective is a slowly evolving, history-sensitive latent state that gates how observations are encoded. Importantly, it is decoupled from policy gradients and is never itself an optimization target. Because it is decoupled, it is portable and can be attached on top of an existing policy-optimizing agent rather than requiring a bespoke design.\nThe first part of the session develops the conceptual frame: how a phenomenologically motivated condition translates into a concrete architectural commitment, and why that commitment is orthogonal to belief-state inference and policy selection. I then trace the capabilities that this single layer has been shown to support across a sequence of studies, including directional hysteresis, history-dependent perceptual reorganization, and body-grounded conative orientation, treating these as unified aspects of a single portable module.\nThere will then be a live demonstration attaching the perspective layer to an active inference agent, such as a hierarchical Bayesian / discrete state-space model, and running it in parallel with its unaugmented counterpart in the same environment, so participants can see exactly what the layer adds and where it plugs in.\nThe session closes with a structured discussion in which participants are invited to identify candidate insertion points for a perspective layer in their own active inference agents, whether built in PyMDP, RxInfer, or custom frameworks. The aim is to leave participants able to reason about whether and how a subjective perspective could be added.", + "abstract_sha256": "69a7502ac6873d79a8117eb33edb8a4cbda4e779c9dd8b1db7634094809096b3", "contribution_type": "Interactive Workshop", "id": "talk-7bfba1b90b06334d", "pictures": [ @@ -480,14 +676,14 @@ "title": "Active Inference and Free Energy Principle in 2026" }, { - "abstract": " \nCognitive scientists have applied advances in our formal understanding of causation in philosophy and computer science, particularly within the Causal Bayes Net formalism, to understand human causal learning. This formalism is also closely related to ideas in active inference. In parallel, reinforcement learning researchers have developed the idea of an intrinsic reward signal called “empowerment”. An agent is rewarded for maximizing the mutual information between its actions and their outcomes, regardless of the external reward value of those outcomes. “Empowerment” may be an important bridge between classical Bayesian causal learning and reinforcement learning and may help to characterize causal learning in humans and enable it in machines ", - "abstract_sha256": "72888682b1f2ff64f4e83e33530897248d8aedd1c4d529bcdf99d3009e1065a1", + "abstract": "Cognitive scientists have applied advances in our formal understanding of causation in philosophy and computer science, particularly within the Causal Bayes Net formalism, to understand human causal learning. This formalism is also closely related to ideas in active inference. In parallel, reinforcement learning researchers have developed the idea of an intrinsic reward signal called “empowerment”. An agent is rewarded for maximizing the mutual information between its actions and their outcomes, regardless of the external reward value of those outcomes. “Empowerment” may be an important bridge between classical Bayesian causal learning and reinforcement learning and may help to characterize causal learning in humans and enable it in machines", + "abstract_sha256": "b15da3a9e306bf10b00e79eb1908884fcfcf0d93f097cf4ea2bef516e9a18271", "contribution_type": "Livestreamed panel or group session", - "id": "talk-2f88a4d50509b911", + "id": "talk-2cb75f3d60e44286", "pictures": [], "presenter": "Alison Gopnik", "source_url": "https://2026symposium.activeinference.institute/", - "title": "Actve Inference, Bayesian Causal Learning and Empowerment in RL." + "title": "Active Inference, Bayesian Causal Learning and Empowerment in RL." }, { "abstract": "How do we keep a coherent sense of self across time — and can that process be measured in real time, on consumer hardware, before it breaks down?\nRoot Frequency Theory (RFT) proposes that experiential continuity is not a default state but an ongoing biological achievement requiring coordination across five nested regulatory layers: environmental constraints (C0), autopoietic regulation (C1), predictive neural integration (C2), narrative self-organization (C3), and the continuity of lived experience (C4). Fragmentation is defined as a self-reinforcing cascade of cross-scale misalignment. The cascade is bidirectional: structured self-knowledge introduced at C3 — operationalized as epistemic alignment (Kₑ) — is hypothesized to initiate upward regulatory stabilization.\nTo test this, I developed the M-RFT metric: M = (Φ · Kₑ) / F, where Φ indexes autonomic flexibility via HRV, Kₑ composites interoceptive accuracy and narrative coherence, and F captures regulatory burden. I then built Root Extension — an iOS application using iPhone and Apple Watch — to administer this protocol ecologically and deliver personalized epistemic scaffolding upon session completion.\nThe N=1 longitudinal study ran 321 sessions across 23 active days (April 16–May 14, 2026), with the researcher as the sole subject. Key findings: mean M = 3.79 (Mdn = 2.14), intra-session regulatory recovery — FRAGMENTED to RESILIENT within the same calendar day — observed on 34.8% of active days.\nThis presentation introduces the theoretical architecture, the metric, the app protocol, and the preliminary data — and invites the active inference community into the next phase: a formal N=20–40 validation trial planned for 2027.", @@ -500,14 +696,30 @@ "title": "Root Frequency Theory: An Integrative Framework for the Continuity of Lived Experience" }, { - "abstract": "AII proposes to facilitate / host active inference working groups as explained in our paper “Coordinating the Field”, with a field group and one or more domain groups. The first symposium session will introduce the concept, recap the paper and reason for the groups and solicit suggested domains and participants. The second session will review the proposed charter and consider amendments, with versioning per group, considering nominations for chair(s) and secretary(ies), and a goal of adopting the charter(s) during the symposium if possible and establish post-symposium meeting cadence and work plan. The third session will report back to the main session recap what was accomplished during the symposium and announce any working groups formed and their first year goals established.", - "abstract_sha256": "31a335e107dfddefde2d3a94c2abb6796d6f3864ddaf9aa70815a9791ff5406e", + "abstract": "AII proposes to facilitate / host active inference working groups as explained in our paper “Coordinating the Field”, with a field group and one or more domain groups. The first symposium session (Opening & Charter, November 12) will introduce the concept, recap the paper and reason for the groups, and solicit suggested domains and participants. It will also review the proposed charter and consider amendments, with versioning per group, considering nominations for chair(s) and secretary(ies), with a goal of adopting the charter(s) during the symposium if possible and establishing post-symposium meeting cadence and work plan. The second session (Outcomes & Next Steps, November 13) will report back to the main session, recap what was accomplished during the symposium and announce any working groups formed and their first year goals established.", + "abstract_sha256": "3a7015d9ea0c9ea278f5e6b301fc762f96b17e592e3e91d4fbd295641ee4cfb6", "contribution_type": "Livestreamed panel or group session", - "id": "talk-bb62d4494869e307", + "id": "talk-98b78d49f79fcb14", "pictures": [], "presenter": "Ed Ober", "source_url": "https://2026symposium.activeinference.institute/", - "title": "Working Groups Sessions (3 sessions: Opening, Charter Session, Outcomes and Next Steps)" + "title": "Working Groups Sessions (2 sessions: Opening & Charter; Outcomes & Next Steps)" + }, + { + "abstract": "People naturally express their beliefs about a topic, situation, or problem in the form of narrative. CogNarr is a soon-to-be-released, open-source, interactive tool that lets a writer transform narrative into an explicit, formal semantic graph, which they can inspect and correct until it faithfully reflects their belief model. A CogNarr story graph captures a narrative's argument structure, its timing, and the modality of its claims, including predictions and conditionals, together with the uncertainty attached to each. In general it expresses the verifiable meaning of what a person wishes to say, while reducing the ambiguity of natural language. As such, it could be of use in modeling the beliefs that active-inference processes operate over, and as a framework within which active-inference methods could be employed to infer semantic meaning from text. Because it makes a person's beliefs explicit, transparent, and shareable, CogNarr has potential wherever consequential beliefs are expressed as narrative, from medicine and governance to public polling and accident reporting. As well, CogNarr story graphs support accurate agent-agent communication. The collective-cognition aim behind the project is alignment of shared belief models during decision-making processes in the large-group setting. The talk will be a preview of features and capabilities, with some discussion of potential applications.", + "abstract_sha256": "bd1c5a829a23b0d6596a0afaec580fc1b0cccccbc5d90c313603267ed871417f", + "contribution_type": "Livestreamed panel or group session", + "id": "talk-414af3e53e96ece8", + "pictures": [ + { + "alt": "John Boik, Glen EP Ropella — submitted presenter image", + "path": "assets/portraits/d34438ab1777fde320741120ab7b2faf918f2c07de10cca079dc510e1ea68276.jpg", + "sha256": "d34438ab1777fde320741120ab7b2faf918f2c07de10cca079dc510e1ea68276" + } + ], + "presenter": "John Boik, Glen EP Ropella", + "source_url": "https://2026symposium.activeinference.institute/", + "title": "CogNarr Story Graph: A Formal Semantics for Narrative as a Belief Model" }, { "abstract": "This panel explores how active inference and the free-energy principle can contribute to a unified account of psychological and social evolution. It examines how cognition, affect, social interaction, cultural practices, and institutions emerge through processes of prediction, adaptation, learning, and uncertainty reduction. By bringing together perspectives from cognitive science, philosophy, anthropology, and social theory, the panel asks how individual and collective forms of agency co-evolve and how shared models of the world shape both minds and societies.", @@ -545,19 +757,51 @@ "source_url": "https://2026symposium.activeinference.institute/", "title": "“Evolution, Emergence, Prediction.”" }, + { + "abstract": "Political disagreement often reflects competing worldviews: different beliefs about how the world works and what will happen under alternative policies. Yet political debate proceeds linearly, through proposals, responses, and counterresponses. The underlying reasoning and evidence remain fragmented, assumptions can escape scrutiny, and criticism loses its target as debate moves on. Consequently, incompatible worldviews can persist without their disagreements being resolved.\nThis talk proposes a new politics, motivated by Active Inference, which provides the mathematical object around which political debate can be organized: the policy map. This map expresses a worldview through predicted outcomes under alternative policies. Making it the persistent object of debate allows criticism to retain a stable address rather than disappear in the rhetoric of successive exchanges.\nThe mathematics of the policy map distinguishes three types of criticism: belief criticism, challenging beliefs about current states or the consequences of policies; outcome incompleteness, identifying omitted consequences; and policy incompleteness, identifying missing alternatives. These provide the horizontal sorting of political debate.\nVertical sorting organizes the reasoning beneath each prediction, allowing criticism to reach its underlying assumptions and evidence. Unresolved objections remain visible until answered or incorporated through revision, potentially changing the predicted outcomes and preferred policies.\nIn this way, Active Inference offers a normative application that goes beyond describing politics as if political actors performed Bayesian inference. It motivates transforming the very structure of political debate, replacing rhetorical exchanges with cumulative criticism and incentivizing a shared search for the worldview that best fits the available evidence.\nThe ultimate aim is a politics in which criticism can actually bite, transforming not only political debates, but the world shaped by its decisions. For the only path forward is critique without exception.", + "abstract_sha256": "b76d74006fd64c15a12c8d920751256bd858769154f576a9d2af9a5f2d41c6ed", + "contribution_type": "Livestreamed panel or group session", + "id": "talk-f86214f68688f98b", + "pictures": [ + { + "alt": "Montijn van Loo — submitted presenter image", + "path": "assets/portraits/9038b884a77a3a6ce8a0ede72312187c3b0dcd96bfc61138aaa59b54e8547a1f.jpg", + "sha256": "9038b884a77a3a6ce8a0ede72312187c3b0dcd96bfc61138aaa59b54e8547a1f" + } + ], + "presenter": "Montijn van Loo", + "source_url": "https://2026symposium.activeinference.institute/", + "title": "A New Politics: From Linear to Sorted Politics Through the Policy Map" + }, + { + "abstract": "Consciousness: The Channel Framework and its Application to INTREPID\nThe Channel Framework treats theories of consciousness as relating physical and experiential events, with the minimal relation being predictive. The Channel Framework embeds this psychophysical relation within the full experimental prediction architecture, connecting physical processes, conscious content, introspective access, and observable outcomes. Expressed through probabilistic channels, these architectures can be interpreted as generative models of experimental predictions, providing a common language in which different theories can be explicated and compared.\nI apply the framework to INTREPID’s adversarial comparison of Integrated Information Theory and predictive-processing theories of consciousness, including Neurorepresentationalism and the Active Inference theory of consciousness (AI-C). The framework asks whether their prediction architectures can be faithfully aligned, which theoretical differences actually generate their contrasting experimental predictions, and whether these differences carry the particular claims the experiments aim to test.\nBuilding on this analysis, the Channel Standard develops criteria for attributing evidence from competing theory predictions to specific theoretical claims. The broader aim is to move beyond theory-level comparison toward methodological concensus.", + "abstract_sha256": "74e8299085acd15f5c631e629533eb94c3c284d07b3c37cfaa7aa283bde832db", + "contribution_type": "Livestreamed panel or group session", + "id": "talk-2da4ceb8dad3a468", + "pictures": [ + { + "alt": "Montijn van Loo — submitted presenter image", + "path": "assets/portraits/9038b884a77a3a6ce8a0ede72312187c3b0dcd96bfc61138aaa59b54e8547a1f.jpg", + "sha256": "9038b884a77a3a6ce8a0ede72312187c3b0dcd96bfc61138aaa59b54e8547a1f" + } + ], + "presenter": "Montijn van Loo", + "source_url": "https://2026symposium.activeinference.institute/", + "title": "Explicating and Comparing Prediction Architectures of Theories of Consciousness and its Application to INTREPID" + }, { "abstract": "In this talk, I will review recent studies and results using active inference and related modeling approaches to study mechanisms of normative cognitive-affective processes and associated neural responses, and how these appear to be affected in different psychiatric conditions. I will also discuss contexts in which active inference appears to offer potentially unique resources for modeling these processes. Conversely, I will discuss contexts in which active inference may overlap with simpler modeling approaches and related issues centered around the level of mechanistic description one is interested in studying.", "abstract_sha256": "3b307267b85c8a2fb7d1233df102568d7846f1a71221047740ab1fa647f4b999", "contribution_type": "Livestreamed panel or group session", - "id": "talk-6b718c5ddd797287", + "id": "talk-ddaf5c37722dfa85", "pictures": [ { - "alt": "Ryan Smith — submitted presenter image", + "alt": "Ryan Smith — submitted presenter image", "path": "assets/portraits/a406432d124c6ab0decd918dece7f9114a65aa3d86da9f3ce2b9f23c4d82162a.jpg", "sha256": "a406432d124c6ab0decd918dece7f9114a65aa3d86da9f3ce2b9f23c4d82162a" } ], - "presenter": "Ryan Smith ", + "presenter": "Ryan Smith", "source_url": "https://2026symposium.activeinference.institute/", "title": "Recent advances in empirical applications of active inference in computational psychiatry" }, @@ -587,6 +831,22 @@ "source_url": "https://2026symposium.activeinference.institute/", "title": "Reverse engineering generative models from zebrafish decision-making" }, + { + "abstract": "If pymdp was an engine, Knowthyself is an attempt to give it a chassis and wheels. The whole point is to promote a workbench to build your active inference agent and environments without writing a single line of code. It’s beginner- and expert-friendly, fostering education, research and development, and maybe the idea of a “hub of digital twins” for currently existing literature. It comes in progressive levels of “difficulty” which unlock various tools and functionalities. You become a particular chemist, authoring all sort of “herds of strange things”.", + "abstract_sha256": "87457746f4b47009112acf66f14942725b9434b766908dbeb206642c35a59ecb", + "contribution_type": "Livestreamed panel or group session", + "id": "talk-97efa98e1c62e734", + "pictures": [ + { + "alt": "Ted J. Vrioni — submitted presenter image", + "path": "assets/portraits/d48f25626b262faabf80a8c7ad3fd152e2376450bf757e36d39c29afc3639e84.jpg", + "sha256": "d48f25626b262faabf80a8c7ad3fd152e2376450bf757e36d39c29afc3639e84" + } + ], + "presenter": "Ted J. Vrioni", + "source_url": "https://2026symposium.activeinference.institute/", + "title": "Knowthyself — A code-free active inference workbench" + }, { "abstract": "Lexical‑disambiguation tasks with supplied context do not reveal whether readers would choose to seek evidence before deciding. Therefore, we adapted the spatial T‑maze in the active‑inference package pymdp to show how agents seek and use contextual information under lexical uncertainty. Agents could choose between two readings or request a contextual hint. We performed two main sets of simulations. First, deterministic simulations used fully informative hints to reproduce context seeking followed by lexical commitment. A hint shifted belief over two readings from equal probabilities (0.50, 0.50) to certainty (1.00, 0.00), reducing entropy by one bit. Second, lexical decision trials were repeatedwith informativeanduninformative hints across 32 paired simulationruns. Agents learned the relationship between the intended reading and the observed hint. Informative experience increased both the probability and simulated frequency of requesting context. This increase persisted when trials ended after one response and feedback. A hint information‑gain ablation, in which policies were rescored without crediting the information expected from hints, eliminated the learning‑related increase in hint‑request probability. These simulations demonstrate how experience changes the expected informational value of context within a lexical task, providing a computational proof of concept for subsequent behavioral experiments.", "abstract_sha256": "cba3606cc6e2fe01a7544b42973a27aa0950f042661362b119bfee12223bc968", @@ -604,8 +864,24 @@ "title": "A Lexical T‑Maze for Active Inference: A Proof‑of‑Concept Model of Context Seeking Under Ambiguity" }, { - "abstract": "This Symposium 2026 tutorial covers Active Inference from various angles based on the experience over several years with teaching and applying Active Inference. It begins with a brief descriptive rehearsal of fundamental ideas and principles akin to other previous tutorials to establish a foundation for the audience. However, this will be followed by a tour through various textbook materials and publications, past and present, to emphasize crucial areas for understanding modelling and application areas. This tutorial will include visual and discursive illustrations of a range of model classes for different use-cases; discussions of existing and under-development software libraries; and some frontiers in modelling for identifying directions for further research applications which can be especially valuable for the broader research community in 2026 onward. Topics will include, non-exhaustively, the Bayesian brain hypothesis and behavioralism versus partial observability and modelling internal states; principles of exploration, learning, and novelty; fundamental discrete and continuous models in Active Inference and their relationship; computational psychiatry and its significance to other domains; the FEP and its historical and practical relationship to Bayesian model comparison; software libraries including SPM, pymdp, RxInfer, RGMs, and other developments; hierarchical and hybrid models; and structure learning. ", - "abstract_sha256": "7ab6bfb3626aecfdc813fffcdcacac28ff84fdf2486f0001a4d022c5ef910895", + "abstract": "Winnicott held that the infant’s transitional object must never be questioned - did you create it or find it? The paradox is to be held, not resolved, because play, creativity and culture live in the space it opens. This talk argues that active inference carries the same paradox. Perception is jointly authored by the generative model and the evidence, and action shapes the world the agent then finds. I draw thinking from the pioneers of early childhood education, philosophy of time and contemporary discussions. I explore some possible consequences, treating the unresolved paradox as a design principle.", + "abstract_sha256": "0171e69c3ca2e83399ea58689ef86fad953c5821cdc1cd265d5248506e2f6bc3", + "contribution_type": "Pre-recorded Video presentation", + "id": "talk-34c1d546c1122ef8", + "pictures": [ + { + "alt": "Alexander Sabine — submitted presenter image", + "path": "assets/portraits/39ea42c3ae9a04aad471cf97827cd691cf2a02175bd1e50dde14c9c976f71ada.jpg", + "sha256": "39ea42c3ae9a04aad471cf97827cd691cf2a02175bd1e50dde14c9c976f71ada" + } + ], + "presenter": "Alexander Sabine", + "source_url": "https://2026symposium.activeinference.institute/", + "title": "FEP and Active Inference through the lens of Winnicott’s Potential Space" + }, + { + "abstract": "This Symposium 2026 tutorial covers Active Inference from various angles based on the experience over several years with teaching and applying Active Inference. It begins with a brief descriptive rehearsal of fundamental ideas and principles akin to other previous tutorials to establish a foundation for the audience. However, this will be followed by a tour through various textbook materials and publications, past and present, to emphasize crucial areas for understanding modelling and application areas. This tutorial will include visual and discursive illustrations of a range of model classes for different use-cases; discussions of existing and under-development software libraries; and some frontiers in modelling for identifying directions for further research applications which can be especially valuable for the broader research community in 2026 onward. Topics will include, non-exhaustively, the Bayesian brain hypothesis and behavioralism versus partial observability and modelling internal states; principles of exploration, learning, and novelty; fundamental discrete and continuous models in Active Inference and their relationship; computational psychiatry and its significance to other domains; the FEP and its historical and practical relationship to Bayesian model comparison; software libraries including SPM, pymdp, RxInfer, RGMs, and other developments; hierarchical and hybrid models; and structure learning.", + "abstract_sha256": "1acf9b424265b2530a9825e52fe89e9718323b0fe9daa84aff0736c5120d7eb0", "contribution_type": "Pre-recorded Video presentation", "id": "talk-f41341ff72644ef5", "pictures": [ @@ -620,8 +896,24 @@ "title": "Active Inference Tutorial: Perspectives, Correlates, and Future Directions for Graphical Brains" }, { - "abstract": "The Free Energy Principle offers a physically grounded account of intentional properties in mind and life, framed within Dynamical Systems Theory. Because of this generality, it was proposed as a formal basis to study biological autonomy, autopoiesis, and enactive cognition. Yet, those approaches are defined by a relational understanding of mind and life, centred on the description of engagement with the world as a constructive process where agents are “laying down a path in walking”. Building from Rosen’s relational biology, we argue that such processes are by construction outside a “Newtonian” description in terms of well-defined observables and their dynamics. Rather, we draw from Fields’ demonstration of the emergence of contextuality – the participatory production of observables against which the agent regulates their activity – in classical, thermodynamically bounded processes of observation. We suggest a complementarity between Friston’s classical FEP and Fields et al.’s “quantum” (or, in our account, relational) FEP, describing respectively the dynamics within a biological / cognitive space of relevant observables and the physical processes underlying the construction of that space. Finally, we propose a structural realist interpretation of this complementarity within the “closure of constraints” account of biological autonomy, where both principles are understood to act on a physically embedded system of biological constraints rather than an abstract mathematical space assumed to fully represent biological / cognitive organization. This approach foregrounds the synthesis between mathematical and theoretical approaches to biological autonomy, thus supporting a principled approach to both analyzing the organization of natural systems and attempting the implementation of artificial autonomy.\n\n\nThis presentation is adapted from Guénin--Carlut, Avel. 2026. “A Tale of Two Principles - Towards a Relational Perspective on the FEP.” https://hal.science/hal-05697510., currently under review at the Entropy Special Issue “Active Inference in Cognitive Neuroscience”.", - "abstract_sha256": "059387f4d15fe2e1057fbd24d182cb5053d346ced3cd03029ac6e472ba395df8", + "abstract": "As AI systems increasingly mediate access to scientific knowledge, how they communicate complex theories becomes consequential for public understanding. Active inference presents a distinctive challenge: its mathematical and computational foundations offer explanatory resources, yet accessible accounts must distinguish formal results, empirical evidence, and emerging interpretations. Simplification can support understanding, but can also obscure uncertainty or encourage misleading conclusions about personal experience.\nBuilding on Pereira and Chamberlin’s manuscript, this foundational study will explore recommendations for large AI companies communicating active inference to general audiences. Its central question is how AI communication can preserve scientific integrity while helping people understand both the framework's core concepts and their potential relevance to everyday life. Particular attention will be given to the difference between being able to repeat an explanation and developing an understanding that supports reflection, judgment, and appropriate application.\nThe project will draw on literature spanning active inference, science communication, human–AI interaction, and learning. It will examine candidate communication approaches, including layered explanations, everyday examples, explicit statements of uncertainty, and prompts that connect abstract concepts to users' existing knowledge. A potential evaluation framework could consider conceptual accuracy, accessibility, perceived personal relevance, and the ability to distinguish supported implications from speculation. This framework could guide empirical evaluation of approaches.\nThe proposed contribution is an initial set of communication principles and research questions for AI developers, researchers, and educators. Rather than assuming that greater accessibility necessarily produces better understanding, the study will examine the tensions between clarity, scientific fidelity, personal relevance, and unwarranted certainty. The broader aim is to support communication that enables people to engage meaningfully with complex science without presenting a theoretical framework as a comprehensive explanation of their lives. This thirty minute presentation will outline the rationale, proposed framework, and recommendations emerging from the work, alongside priorities for further research and evaluation.", + "abstract_sha256": "fcfc11fc0b871107bf6574eb14579fbabe42a31981b7fae1eebeb05e491c8c6e", + "contribution_type": "Pre-recorded Video presentation", + "id": "talk-7013ce9d23d8fd4d", + "pictures": [ + { + "alt": "Anna Pereira — submitted presenter image", + "path": "assets/portraits/2a3a715956361efcfbf0b8d9187f5ddc74b09493ba93951fa7aa3845a16e6073.jpg", + "sha256": "2a3a715956361efcfbf0b8d9187f5ddc74b09493ba93951fa7aa3845a16e6073" + } + ], + "presenter": "Anna Pereira", + "source_url": "https://2026symposium.activeinference.institute/", + "title": "Inferring the Unseen" + }, + { + "abstract": "The Free Energy Principle offers a physically grounded account of intentional properties in mind and life, framed within Dynamical Systems Theory. Because of this generality, it was proposed as a formal basis to study biological autonomy, autopoiesis, and enactive cognition. Yet, those approaches are defined by a relational understanding of mind and life, centred on the description of engagement with the world as a constructive process where agents are “laying down a path in walking”. Building from Rosen’s relational biology, we argue that such processes are by construction outside a “Newtonian” description in terms of well-defined observables and their dynamics. Rather, we draw from Fields’ demonstration of the emergence of contextuality – the participatory production of observables against which the agent regulates their activity – in classical, thermodynamically bounded processes of observation. We suggest a complementarity between Friston’s classical FEP and Fields et al.’s “quantum” (or, in our account, relational) FEP, describing respectively the dynamics within a biological / cognitive space of relevant observables and the physical processes underlying the construction of that space. Finally, we propose a structural realist interpretation of this complementarity within the “closure of constraints” account of biological autonomy, where both principles are understood to act on a physically embedded system of biological constraints rather than an abstract mathematical space assumed to fully represent biological / cognitive organization. This approach foregrounds the synthesis between mathematical and theoretical approaches to biological autonomy, thus supporting a principled approach to both analyzing the organization of natural systems and attempting the implementation of artificial autonomy.\n\n\n\nThis presentation is adapted from Guénin--Carlut, Avel. 2026. “A Tale of Two Principles - Towards a Relational Perspective on the FEP.” https://hal.science/hal-05697510\n?\n., currently under review at the Entropy Special Issue “Active Inference in Cognitive Neuroscience”.", + "abstract_sha256": "c170e3299f8292d8fefbd89206cd6527953048a9b86da362d1115f3cebba9269", "contribution_type": "Pre-recorded Video presentation", "id": "talk-5f1d4f1776db95c2", "pictures": [], @@ -629,6 +921,16 @@ "source_url": "https://2026symposium.activeinference.institute/", "title": "A precis of two principles - Towards a relational perspective on the FEP" }, + { + "abstract": "Maladaptive daydreaming (MD) involves prolonged immersion in self-generated fantasy and difficulty disengaging despite awareness that the experience is imagined. We propose a testable active-inference framework in which the precision assigned to internal predictions relative to external sensory evidence may contribute to self-sustaining fantasy. This precision-feedback mechanism is a hypothesis, not an established clinical explanation. Using analytic counterexamples and 115,200 synthetic datasets, we show why finite-time persistence and hysteresis-like observations alone do not identify bistability, and examine how ramp-and-hold experimental designs can improve discrimination among competing models. Exploratory secondary analyses of daily-diary data are discussed at their actual timescale, without treating daily associations as proof of an episode-level mechanism. The talk connects active inference, computational psychiatry, experimental design, and open science. It concludes with falsifiable predictions and a research agenda focused on measuring internal and sensory precision, tracking within-episode dynamics, comparing alternative models, and testing feasible interventions. We welcome discussion of practical measurements and alternative explanations.", + "abstract_sha256": "64ae2fa262858b682678ae45ea44c066914791e44d8dc79fd15cc8279fc748a8", + "contribution_type": "Pre-recorded Video presentation", + "id": "talk-c43c58f58bdbaa14", + "pictures": [], + "presenter": "Bo Lee", + "source_url": "https://2026symposium.activeinference.institute/", + "title": "Maladaptive Daydreaming as a Precision Trap: An Active Inference Framework, Its Testable Core, and Evidence from Daily Diaries" + }, { "abstract": "While the generation of behaviors is generally substrate-agnostic (there is more than one way to build a neural circuit), complex behaviors require an increase in representational complexity. We explore an architecture called the biological Rube Goldberg Machine (bRGM). This will be used to model the Developmental Neurosimulation of a generalized architecture that produces spatial navigation behaviors. Developmental Neurosimulation has been used to simulate the development of Braitenberg Vehicles. In this talk, we will explore the neurosimulation of Cybernetic Convolution Networks (CCNs), which have the potential to add great complexity to sensor-effector pairings. Additional connections to agentive learning and optimization models of behavior will also be discussed.", "abstract_sha256": "83f29b88ab29c219641635abf0dbb55e56c873f5d2fc3a58397eb796d10fb011", @@ -645,6 +947,38 @@ "source_url": "https://2026symposium.activeinference.institute/", "title": "The Benefits of Circuit Convolution: behavioral complexity arises from developmentally- simulated nonlinearity." }, + { + "abstract": "Discovering whether there is a minimum structure required for inquiry to be complete.\nThe paper begins with six familiar questions: Who, What, Where, When, How, and Why.\nBut the research is asking something deeper: Are these six categories simply a useful convention, or does each represent something fundamentally necessary about what can be known?\nConsider the bridge-collapse example.\nTwo bridge collapses occur. For both, the involved parties are identified\n(WHO: the same engineering firm and inspection authority in both cases). The event is\nspecified (WHAT: structural failure of a suspension bridge). The locations are of the\nsame type (WHERE: bridges of identical design crossing rivers of similar flow rate). The\ntime and sequence of failure are identical (WHEN: both collapse within a three-minute\nwindow following the same triggering load). The mechanical failure sequence is\nidentical in both cases (HOW: the same crack propagation pattern, the same load\ntransfer failure, the same sequence of component failures).\nEven with all five answers fixed and identical across both collapses, the explanation\ndiffers: in the first case, deferred maintenance created metal fatigue that made the\nbridge susceptible to normal load; in the second, a design flaw in the original\nspecifications made the bridge inherently susceptible under identical conditions. The\nidentical HOW does not determine WHY. The joint answer set does not determine\nWHY. Therefore WHY is independent of WHO, WHAT, WHERE, WHEN, and HOW.", + "abstract_sha256": "821eba25fae79757eeed59a976333b863934dc3bd29dd71d399b536da134a886", + "contribution_type": "Pre-recorded Video presentation", + "id": "talk-cd9f12051e1897ff", + "pictures": [ + { + "alt": "Bruce Tisler — submitted presenter image", + "path": "assets/portraits/014645d9257973adff200505059320c73f69a21a0ef8a14d862a1dfed768c07d.jpg", + "sha256": "014645d9257973adff200505059320c73f69a21a0ef8a14d862a1dfed768c07d" + } + ], + "presenter": "Bruce Tisler", + "source_url": "https://2026symposium.activeinference.institute/", + "title": "The Minimal Necessary Basis for Primitive State-of-Affairs Inquiry" + }, + { + "abstract": "This project introduces an innovative computational architecture that bridges cognitive neuroscience and interactive simulation by integrating na Active Inference cognitive core directly into UI engines. Grounded in the Free Energy Principle, the system continuously minimizes Variational Free Energy (VFE) to drive autonomous agent behavior, dynamic navigation, and adaptive decision-making within complex, changing environments.\nUnlike traditional rule-based non-player characters (NPCs) or standard robotic controllers, the agent maintains an internal affective engine that maps multi-dimensional emotional telemetry—such as curiosity, motivation, stress, and fear—directly to behavioral drives, energy management, and rich audio-visual expressions. These affective states are rendered in real time through belief-versus-reality spatial overlays, floating emotional telemetry indicators, and adaptive state vocalizations that mirrors the agent's psychological trajectory.\nBy unifying autonomous perception-action loops with transparent emotional expression, this framework offers powerful capabilities for powering both advanced games and autonomous robots. Its potential applications span multiple domains:\nEntertainment: Powering next-generation autonomous game characters and dynamic simulations with emergent, lifelike behaviors.\nEducation: Serving as an interactive pedagogical platform for exploring cognitive architectures, cybernetics, and affective computing.\nCompanionship & Emotional Support: Creating empathetic digital companions and social robots capable of contextual awareness and authentic emotional resonance to foster meaningful human-agent interaction and support human well-being.\nUltimately, this project demonstrates that Active Inference provides a robust, biologically inspired foundation for artificial systems that not only navigate physical spaces intelligently but also communicate and connect through shared affective experiences.", + "abstract_sha256": "c89f12fbbcf76da2412aaca5e51050015750446fe15a755d4c3d4ec620119dbf", + "contribution_type": "Pre-recorded Video presentation", + "id": "talk-6cf1d4c15e70a930", + "pictures": [ + { + "alt": "Cleber Gomes — submitted presenter image", + "path": "assets/portraits/afd0c5d8c2a9d167373dc08080604af1784cf9f32eb47cb089098c3a62858304.jpg", + "sha256": "afd0c5d8c2a9d167373dc08080604af1784cf9f32eb47cb089098c3a62858304" + } + ], + "presenter": "Cleber Gomes", + "source_url": "https://2026symposium.activeinference.institute/", + "title": "Artificial Sentience Project" + }, { "abstract": "We consider how Active Inference ideas shed new light on issues in epistemology and axiology, in particular proposing a unifying paradigm based on understanding human enquiry as a process of engagement operating in multiple realms, specifically embeddedness, embodiedness, reflection, participation and immersion. We look at what can be learned from within this framework about areas which have become flashpoints in society such as conflicting worldviews and identity politics.", "abstract_sha256": "7698c38d740f2185f45bed49cf1201e488e4a97882bec28dc53437dd2b600501", @@ -656,8 +990,8 @@ "title": "Realms of cognitive engagement in Active Inference" }, { - "abstract": "Come see the beauty of mathematics. Its just not some fancy add on to life. It is life. ", - "abstract_sha256": "310d7b89e4a65aaeaae2c5c8bf6801a857c2fb36a7b7cf5291202c2e61c49a45", + "abstract": "Come see the beauty of mathematics. Its just not some fancy add on to life. It is life.", + "abstract_sha256": "d899063be7a8506cd83b94c4296f5fb29ccd76343d750ba966be9c32ee76dd20", "contribution_type": "Pre-recorded Video presentation", "id": "talk-42ee83a32b9468cb", "pictures": [ @@ -671,6 +1005,38 @@ "source_url": "https://2026symposium.activeinference.institute/", "title": "NUMEN: Bare metal inference" }, + { + "abstract": "Generalized Notation Notation (GNN) was introduced in 2023 (Smekal & Friedman) as a text-based standard for writing Active Inference generative models, with a “Triple Play” of linguistic, visual and executable renderings. This talk reports where GNN stands in 2026 and what it enables for applied Active Inference.\nA GNN file is plain Markdown: a state-space block declaring variables and dimensions, a connections graph, initial parameterization (e.g. A, B, C, D, E and related terms), equations, time settings and Active Inference Ontology annotations. The open-source GNN pipeline (v5, 25 steps) parses, type-checks and validates these files, visualizes and exports them, maps them to the ontology, renders them to backends including PyMDP, RxInfer.jl, ActiveInference.jl and JAX, executes the results, and reports each run against source hashes and configuration. The repository’s 38 exemplar models span static and dynamic perception, discrete POMDPs, the epistemic T-maze, continuous and hybrid models, hierarchical and deep temporal models, Dirichlet learning, precision and curiosity, and multi-agent stigmergic swarms.\nThe core is GNN as the specification layer for real projects. The Active Inference Language Model (AILM, released October 2026) uses GNN to specify each layer of a six-level hierarchical discrete language system (letters, phonemes, words, case roles, sentence types, document position), executed in pymdp with no transformer, neural network or learned embedding, and checked with a gate-audit method that tests claims against trivial baselines and deliberately broken variants and ties each reported number to its recorded run. Shorter case studies: deterministic codebase-to-GNN translation (COGANT), inference over assembly traces (Active Assembly), Battleship, and gratitude and model revisability (Thanks for Nothing).\nThroughout, I distinguish what a GNN declaration establishes (an inspectable, reproducible model) from what needs separate evidence (backend support, numerical acceptance, empirical claims). I close with open problems and invitations for contributors: new exemplars, backends, ontology mappings and teaching materials.", + "abstract_sha256": "d7c3fd3883153a0fe675a1237a52b9b027b56d7ab9058d94a4b4e1a517b61f04", + "contribution_type": "Pre-recorded Video presentation", + "id": "talk-1176205d9547addb", + "pictures": [ + { + "alt": "Daniel Friedman — submitted presenter image", + "path": "assets/portraits/c570c7d39296550cf244847da9140cf64120c44173393425ca56e531ceb2fdde.jpg", + "sha256": "c570c7d39296550cf244847da9140cf64120c44173393425ca56e531ceb2fdde" + } + ], + "presenter": "Daniel Friedman", + "source_url": "https://2026symposium.activeinference.institute/", + "title": "GNN in 2026: Generalized Notation Notation as a Specification Layer for Applied Active Inference" + }, + { + "abstract": "Repetitive and self-stimulatory behaviors have been re-evaluated over time, evolving from simple dysfunctions to adaptive self-regulatory strategies, however, a unified biological framework capable of explaining their contextual activation and underlying physiology remains incomplete. The present paper proposes a theoretical-hypothetical model (grounded in the Free Energy Principle and the Active Inference framework) that conceptualizes stimming as serving a dual role: both as a temporary homeostatic buffer, activated during states of prefrontal executive overload, and sensorimotor depletion and as a modulator of attentional processes and information processing. Distinctions are delineated between these forms of stimming and the non-creative flow ( FNC/NCF) state, alongside specific considerations regarding neurodevelopmental conditions.", + "abstract_sha256": "6c40e92cabf5348bae014263b3e03f158b272627c284218e063b3455a9e3469a", + "contribution_type": "Pre-recorded Video presentation", + "id": "talk-29a71ff7cce585e0", + "pictures": [ + { + "alt": "Diaconescu Adrian-Stelian — submitted presenter image", + "path": "assets/portraits/28ba1fe719a313a2da71488ecbfda5816b6cf4cb894126df957c14143355d245.jpg", + "sha256": "28ba1fe719a313a2da71488ecbfda5816b6cf4cb894126df957c14143355d245" + } + ], + "presenter": "Diaconescu Adrian-Stelian", + "source_url": "https://2026symposium.activeinference.institute/", + "title": "Stimming as a Temporary Homeostatic Buffer and Modulator in Autism Spectrum Disorders" + }, { "abstract": "In multi-agent social settings, model reliability varies across relationships. Beyond inferring what others will do, an agent must calibrate how confidently those inferences should guide policy selection for each relationship. An agent may maintain a well-validated model of one partner, a fragile model of another, and a model under revision for a third; collapsing these into a single confidence estimate loses information relevant to policy selection. We therefore formalize affective precision as a relationship-specific metacognitive estimate of confidence in the current partner model. Each partner's behavioral evidence updates a local confidence estimate that modulates policy precision during selection, regulating how strongly current beliefs are expressed in policy rather than changing the content of those beliefs. Simulations in a multi-partner graded trust game show that partner-local affective precision influences behavior primarily through policy commitment rather than direct improvement of partner-state inference. Because the mechanism tracks partner-response predictability rather than realized payoff, greater confidence produces sharper policy commitment without necessarily producing higher rewards. Under abrupt shifts in social behavior, confidence accumulated from previously reliable predictions can remain behaviorally active after the relationship changes, showing that confidence revision can lag behind social change. Finally, varying precision gain and priors produce distinct trust-calibration dynamics, showing how confidence accumulation and revision depend on model parameters. Together, these results show how relationship-specific affective precision can distinguish social prediction from social policy commitment.", "abstract_sha256": "8e1b8ed8824fcc82fd9a957ea79fa213853baa2fcd10e2a8d3540a223851f6fc", @@ -681,17 +1047,54 @@ "alt": "Harshil Shah, Andrew Pashea — submitted presenter image", "path": "assets/portraits/526879f35c614a3d733bba044f41abac57d52a7ffcbf9be6de7d6a4cd1af4a17.jpg", "sha256": "526879f35c614a3d733bba044f41abac57d52a7ffcbf9be6de7d6a4cd1af4a17" - }, - { - "alt": "Harshil Shah, Andrew Pashea — submitted presenter image", - "path": "assets/portraits/4ee8e72f2ce9ac573ddc96edbc24be6f84b244898fc4b700980bc71f79f0d20f.jpg", - "sha256": "4ee8e72f2ce9ac573ddc96edbc24be6f84b244898fc4b700980bc71f79f0d20f" } ], "presenter": "Harshil Shah, Andrew Pashea", "source_url": "https://2026symposium.activeinference.institute/", "title": "Partner-Specific Affective Precision in Social Active Inference" }, + { + "abstract": "We present VAMAI (Variational Affordance Mapping and Active Inference), a training-free hierarchical framework for autonomous UAV navigation in partially observed environments where reliable GPS may be unavailable. VAMAI brings together affordance theory, probabilistic environmental modeling, and active inference to address how an autonomous agent can navigate while continually learning about its surroundings.\nVAMAI connects geometric perception to the question of which actions the environment affords the drone. It maintains probabilistic beliefs over possible obstacle configurations, updated through LiDAR and depth-sensing observations. From these beliefs, the system derives regional suitability estimates describing whether passages are blocked, constrained, or sufficiently open for traversal.\nFor decision-making under uncertainty, Expected Free Energy (EFE)-based planning evaluates candidate movements using goal progress, regional suitability, and the expected information gain of future observations across spatial regions. At the local level, a separate motion-planning and control system translates selected intentions into dynamically feasible trajectories, accounting for obstacles, vehicle motion, and evolving environmental beliefs.\nWe will illustrate this process through simulation examples, tracing how sensor observations influence environmental beliefs, regional navigation decisions, and continuous flight execution. We will also discuss the computational and architectural challenges of coordinating perception, inference, planning, and control as independently progressing components, and how these considerations shape the development of a scalable active inference navigation system.", + "abstract_sha256": "de462961da948aa635ce4424887e812ec325edb9952a55082776735f2755e86f", + "contribution_type": "Pre-recorded Video presentation", + "id": "talk-926ae4cbac21b98d", + "pictures": [ + { + "alt": "Harshil Shah, Satyaki Maitra — submitted presenter image", + "path": "assets/portraits/567f6620a2f224ac3f1f5a360c46e4dd3ccad50975342b163b097cbb8130f4da.jpg", + "sha256": "567f6620a2f224ac3f1f5a360c46e4dd3ccad50975342b163b097cbb8130f4da" + } + ], + "presenter": "Harshil Shah, Satyaki Maitra", + "source_url": "https://2026symposium.activeinference.institute/", + "title": "VAMAI: Variational Affordance Mapping and Active Inference for Training-Free UAV Navigation" + }, + { + "abstract": "", + "abstract_sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", + "contribution_type": "Pre-recorded Video presentation", + "id": "talk-e486df293cd09fef", + "pictures": [], + "presenter": "Holly Grimm, John H. Clippinger, Daniel Ari Friedman", + "source_url": "https://2026symposium.activeinference.institute/", + "title": "Coupling energy-system models with a seam you can read and an agent you can audit: Active Inference in the NEST programme" + }, + { + "abstract": "How can a prosocial agent reconcile self-protection with convergence toward mutual cooperation? Classic reactive strategies, such as Tit-for-Tat (TFT) and Win-Stay Lose-Shift (WSLS), perform well only under restrictive conditions. Recent neurocomputational studies show that the brain dynamically updates beliefs over hidden intentions for adaptive social inference. Meanwhile, emerging accounts of social neuroscience suggest that social valuation may be regulated with reference to an internal setpoint, as an agent balances internalized prosocial motives against its current resource state.\nBuilding on both, we propose an active inference agent for the iterated Prisoner’s Dilemma where prosociality is endogenously regulated. This includes (i) perspective-taking, approximated via particle filter Bayesian inference over the opponent’s model parameters; (ii) distributional reinforcement learning, implemented as quantile regression temporal difference learning and Dyna planning with generative replay; (iii) prosociality regulator, which dynamically controls the weight placed on the other’s expected utility relative to a setpoint; and (iv) a self-model, which memorizes partner-specific social expectations and learned reward distributions across interactions.\nIn preliminary simulations, our agent robustly tracks fluctuating opponent intentions and differentiates reciprocating from exploitative partners. Under non-stationary payoffs in mixed memory-one populations, it achieves higher long-run average payoffs than any other tested strategy after a learning phase, while maintaining mutual cooperation at a level above TFT and WSLS yet below ALLC and GTFT. These findings highlight setpoint-referenced regulation as a candidate computational foundation for adaptive prosociality, offering mechanistic insights into how self-protection and mutual cooperation can be jointly realized and an extensible blueprint for socially aligned embodied agents.", + "abstract_sha256": "161af41147ed6bde7d7618a3ee16d0125b7348cfd3484cf188eded4c3ce4f7e4", + "contribution_type": "Pre-recorded Video presentation", + "id": "talk-49c86444406fb4d6", + "pictures": [ + { + "alt": "Hyojun Choi — submitted presenter image", + "path": "assets/portraits/fa8613f986b60fd7ff77b5d82e62eb91f48b04ba7c81391a19e37df6517d8405.jpg", + "sha256": "fa8613f986b60fd7ff77b5d82e62eb91f48b04ba7c81391a19e37df6517d8405" + } + ], + "presenter": "Hyojun Choi", + "source_url": "https://2026symposium.activeinference.institute/", + "title": "Adaptive Prosociality Through Allostatic Regulation in Social Dynamics: A Simulation Study" + }, { "abstract": "My research project asks, \"How can an autonomous robot learn to survive in a world it initially knows nothing about?\"\n\nTo answer this, I am building a cognitive system (enactive, emergent and symbolic) embodied in an autonomous -and mortal- Lego robot. The robot survives in a nourishing and dissipative world by acting-sensing and finding meaning in what it experiences.\n\nThis project presents many challenges, technical but also metaphysical. A primary concern is the multiplicity of perspectives on \"reality\", that of the robot, of its (agentic) environment, of the programmer, and of observers. What can each know, and how? Is a confusion, a cross-contamination of epistemologies inevitable? With \"agentic AI\" poised to transform our lives, these questions about knowing matter far beyond the programming of a Lego robot, but they can be illuminated by it.", "abstract_sha256": "eb8861e792d73a3583ae2fede73c40ea4a6935bd0cb1970a14859c0e3e558a52", @@ -722,15 +1125,41 @@ "abstract": "Active inference and the free-energy principle transform design theory by providing a unified language for understanding both the production of artifacts and their use. Contemporary design research often separates two problems that are, in practice, deeply entangled. On the one hand, it studies the design process: how designers explore possibilities, negotiate constraints, prototype solutions, and stabilize forms. On the other hand, it studies user–product interaction: how artifacts orient attention, solicit action, shape habits, and reorganize experience. These two domains are usually described through different vocabularies and evaluated through different metrics.\nActive inference offers a way to reconnect them. If living and cognitive systems persist by minimizing uncertainty through perception and action, then design can be understood as the deliberate construction of environments, artifacts, and interfaces that modulate the user’s field of possible inferences. From this perspective, a product is not simply a passive object that satisfies pre-given needs. It is a predictive structure: a material and semiotic configuration that embodies assumptions about users, contexts, actions, and futures. Conversely, the design process itself can be modeled as the progressive refinement of generative models through which designers attempt to reduce uncertainty about what an artifact should do, how it should appear, and how it will be taken up in practice.\nThe paper’s central claim is that active inference gives design a common metric: the modulation of entropy, precision, affordance, and expected free energy across the designer–artifact–user system. This makes it possible to describe design as the engineering of predictive niches and Markov blankets that coordinate human and technological agency. The result is a unified framework for analyzing how artifacts are imagined, produced, interpreted, and incorporated into everyday life.", "abstract_sha256": "82e81c60aa933cbc47fd669f11363923d605357f2e979311153f492675de1ab1", "contribution_type": "Pre-recorded Video presentation", - "id": "talk-6515495f47af63f0", + "id": "talk-64d975092642fb38", "pictures": [], "presenter": "Luca Possati", "source_url": "https://2026symposium.activeinference.institute/", - "title": "Active inference and design " + "title": "Active inference and design" }, { - "abstract": "Tiny social units of Pliocene Hominina fostered exchange among and between adults, youngsters and babies, about information gleaned and garnered by Active Inference. Henceforth, human evolution would involve the prehistory of \"Homo informatio\" whose biological, social, technological, and cultural legacy is an outcome of evolutionary 'snakes and ladders' autopoiesis and self-evidencing, indicated by archaeological irregular regularities and regular irrrgularities in the worldwide Pleistocene spatiotemporal record (as only is to be expected by the itinerancy of stochastic markovian processes), and of uneven imperceptible network-spread between social units, individuals, and, naturally, their cerebral neuronal microcircuits. Spreading, deep, informational networks characterised and underpinned human evolution during four million years. They do so still. \"Mighty oaks from litte acorns grow\", Geoffrey Chaucer, dixit in \"Troilus and Creysede\" in 1374.", - "abstract_sha256": "7c8da7406616421f522639d32cab02420c3042b0ff5bc548b61157c81d446631", + "abstract": "Most real-world work is messy, variable, and full of contact: cables that flex, parts that don’t quite fit, valves that stick. Traditional robots excel only where conditions are controlled and repeatable, and today’s leading AI approaches (vision-language-action models, diffusion policies, and embodied foundation models) struggle to close the gap. They depend on massive datasets that don’t exist for physical interaction, require heavy compute at inference time, and break down when reality drifts from their training distribution.\nThis talk argues that dexterity is not primarily a data problem but an adaptation problem. Nature doesn’t solve manipulation by training once offline and hoping it generalizes. It learns continuously at the point of contact, with perception, action, and learning operating as a single loop. We contrast the dominant paradigm of universal function approximation, which is retrospective, data-dependent, and time-agnostic, with enactive synchronization: a prospective, time-aware process in which an agent continuously adjusts to maintain predictive coherence with its environment.\nTracing a line from W. Ross Ashby’s cybernetics and his Homeostat through the Free Energy Principle and Active Inference, we show how ultrastable networks can form a minimum-prediction-error synchronization manifold between a robot and the world it touches. Sensors and motors become the interface through which goals are expressed as preferred states, and behavior emerges from the network’s drive to stay in equilibrium.\nWe then show what this looks like in practice: compact models that run on a single CPU at the edge, update in under a millisecond, and learn tasks in minutes rather than weeks, illustrated through new demonstrations of adaptive dexterous manipulation on real robotic hardware.", + "abstract_sha256": "53deffec496e2322285e7d15c559b85937810dac1b14b43291e5fdc4f6a6dc63", + "contribution_type": "Pre-recorded Video presentation", + "id": "talk-36b60916ca76bc85", + "pictures": [ + { + "alt": "Matthew Brown — submitted presenter image", + "path": "assets/portraits/edc5a408ce307d7487085d9f429c8b6dd571d376a76f9986de7401260d5d575f.jpg", + "sha256": "edc5a408ce307d7487085d9f429c8b6dd571d376a76f9986de7401260d5d575f" + } + ], + "presenter": "Matthew Brown", + "source_url": "https://2026symposium.activeinference.institute/", + "title": "Unlocking Robotic Dexterity" + }, + { + "abstract": "Imagine the Economics Nobel in 2036 is awarded for the 1st time to a community: the Active Inference Community. Scientists, communities, and institutions now tackle tangled social and ecological challenges more reliably, because they learn together within, between, and beyond their disciplines. What did we build to get there?\nThis talk works backwards from that future, building on last year’s invitation to grow the learning capacity of our networks. It proposes machine-assisted collective learning infrastructure enabled by active inference: an organ through which diverse networks sense, make sense, infer, compass, and coordinate adaptive decisions across ways of knowing.\nIts architecture is a loop of Evidence, Dialogue, and Adapt, circling a field of memory three layers deep at every scale. The map holds what is measured and recorded. The compass holds interior perceptions and shared hopes that orient action, even in the dark. The ground holds what we inherit, biologically and culturally, from immune memory to language and norms. Each layer is evidence of a different kind, and none certifies another. Friedman’s meta-pragmatic mechanisms, defined for a single agent and extended here to groups, arbitrate how far to trust each layer and when to revise it.\nStories from development practice, ecology, and network science serve as modelling targets: project dialects that bind teams and wall them off, lessons trapped at the wrong scale, forests managed for what could be measured. Today’s dialogue, it turns out, is tomorrow’s inheritance.\nThe talk is frank about AI’s double edge: pre-trained models are canned, but the medium around them can learn, and the same tools can homogenise dialogue, breed confident wrongness, or let agents narrate their own history.\nListeners will leave with a flipped version of an old parable, fresh metaphors for explaining active inference, and an invitation to help build the organ.", + "abstract_sha256": "a697794f407c85b1c79b57371e9acc883d99e36b0ff0fddb4241da2085a33b64", + "contribution_type": "Pre-recorded Video presentation", + "id": "talk-411510fca178636f", + "pictures": [], + "presenter": "Michael Lennon", + "source_url": "https://2026symposium.activeinference.institute/", + "title": "Today’s Dialogue, Tomorrow’s Inheritance: Active Inference for Networks That Learn Together." + }, + { + "abstract": "Tiny social units of Pliocene Hominina fostered exchange among and between adults, youngsters and babies, about information gleaned and garnered by Active Inference. Henceforth, human evolution would involve the prehistory of \"Homo informatio\" whose biological, social, technological, and cultural legacy is an outcome of evolutionary 'snakes and ladders' autopoiesis and self-evidencing, indicated by archaeological irregular regularities and regular irrrgularities in the worldwide Pleistocene spatiotemporal record (as only is to be expected by the itinerancy of stochastic markovian processes), and of uneven imperceptible network-spread between social units, individuals, and, naturally, their cerebral neuronal microcircuits. Spreading, deep, informational networks characterised and underpinned human evolution during four million years. They do so still. \"Mighty oaks from litte acorns grow\", Geoffrey Chaucer, dixit in \"Troilus and Creysede\" in 1374.", + "abstract_sha256": "e3f54f3143f768df41d3c729129911f42e4422f32dc2b7661679dd33c5bff57c", "contribution_type": "Pre-recorded Video presentation", "id": "talk-dba92aa493206478", "pictures": [], @@ -739,8 +1168,66 @@ "title": "Active Inference, Information, Human Evolution" }, { - "abstract": "The Rosales-Gheuens framework challenges the assumption that brute-force energy alone saves open systems from collapse. In this session, we will introduce a testable, normalized physics layer that merges the Rosales congruence principle with Gheuens’ interaction geometry. Rather than replacing established physical laws, this framework acts as a powerful diagnostic tool that sits cleanly on top of them. Our core master equation posits that a system's survival requires geometrically aligned effective recovery multiplied by a structural congruence gate, minus total perturbation. \nCrucially, we will explore how this model bridges directly to Karl Friston’s Free Energy Principle. Friston dictates that systems persist by minimizing variational free energy, or \"surprise.\" We will demonstrate how our framework translates this active inference into hard physical variables, mathematically formalizing how a system must continuously minimize structural mismatch and physical dissipation to avoid shattering.\nTo ground the theory empirically, we will use Quantum Error Correction (QEC) as our primary physical anchor, mapping code decoders and physical error rates directly to aligned recovery and perturbation. \nInteractive Aspect:The session will conclude with a collaborative exercise where attendees can apply the framework's \"falsification matrix\" to a theoretical open system, testing how our bounded composite equation predicts system survival compared to simpler Markovian baselines.", - "abstract_sha256": "ea87a50a101d45ee662cd27396a9c1b6aec25174405a8cbd50171fc98e0f8b89", + "abstract": "This paper proposes a simpler account of generalized relevance realization within active inference, responding to challenges to existing approaches by specifying an explicit relevance-selection criterion. The generalized relevance problem concerns how agents identify information and action consequences worth considering without exhaustive examination. The proposed criterion is that a recognized state becomes relevant when it activates a learned prediction of an outcome that would satisfy or frustrate an existing preference. Because such predictions may concern actions other than those currently pursued, relevance selection can precede current-policy evaluation. Recruited state information subsequently enters ordinary active-inference policy evaluation, while continuous learning establishes new predictors and predictive relationships. This account explains how learned predictions can recruit relevant information without requiring higher-order preference-precision regulation, while allowing flexible behavior under relatively stable preferences.", + "abstract_sha256": "80b19871d6bb2c5da0042d7c2badc813aff363c3ffa3aaf8c7a35581595198f7", + "contribution_type": "Pre-recorded Video presentation", + "id": "talk-42235dc73596d92f", + "pictures": [ + { + "alt": "Montijn van Loo — submitted presenter image", + "path": "assets/portraits/9038b884a77a3a6ce8a0ede72312187c3b0dcd96bfc61138aaa59b54e8547a1f.jpg", + "sha256": "9038b884a77a3a6ce8a0ede72312187c3b0dcd96bfc61138aaa59b54e8547a1f" + } + ], + "presenter": "Montijn van Loo", + "source_url": "https://2026symposium.activeinference.institute/", + "title": "Solving the Relevance Problem: A General Relevance Criterion in Active Inference" + }, + { + "abstract": "Philosophy is characterized by having something to talk about, yet no consensus on how to talk about it. Mathematics helped physics emerge from philosophy into a cumulative science. Active Inference, potentially a unifying theory of psychology, may mark a comparable philosophical turning point. Psychology and sociology are already flourishing in this mathematical direction, but its consequences may reach further, into philosophical problems that have resisted resolution for centuries. This paper applies that prospect to the mark of the moral and the longstanding tension between moral subjectivity and objectivity.\nThe proposal is that the mark of the moral is the best predicted global outcome under a role-invariant desire. Active Inference represents choice as the evaluation of predicted outcomes through beliefs and desires. Outcomes are relationally represented according to one’s role within them. An authorial desire is invariant under such role changes: it values the same global outcome equally regardless of which participant’s perspective is taken. Moral action consequently maximizes expected authorial value.\nThis distinction clarifies moral disagreement. Individuals may share an authorial desire yet choose differently because their worldviews generate different predictions. Even agreement about immediate consequences need not establish agreement about their wider significance. The Shared Author Hypothesis goes further, proposing that all role-invariant desires are one and the same. Under this hypothesis, moral disagreement ultimately concerns differences in worldview rather than competing moral standards.\nThe framework thus offers a conditional resolution of the subjectivity–objectivity problem: moral choices depend on individual beliefs, while their underlying standard of evaluation remains shared. It also redirects moral and political disagreement toward determining which worldview best fits the available evidence.", + "abstract_sha256": "46149f1f3f5dcd4be689ab05db73e90bec29e879e207e53f6595039738e3a2dd", + "contribution_type": "Pre-recorded Video presentation", + "id": "talk-528af36ab3c9a9d4", + "pictures": [ + { + "alt": "Montijn van Loo — submitted presenter image", + "path": "assets/portraits/9038b884a77a3a6ce8a0ede72312187c3b0dcd96bfc61138aaa59b54e8547a1f.jpg", + "sha256": "9038b884a77a3a6ce8a0ede72312187c3b0dcd96bfc61138aaa59b54e8547a1f" + } + ], + "presenter": "Montijn van Loo", + "source_url": "https://2026symposium.activeinference.institute/", + "title": "The Mark of the Moral and the Shared Author Hypothesis: An Active Inference Approach to Moral Objectivity" + }, + { + "abstract": "Common Grounds AI is making virtual worlds where humans and AIs can inhabit simulated bodies and playfully pursue open-ended goals together. Each world is a rich but simplified analog of our own, using material physics simulations to ground core aspects of human experience.\nToday’s AIs are largely trained on fragmented, ungrounded text that conveys a distorted, partial version of human conceptual ontology. We are closing this ontological gap by building rich virtual playgrounds where AIs and humans can share social and physical experiences, grounding language in embodied interactions that mirror young children’s cognitive development. Our most recent prototype models temperature and chemical reactions. It also includes experimental examples of native creatures that consume environmental free energy and move using muscle analogues, with plans for action-perception loops guided by Active Inference principles. We plan to explore whether incorporating embodiment yields systems that have ontologies more compatible with human cognition, both by fine-tuning LLMs on embodied experience and by building hybrid architectures merging the environment’s native creatures with LLMs.\nOur virtual worlds will be broadly accessible (playable in a browser, no installation needed), released as open-source software, and (we hope) fun to play. We want these worlds to serve as a common ground for humans and many forms of AI and AI research to meet.", + "abstract_sha256": "f5122156b0817958d2affb4736cd65137fa506451ce4578d5f61a97904cae213", + "contribution_type": "Pre-recorded Video presentation", + "id": "talk-862add1fbafe9ec5", + "pictures": [], + "presenter": "Nick Hay, Adam Safron", + "source_url": "https://2026symposium.activeinference.institute/", + "title": "Commongrounds AI: alife-inspired virtual environments for embodied active inference agents" + }, + { + "abstract": "Psychological taxonomies, from the Big Five to the DSM, are descriptive, not mechanistic: they classify how people differ without modelling why. Active inference offers a mechanistic account: it formalises a small set of generative parameters that govern how a mind perceives, learns and acts under uncertainty. Chief among them is precision: the confidence a mind places in different sources of information. Sensory and prior precision govern whether new evidence or existing expectations dominate belief updating; policy precision, how confidently a mind selects goal-directed action over habit; preference precision, how it balances reward-seeking against information-seeking. Their configuration constitutes what we call a mind’s perceptual phenotype. Meta-precision, a mind’s capacity to monitor and revise its own precision assignments, has been proposed as the mechanism underlying epistemic depth — one of three conditions the theory identifies for consciousness (Laukkonen et al. 2025). Today first-order precision parameters are estimated from laboratory tasks; meta-precision has no established paradigm.\nThis talk introduces Percepia, a perceptual phenotyping layer between a user and a large language model that tracks probability distributions over personality and beliefs, and aims to reverse-infer the precision parameters from them.\nI present two experiments with synthetic LLM personas, where parameters are known by construction. In the first, 625 personas were prompted to vary sensory, prior and policy precision in conversation; Percepia inferred their Big Five from the dialogue, testing whether precision leaves a recoverable signature in personality, as Safron and DeYoung (2021) propose. In the second, personas varied in meta-precision, and Percepia tracked their stage of adult development (Kegan 1994).\nAs the personas were primed with the theory, these experiments test whether a reverse-inference channel from language to precision parameters is viable, not whether the mapping holds in humans: a first step toward measuring perceptual phenotypes at scale.", + "abstract_sha256": "3b0e104b27b606531d74a8cab2f3c0fc5a1a9d19ad7346550437ff4bf1436d65", + "contribution_type": "Pre-recorded Video presentation", + "id": "talk-662b4152a55d7304", + "pictures": [ + { + "alt": "Patricia Craja — submitted presenter image", + "path": "assets/portraits/00f3ab9b83d67462ccea7c6a47117ac7c5da72779236bc3c2257b8a5cfb04b70.jpg", + "sha256": "00f3ab9b83d67462ccea7c6a47117ac7c5da72779236bc3c2257b8a5cfb04b70" + } + ], + "presenter": "Patricia Craja", + "source_url": "https://2026symposium.activeinference.institute/", + "title": "Percepia: Perceptual Phenotyping from Conversation" + }, + { + "abstract": "The Rosales-Gheuens framework challenges the assumption that brute-force energy alone saves open systems from collapse. In this session, we will introduce a testable, normalized physics layer that merges the Rosales congruence principle with Gheuens’ interaction geometry. Rather than replacing established physical laws, this framework acts as a powerful diagnostic tool that sits cleanly on top of them. Our core master equation posits that a system's survival requires geometrically aligned effective recovery multiplied by a structural congruence gate, minus total perturbation.\nCrucially, we will explore how this model bridges directly to Karl Friston’s Free Energy Principle. Friston dictates that systems persist by minimizing variational free energy, or \"surprise.\" We will demonstrate how our framework translates this active inference into hard physical variables, mathematically formalizing how a system must continuously minimize structural mismatch and physical dissipation to avoid shattering.\nTo ground the theory empirically, we will use Quantum Error Correction (QEC) as our primary physical anchor, mapping code decoders and physical error rates directly to aligned recovery and perturbation.\nInteractive Aspect:The session will conclude with a collaborative exercise where attendees can apply the framework's \"falsification matrix\" to a theoretical open system, testing how our bounded composite equation predicts system survival compared to simpler Markovian baselines.", + "abstract_sha256": "c11e491acb2cbdc1168560dd59fd8b34c93730cdb13ff63640eafc847c9d7c85", "contribution_type": "Pre-recorded Video presentation", "id": "talk-81e4f2c68471e90d", "pictures": [], @@ -749,8 +1236,18 @@ "title": "Congruent Interaction Geometry" }, { - "abstract": "The highly developed human sense of self is one of our most distinctive traits. We each acquire our sense of self in conversations, as we learn how conversational partners regard us. Our primary sense of self is not ‘myself as I experience myself’, but ‘myself as I think other people see me’ – a second-hand view of myself. In private verbal thought, with nobody else physically present, other people are represented as a shadow audience in our minds – an audience which sees what we do or say, and judges us for it. The shadow audience is the source of human emotion and motivation.\n \nLanguage started in conversational pragmatics, before there was complex syntax or semantics. Pragmatics requires us to mind-read, to infer what a conversational partner thinks, wants or needs. In this way we infer what or conversational partners think about us – how they assess our social status. In any conversation, we are concerned about our own social status in the eyes of a conversational partner, or of a shadow audience. This is illustrated by the pragmatic phenomenon of conversational irony.", - "abstract_sha256": "c3e821957c0b490a549dc3e4f90a62c49c42cad0fdcd90c676dbdab041549add", + "abstract": "Short-form video feeds are now a major channel of belief formation, yet cognitive security is often studied at the level of institutions and populations rather than individuals. Albarracin et al. (2022) showed with active inference how echo chambers can emerge among peers. We take a first step toward the individual case: one person and an engagement-optimising recommender.\nWe treat the phone screen as a Markov blanket. What a person is shown forms the sensory states, and what they do forms the active states. We sketch a minimal user generative model in GNN and couple it with a simple recommender in pymdp, to explore when such a loop keeps a feed diverse and when it narrows or escalates.\nWe present early simulations and outline a privacy-preserving, on-device instrument through which Worldview aims to make these dynamics visible to people: awareness as a product.", + "abstract_sha256": "e04a11951161a02f7a174fdf8c2d28b199ec9c1601a378834a9fe16d8f7b4413", + "contribution_type": "Pre-recorded Video presentation", + "id": "talk-e0738f0a19674084", + "pictures": [], + "presenter": "Rajkaushik Borgohain", + "source_url": "https://2026symposium.activeinference.institute/", + "title": "The Screen as a Markov Blanket: An Active Inference Account of Feed-Driven Narrowing, and a Personal Instrument to Measure It" + }, + { + "abstract": "The highly developed human sense of self is one of our most distinctive traits. We each acquire our sense of self in conversations, as we learn how conversational partners regard us. Our primary sense of self is not ‘myself as I experience myself’, but ‘myself as I think other people see me’ – a second-hand view of myself. In private verbal thought, with nobody else physically present, other people are represented as a shadow audience in our minds – an audience which sees what we do or say, and judges us for it. The shadow audience is the source of human emotion and motivation.\n\nLanguage started in conversational pragmatics, before there was complex syntax or semantics. Pragmatics requires us to mind-read, to infer what a conversational partner thinks, wants or needs. In this way we infer what or conversational partners think about us – how they assess our social status. In any conversation, we are concerned about our own social status in the eyes of a conversational partner, or of a shadow audience. This is illustrated by the pragmatic phenomenon of conversational irony.", + "abstract_sha256": "b2facde42a54466980e529920d2c7648c21c37fe1a1c4d321a1dac8cd4ded7d9", "contribution_type": "Pre-recorded Video presentation", "id": "talk-59d5a0672890ee22", "pictures": [], @@ -800,6 +1297,38 @@ "source_url": "https://2026symposium.activeinference.institute/", "title": "Entropy Minimization is Maximal Order" }, + { + "abstract": "The zone of bounded surprisal (ZBS), introduced by Manrique and Walker and developed with Friston, describes how much surprising input an agent can absorb under active inference. A narrow zone has been proposed to limit the faithful copying of novel behaviour in great apes, and very precise priors, which narrow the zone, to characterise discriminatory minds. In active-inference terms the zone is the set of outcomes whose surprisal stays under a bound. What sets that bound, and why crossing it costs the agent anything, is left open. We take up this question using four papers published or accepted in 2026 that develop Terrence Deacon’s teleodynamic account of information. A self-produced Markov blanket institutes the epistemic cut, so the zone belongs to the system that draws its own boundary. Function, understood as the at-risk regeneration of a capacity, gives that system a stake. The bound can then be read as the largest surprisal an agent absorbs without putting this capacity at risk, while precision sets how wide the zone is beneath it. Read together with Walker’s Homo informatio model, the account explains why width pays: constructive information saves work later and for others, and only an agent with a wide zone can hold it long enough to learn its use. It also raises a testable tension. The constructive information that a broader zone made possible over evolution may narrow the zone within a life when it is held with high precision, and informational inbreeding may be its counterpart at group scale. We propose three measurements, including one that holds surprisal fixed while varying what it threatens, and close with questions for discussion.", + "abstract_sha256": "b2b22c06394a743898a05cbe4a9c017bf0bf85d9f6ebd37966d2904e5e7909dd", + "contribution_type": "Pre-recorded Video presentation", + "id": "talk-17082b710a933608", + "pictures": [ + { + "alt": "Tao Gong — submitted presenter image", + "path": "assets/portraits/b9c441bd5583e527ea92fc31f647edd8b958564d964d5418c5ea82ba18c61a08.jpg", + "sha256": "b9c441bd5583e527ea92fc31f647edd8b958564d964d5418c5ea82ba18c61a08" + } + ], + "presenter": "Tao Gong", + "source_url": "https://2026symposium.activeinference.institute/", + "title": "What bounds the surprisal? Zones of Bounded Surprisal, self-maintenance and the informational turn in anthropogenesis" + }, + { + "abstract": "I'm a Holistic Health Practitioner and independent researcher on Hawaiʻi Island. This talk brings one open question to the Active Inference community: when a living system learns, does it only move differently through a possibility space that already existed, or can learning reorganize the space itself, changing what becomes available to perceive, generate, or do next?\nI come to the question from two directions. The first is physiology. In a 15-month longitudinal study of working first responders, still in design and not yet under review, the question is whether recovery changes which states and actions become available afterward, not only which state a person is in. Nothing has been measured yet. The second is method. In my own cross-domain research process, I carry a relation into another field, try to find where it breaks, and let that information change the next question. The history of that process seems to change which distinctions I can draw next. I present that as method, not as evidence about mechanism.\nSame grammar, not yet the same mechanism. Several formal lenses meet here: viability theory and reachable sets work on how the landscape of reachable states is reshaped, the difference between changing what a system generates and what it selects, and recent Active Inference accounts of insight as non-local search.\nSo the talk ends on a request rather than a result: what observation would distinguish a system moving through a space from a system whose space has changed, and what does Active Inference make of that difference?", + "abstract_sha256": "9a4aaceb2abc6bcbd344fe9dcf6b70c7a2ae02d3ca789617f3edb53c2a7f5f4d", + "contribution_type": "Pre-recorded Video presentation", + "id": "talk-15d7c576be7d71da", + "pictures": [ + { + "alt": "Tiga Kailimai — submitted presenter image", + "path": "assets/portraits/87ef26631a7d81343f3639ba3dd1ac645882b5ad26ff5185617c56464d15c53c.jpg", + "sha256": "87ef26631a7d81343f3639ba3dd1ac645882b5ad26ff5185617c56464d15c53c" + } + ], + "presenter": "Tiga Kailimai", + "source_url": "https://2026symposium.activeinference.institute/", + "title": "What becomes available next?" + }, { "abstract": "Active Inference commonly assumes a fixed, pre-specified action space and sufficiently informative preferences for planning. In realistic settings, however, an agent may encounter previously unavailable actions while receiving only sparse goal information and operating with a short planning horizon. Existing Active Inference formulations generally assume transition models for a predefined action set, leaving the initialization of newly introduced actions without direct transition evidence underexplored. Moreover, while Dirichlet transition learning provides a principled mechanism for accumulating observed evidence, it primarily reinforces supported transitions and does not explicitly exploit counter-evidence indicating that candidate transitions are inconsistent with observed outcomes. We propose Dynamic Action-Space Adaptation (DASA), a framework that enables Active Inference agents to incorporate new actions during interaction, initialize each new action’s transition model using the average transition slice of existing actions, and progressively refine it through Contrastive Transition Adaptation (CTA) as evidence accumulates. At the core of DASA, CTA combines complementary positive and negative updates for newly introduced actions. Positive updates adapt the reinforcement magnitude according to the current transition belief, assigning stronger updates to low-probability transitions supported by evidence while limiting excessive reinforcement of already confident transitions. Negative updates explicitly reduce support for candidate transitions contradicted by observed evidence, enabling the transition model to adapt in both supportive and corrective directions. Across eight random seeds and two target directions, CTA achieved lower MAE than Dirichlet transition learning in all runs, reducing the mean error from 0.1581 to 0.1370 (13.4%), while also achieving a slightly lower mean RMSE (0.3006 vs 0.3030). This indicates more consistent overall recovery of the ground-truth transition dynamics, despite seed-dependent variation in larger localized errors. Overall, DASA provides a practical mechanism for incorporating and adapting newly introduced actions in Active Inference, allowing their transition models to become progressively better aligned with the underlying environment dynamics.", "abstract_sha256": "80fed90d44032b793ea00070fa7f128684abf0ea38547d46bccbb86594735991", @@ -816,11 +1345,27 @@ "source_url": "https://2026symposium.activeinference.institute/", "title": "Dynamic Action-Space Adaptation via Contrastive Transition Adaptation in Active Inference" }, + { + "abstract": "A substrate that derives its own body in its own camera through published retinal biology running on hardware. The reafference ladder M0 to M3 is the engineering sequence: discretize action with a move-stop policy, let the body report its own arrival through proprioceptive timing, make arrival predictable, and identify the body as a place through sensitize-adapt dynamics.\nThe retinal circuit is the one measured in salamander retina by Kastner and Baccus (2013) and others, with thirteen published constants carried at their measured values. Nothing in the chain is tuned. The release time constant arrives on our camera at 2.35 seconds against the paper’s 2.40, over 98 continuous movement offsets in a novel room with direct sun and moving shadow. The body falls out as the region of the image that stays still while everything touching it moves. No markers, no CAD model, no training set.\nThe talk will present the implementation, the pre-registered falsification conditions, and the honest limits. τ_on fails against the instrument ceiling. One parameter is not derived. The core observation worth the room’s attention is that the policy chosen at M0 for credit assignment also solves self-localization at M3. One policy. Two problems usually treated as separate. Active inference predicts this in principle through information-maximizing policy selection, but the specific case of move-stop resolving both at once appears to be new to the literature.\nPre-recorded with embedded demonstration video. Live Q&A after broadcast if the symposium runs one.", + "abstract_sha256": "be7c7c71a12fb0a97c5ca87c58c0ef202b7305634cd307d0ee34dd5351a543b2", + "contribution_type": "Pre-recorded Video presentation", + "id": "talk-d6fc5ed19b44f3aa", + "pictures": [ + { + "alt": "Viraj Deshwal — submitted presenter image", + "path": "assets/portraits/1320bdf23d005bed17c726e17ff44f5ccbdc8ea7577928306be11953f8c31bbd.jpg", + "sha256": "1320bdf23d005bed17c726e17ff44f5ccbdc8ea7577928306be11953f8c31bbd" + } + ], + "presenter": "Viraj Deshwal", + "source_url": "https://2026symposium.activeinference.institute/", + "title": "A derived self" + }, { "abstract": "Recursive Organic Integration Framework (ROIF) is a deterministic, domain-agnostic computational architecture for complex systems whose present organization and subsequent responses may be shaped by prior interactions, distributed reconfiguration, and anticipatory preconfiguration. Rather than treating history as an additional input variable, ROIF represents how prior events may become embodied in the organization through which subsequent events are processed.\nThe framework distinguishes experimentally separable routes through which the past and represented possible futures may affect subsequent dynamics: history embodied in present organization; structured memory conditioning a subsequent transition; and anticipatory preconfiguration based on a represented possible future Temporal Image. Matched-current-state controls found no independently identifiable history-specific transition effect for the tested physical-history pathway. In contrast, a separate structured-memory experiment produced different subsequent physical transitions under matched represented physical state and adaptive connection state.\nThe predictive architecture allows possible future SystemImages to participate in present preconfiguration. Current experiments, however, test only a restricted one-step preconfiguration within the implemented prestress representation. This experimental restriction does not define the broader architectural role of prestress in ROIF, where prestress reflects an already organized relational system state shaped by interacting internal and external conditions, constraints, capacities, history, and time. Objective-independence tests further prevent interpreting the present result as evidence for general predictive stabilization.\nThese results motivate a boundary question rather than a claim of equivalence: when does a history-conditioned anticipatory architecture constitute Active Inference? ROIF does not presently require a variational free-energy functional, Bayesian generative model, or particular neurobiological implementation. The presentation therefore uses ROIF as a computational case study to ask which mathematical conditions distinguish Active Inference from a broader class of history-conditioned anticipatory systems, and whether these formalisms should be understood as nested, neighbouring, or formally distinct.", "abstract_sha256": "60aa6b1470e641034d9dc4496bc2ffef2d3eb25dd5a745561ef9ea2dc5c378c9", "contribution_type": "Pre-recorded Video presentation", - "id": "talk-877eaccf6e0007ec", + "id": "talk-29cbf4c7ed2a35fb", "pictures": [ { "alt": "Vitalii Shapovalov — submitted presenter image", @@ -830,7 +1375,23 @@ ], "presenter": "Vitalii Shapovalov", "source_url": "https://2026symposium.activeinference.institute/", - "title": "ROIF and Active Inference: The Formal Boundary of History-Conditioned Anticipatory Systems.\n\nHistory-dependent reconfiguration, structured memory, and predictive preconfiguration in prestressed complex systems" + "title": "ROIF and Active Inference: The Formal Boundary of History-Conditioned Anticipatory Systems. ​ History-dependent reconfiguration, structured memory, and predictive preconfiguration in prestressed complex systems" + }, + { + "abstract": "How do we understand sentences like “The boy is opening a can of soda”? Accounts of grounded and embodied cognition propose that comprehension is supported by the partial reactivation of sensorimotor states. For instance, understanding the “The boy is opening a can of soda” entails reactivating motor and visual perceptual states congruent with the action described (i.e., pinching). While verbal theories have been offered to account for sensorimotor contributions to comprehension, mathematical and mechanistic models are scant. To address this gap, this study provides a proof-of-concept, hierarchical active inference model of sensorimotor contributions to sentence comprehension. In our study, we simulate sensorimotor simulations in a seminal sentence-picture verification task in which participants read a sentence and then are shown a picture of an agent performing an action that either matches or mismatches the shape of the action implied by the sentence. Behaviourally, participants are slower at deciding that the picture reflects the concepts of the sentence when the shape mismatches, implicating sensorimotor reactivation. Our model simulations show that mismatching shapes cause extended epistemic foraging, leading to slower reaction times. This model provides a mathematical and mechanistic account of embodied cognition, extending verbal theories.", + "abstract_sha256": "1e969c3efb7264e47282ed391dc6758c7bad65a02bdf18007573a19e4148c73d", + "contribution_type": "Pre-recorded Video presentation", + "id": "talk-eb137baf8fa9df10", + "pictures": [ + { + "alt": "Vitória de Souza — submitted presenter image", + "path": "assets/portraits/410aad34512cede66b6eed72f875d8e91753ae5e6275492c7d802322df3286b5.jpg", + "sha256": "410aad34512cede66b6eed72f875d8e91753ae5e6275492c7d802322df3286b5" + } + ], + "presenter": "Vitória de Souza", + "source_url": "https://2026symposium.activeinference.institute/", + "title": "Modelling Language Comprehension with Embodied Active Inference" }, { "abstract": "Severe childhood maltreatment is known to impair brain development. However, this retrospective single-case autoethnography illustrates that an individual with extreme developmental trauma can achieve functional recovery without formal intervention. This study covers the participant’s life from birth to age 28, and the core recovery period begins with cognitive collapse at age 14 and ends with high functional recovery at age 28. This recovery is marked by the participant‑researcher’s ability to produce this manuscript. Consequently, this case shapes a distinct recovery trajectory that existing models do not capture.", @@ -898,7 +1459,7 @@ { "end_utc": "2026-11-12T13:00:00Z", "id": "session-c96e37c40f2f6008", - "kind": "Livestream Keynote", + "kind": "Live Keynote", "presenter": "Sergio Rubin", "start_utc": "2026-11-12T12:00:00Z", "title": "An elementary form of agency at planetary-scale" @@ -906,7 +1467,7 @@ { "end_utc": "2026-11-12T14:00:00Z", "id": "session-980c7fe3b12846e9", - "kind": "Livestream Keynote", + "kind": "Live Keynote", "presenter": "Karl Friston", "start_utc": "2026-11-12T13:00:00Z", "title": "Opening Keynote and Panel" @@ -914,23 +1475,31 @@ { "end_utc": "2026-11-12T15:00:00Z", "id": "session-ac78a95c400be1eb", - "kind": "Livestream", + "kind": "Livestream Session", "presenter": "Ryan Smith", "start_utc": "2026-11-12T14:00:00Z", "title": "Recent advances in empirical applications of active inference in computational psychiatry" }, { - "end_utc": "2026-11-12T16:00:00Z", + "end_utc": "2026-11-12T15:30:00Z", "id": "session-980a444c33f7622d", - "kind": "Livestream", + "kind": "Livestream Session", "presenter": "Bianca Avanzo", "start_utc": "2026-11-12T15:00:00Z", "title": "Root Frequency Theory: An Integrative Framework for the Continuity of Lived Experience" }, + { + "end_utc": "2026-11-12T16:00:00Z", + "id": "session-1c5899f2b33b194d", + "kind": "Livestream Session", + "presenter": "Ted J. Vrioni", + "start_utc": "2026-11-12T15:30:00Z", + "title": "Knowthyself — A code-free active inference workbench" + }, { "end_utc": "2026-11-12T17:00:00Z", "id": "session-b196df2921dc6a17", - "kind": "Livestream", + "kind": "Livestream Session", "presenter": "Ed Ober", "start_utc": "2026-11-12T16:00:00Z", "title": "Working Groups Session 1 ~ Opening & Charter" @@ -938,7 +1507,7 @@ { "end_utc": "2026-11-12T18:00:00Z", "id": "session-921579c396db78dc", - "kind": "Livestream", + "kind": "Livestream Session", "presenter": "Alison Gopnik", "start_utc": "2026-11-12T17:00:00Z", "title": "Active Inference, Bayesian Causal Learning and Empowerment in RL." @@ -951,10 +1520,26 @@ "start_utc": "2026-11-12T18:00:00Z", "title": "A Modular Perspective Layer for Active Inference Agents" }, + { + "end_utc": "2026-11-12T20:30:00Z", + "id": "session-f043ab2fc8467053", + "kind": "Livestream Session", + "presenter": "John Boik, Glen EP Ropella", + "start_utc": "2026-11-12T19:30:00Z", + "title": "CogNarr Story Graph: A Formal Semantics for Narrative as a Belief Model" + }, + { + "end_utc": "2026-11-12T21:30:00Z", + "id": "session-80a7aee761af128e", + "kind": "Livestream Session", + "presenter": "Montijn van Loo", + "start_utc": "2026-11-12T20:30:00Z", + "title": "Explicating and Comparing Prediction Architectures of Theories of Consciousness and its Application to INTREPID" + }, { "end_utc": "2026-11-13T13:00:00Z", "id": "session-643951a3731bf120", - "kind": "Livestream", + "kind": "Livestream Session", "presenter": "Takuya Isomura", "start_utc": "2026-11-13T12:00:00Z", "title": "Reverse engineering generative models from zebrafish decision-making" @@ -962,7 +1547,7 @@ { "end_utc": "2026-11-13T14:00:00Z", "id": "session-0b80cf1a50dd81f2", - "kind": "Livestream", + "kind": "Livestream Session", "presenter": "Majid D. Beni", "start_utc": "2026-11-13T13:00:00Z", "title": "From Self-Evidencing to Global Governance: Active Inference and the Dynamics of Justice" @@ -970,42 +1555,50 @@ { "end_utc": "2026-11-13T15:00:00Z", "id": "session-5ae2ee1184879762", - "kind": "Livestream", + "kind": "Livestream Session", "presenter": "Luca Possati", "start_utc": "2026-11-13T14:00:00Z", "title": "Active Inference and the Evolution of Mind and Society" }, { "end_utc": "2026-11-13T17:00:00Z", - "id": "session-dba90c3d26be06bb", - "kind": "Livestream", + "id": "session-a5374a2c8e3f94db", + "kind": "Livestream Session", "presenter": "Mike Levin", "start_utc": "2026-11-13T15:00:00Z", - "title": "" + "title": "“Evolution, Emergence, Prediction.”" }, { "end_utc": "2026-11-13T19:00:00Z", - "id": "session-06ebf625f802e5cc", + "id": "session-e29c9aff1d3dec95", "kind": "Workshop", - "presenter": "Dan Corva", + "presenter": "Dan Corva, Kevin Backhouse", "start_utc": "2026-11-13T17:00:00Z", - "title": "Build a curious rocket: the hands-on cpomdp tutorial for the Applied Active Inference Symposium" + "title": "Build a curious rocket: a hands-on continuous-state AIF session with cpomdp" }, { "end_utc": "2026-11-13T20:30:00Z", - "id": "session-703a83883944eb1d", + "id": "session-f937cae76e26866e", "kind": "Workshop", "presenter": "Bradly Alicea", "start_utc": "2026-11-13T19:00:00Z", - "title": "An in-depth discussion on “World models in natural and artificial intelligence" + "title": "An in-depth discussion on “World models in natural and artificial intelligence”" }, { "end_utc": "2026-11-13T21:30:00Z", "id": "session-ce8d22b44223b8a3", - "kind": "Livestream", + "kind": "Livestream Session", "presenter": "Ed Ober", "start_utc": "2026-11-13T20:30:00Z", "title": "Working Groups Session 2 ~ Outcomes & Next Steps" + }, + { + "end_utc": "2026-11-13T22:00:00Z", + "id": "session-319fbfcbb775215e", + "kind": "Livestream Session", + "presenter": "Montijn van Loo", + "start_utc": "2026-11-13T21:30:00Z", + "title": "A New Politics: From Linear to Sorted Politics Through the Policy Map" } ] } diff --git a/data/public/2026/publication.json b/data/public/2026/publication.json index 97064bf..ead7fcb 100644 --- a/data/public/2026/publication.json +++ b/data/public/2026/publication.json @@ -1,63 +1,111 @@ { "approval_basis": "published-program-and-affirmative-registration-consent", - "bundle_sha256": "2083941d514a170f1e1ff50008c8b7578796bf2303ec6afbb5423850fea42926", + "bundle_sha256": "4e677e70189dc54a6eb5e585d0e0a6fe3e983e4d87c55da493fef977c3ca0a33", "files": { + "assets/portraits/00f3ab9b83d67462ccea7c6a47117ac7c5da72779236bc3c2257b8a5cfb04b70.jpg": "00f3ab9b83d67462ccea7c6a47117ac7c5da72779236bc3c2257b8a5cfb04b70", + "assets/portraits/014645d9257973adff200505059320c73f69a21a0ef8a14d862a1dfed768c07d.jpg": "014645d9257973adff200505059320c73f69a21a0ef8a14d862a1dfed768c07d", "assets/portraits/0981ae2f473c8da037218b40cdd698824987b1f962cbecf6d98ace96d17e0877.jpg": 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"da5c24e1b6121116b44f04607ab80efc8b081f0ce09001ba3e0be8e9f1eba267", "participants/person-fc33ea9062427010.md": "022cc245f88e0f1c6ba6d9bd9507140331c4e6fe12098e549f1ca8213a406189", "participants/person-ff7277734631ccd5.md": "f28f300d458b334fa8dc8f8df97f59b5bcd96dd08d9cc30832283b58d2a1cfb0", - "program.ics": "7e502bc80fae02e9762141857625d82fe41f8b03fa716f11a03d6408074439f2", - "program.json": "2083941d514a170f1e1ff50008c8b7578796bf2303ec6afbb5423850fea42926" + "program.ics": "98e2d40b52a46eca69e0051dbd545be97ac2378b25af7b1a6ce1342b228a20d7", + "program.json": "4e677e70189dc54a6eb5e585d0e0a6fe3e983e4d87c55da493fef977c3ca0a33" }, "schema_version": 1 } diff --git a/docs/organizing_publication.md b/docs/organizing_publication.md index ebc0ff9..92f67e8 100644 --- a/docs/organizing_publication.md +++ b/docs/organizing_publication.md @@ -96,6 +96,17 @@ An accepted abstract is not evidence of a speaking time. Pending sessions remain pending, and the official program is the revision authority. A published site is a snapshot, not a live synchronization promise. +Typed Docs datetime cells with an `epoch` use finite Unix seconds in UTC; their +localized `input` display text does not determine a session's time. Invalid epochs +are rejected without falling back to display text. Plain timestamp strings still +require an explicit timezone offset. + +An optional event `snapshot_date_utc` supplies the visible program snapshot stamp. +Use the source capture's UTC calendar date; staging rejects a mismatched date. +Older approved bundles without this field remain readable and are not assigned +a guessed capture date. The live public program, including its Sessions page, +remains the revision authority. + `publication.stage_public(..., apply=True)` rechecks the snapshot, its age, the projection, the private ledger, approved media and artifact bindings before creating a new public tree. The default snapshot freshness limit is one hour; the API permits @@ -171,7 +182,9 @@ staging directory as `book.pdf` and `book.docx`. The renderer applies a document process-local Template compatibility policy to preserve literal tilde and middle-dot characters, fails if the expected Template contract changes, and sets Word page geometry to A4 with 25 mm margins. It records original/native and final -artifact hashes. The PDF has a linked contents section; the editable Word version +artifact hashes. The PDF uses staged, hash-recorded DejaVu Serif font files from +the declared Matplotlib dependency; missing-glyph compiler diagnostics refuse +render success instead of silently dropping submitted characters. The PDF has a linked contents section; the editable Word version uses heading styles and has no generated contents section or printed page numbers. ```bash diff --git a/pyproject.toml b/pyproject.toml index b3c3a98..43badf5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "symposium" -version = "3.1.0" +version = "3.2.0" description = "Research analysis and project generation for Active Inference Symposium" authors = [{name = "Active Inference Institute"}] requires-python = ">=3.12" diff --git a/src/symposium/__init__.py b/src/symposium/__init__.py index 5bfb88e..82dceaf 100644 --- a/src/symposium/__init__.py +++ b/src/symposium/__init__.py @@ -4,7 +4,7 @@ project proposals, and visualizing research networks for the Active Inference Symposium. """ -__version__ = "3.1.0" +__version__ = "3.2.0" from symposium.calendar.exporter import ScheduleExporter from symposium.core.api import APIClient diff --git a/src/symposium/organizing/artifacts.py b/src/symposium/organizing/artifacts.py index d14fd74..6f6cf3d 100644 --- a/src/symposium/organizing/artifacts.py +++ b/src/symposium/organizing/artifacts.py @@ -12,6 +12,8 @@ from urllib.parse import quote from xml.etree import ElementTree as ET +from matplotlib import get_data_path + from .models import PublicBundle, PublicPresentation, validate_bundle @@ -128,8 +130,28 @@ def stage_abstract_book(bundle: PublicBundle, workspace: Path) -> Path: for talk in talks ) _write(manuscript / "01_abstract_book.md", text) + # Use package-owned font bytes: Latin Modern drops source glyphs such as + # the Hawaiian okina. Copy all faces so the native writer needs no host font. + fonts = project / "fonts" + fonts.mkdir() + font_source = Path(get_data_path()) / "fonts" / "ttf" + font_hashes = {} + for suffix in ("", "-Bold", "-Italic", "-BoldItalic"): + name = f"DejaVuSerif{suffix}.ttf" + payload = (font_source / name).read_bytes() + (fonts / name).write_bytes(payload) + font_hashes[name] = hashlib.sha256(payload).hexdigest() + _write( + manuscript / "preamble.md", + "```latex\n" + + r"\setmainfont{DejaVuSerif}[Path={" + + fonts.as_posix() + + r"/},Extension=.ttf,UprightFont=*,BoldFont=*-Bold,ItalicFont=*-Italic,BoldItalicFont=*-BoldItalic]" + + "\n```\n", + ) manifest = { "schema_version": 1, + "pdf_font_files": font_hashes, "event_id": event["id"], "talk_count": len(talks), "talks": [{"id": talk["id"], "abstract_sha256": talk["abstract_sha256"]} for talk in talks], @@ -179,6 +201,14 @@ def _set_docx_page_geometry(path: Path) -> None: target.replace(path) +def _check_pdf_glyphs(log: Path) -> None: + """Refuse native PDF success when the compiler lost any source glyph.""" + if not log.is_file(): + raise ArtifactError("Native PDF glyph log is unavailable") + if "Missing character:" in log.read_text(encoding="utf-8", errors="replace"): + raise ArtifactError("Native PDF has missing glyphs; inspect the retained compiler log") + + def render_abstract_book( bundle: PublicBundle, workspace: Path, @@ -276,6 +306,7 @@ def render_abstract_book( for kind, path in outputs.items(): if not path.is_file() or path.stat().st_size == 0: raise ArtifactError(f"Native render missing nonempty {kind}: {path}") + _check_pdf_glyphs(project / "output" / "pdf" / "_combined_manuscript.log") native_docx_sha256 = hashlib.sha256(outputs["docx"].read_bytes()).hexdigest() _set_docx_page_geometry(outputs["docx"]) manifest = json.loads((project / "abstract_manifest.json").read_text(encoding="utf-8")) diff --git a/src/symposium/organizing/models.py b/src/symposium/organizing/models.py index 61f042e..83dc8f2 100644 --- a/src/symposium/organizing/models.py +++ b/src/symposium/organizing/models.py @@ -7,7 +7,7 @@ import re from datetime import date, datetime from pathlib import Path, PurePosixPath -from typing import Any, TypedDict, cast +from typing import Any, NotRequired, TypedDict, cast from urllib.parse import urlsplit SCHEMA_VERSION = 1 @@ -52,6 +52,7 @@ class PublicEvent(TypedDict): community_url: str donate_url: str livestreams: list[PublicLink] + snapshot_date_utc: NotRequired[str] class PublicPresentation(TypedDict): @@ -210,8 +211,17 @@ def validate_bundle(value: object) -> PublicBundle: bundle = _record(value, fields, "Bundle") if type(bundle["schema_version"]) is not int or bundle["schema_version"] != SCHEMA_VERSION: raise PublicationError("Unsupported public interchange schema version") - event_fields = set(PublicEvent.__annotations__) + event_fields = set(PublicEvent.__annotations__) - {"snapshot_date_utc"} + if isinstance(bundle["event"], dict) and "snapshot_date_utc" in bundle["event"]: + event_fields.add("snapshot_date_utc") event = _record(bundle["event"], event_fields, "Event") + if "snapshot_date_utc" in event: + stamp = _text(event["snapshot_date_utc"], "Snapshot date", required=True) + try: + if date.fromisoformat(stamp).isoformat() != stamp: + raise ValueError("Noncanonical calendar date") + except ValueError as exc: + raise PublicationError("Snapshot date must be an ISO calendar date") from exc if type(event["year"]) is not int or not 2000 <= event["year"] <= 2100: raise PublicationError("Event year is invalid") for field in event_fields - {"year", "livestreams"}: diff --git a/src/symposium/organizing/projection.py b/src/symposium/organizing/projection.py index b380063..51e7ef9 100644 --- a/src/symposium/organizing/projection.py +++ b/src/symposium/organizing/projection.py @@ -53,6 +53,18 @@ def cell_text(value: Any, *, depth: int = 0) -> str: if isinstance(value, dict): if "value" in value: return cell_text(value["value"], depth=depth + 1) + if value.get("type") == "dt" and "epoch" in value: + epoch = value["epoch"] + if type(epoch) not in (int, float) or not -62135596800 <= epoch < 253402300800: + raise PublicationError("Typed datetime requires finite Unix epoch seconds") + try: + return ( + datetime.fromtimestamp(epoch, timezone.utc).isoformat().replace("+00:00", "Z") + ) + except (OverflowError, OSError, ValueError) as exc: + raise PublicationError( + "Typed datetime epoch is outside the supported range" + ) from exc if value.get("type") == "dt" and isinstance(value.get("input"), str): return value["input"] if isinstance(value.get("name"), str): diff --git a/src/symposium/organizing/publication.py b/src/symposium/organizing/publication.py index 9cccbd0..358f31d 100644 --- a/src/symposium/organizing/publication.py +++ b/src/symposium/organizing/publication.py @@ -361,6 +361,12 @@ def stage_public( if type(max_age_seconds) is not int or not 0 < max_age_seconds <= 86400: raise PublicationError("Snapshot age limit must be positive and at most one day") observed = _utc(snapshot.get("observed_at")) + if ( + "snapshot_date_utc" in bundle["event"] + and bundle["event"]["snapshot_date_utc"] + != observed.astimezone(timezone.utc).date().isoformat() + ): + raise PublicationError("Snapshot date differs from source capture date") now = _utc(as_of) if as_of else datetime.now(timezone.utc) age = (now - observed).total_seconds() if age > max_age_seconds or age < -300: diff --git a/src/symposium/organizing/review.py b/src/symposium/organizing/review.py index a2d9080..9cc5a73 100644 --- a/src/symposium/organizing/review.py +++ b/src/symposium/organizing/review.py @@ -157,7 +157,9 @@ def review_update(previous_public_dir: Path, candidate_public_dir: Path) -> Upda "snapshot_integrity": "both_verified", "source_freshness": "not_checked", "event_changed_fields": sorted( - key for key in before["event"] if before["event"][key] != after["event"][key] + key + for key in before["event"].keys() | after["event"].keys() + if before["event"].get(key) != after["event"].get(key) ), "records": records, "artifacts": { diff --git a/src/symposium/organizing/site.py b/src/symposium/organizing/site.py index 98636f3..8360f77 100644 --- a/src/symposium/organizing/site.py +++ b/src/symposium/organizing/site.py @@ -104,6 +104,14 @@ def build_site( for name in ("site.css", "site.js", "favicon.svg"): copies[f"static/{name}"] = (Path(__file__).parent / "static" / name).read_bytes() event = bundle["event"] + stamp = event.get("snapshot_date_utc") + snapshot_notice = ( + f'

. ' + + _link(event["program_url"], "Live public program — revision authority") + + ". For the live schedule, open its Sessions page.

" + if stamp + else "" + ) abstract_pending = sum(not p["abstract"].strip() for p in bundle["presentations"]) title_pending = sum(not s["title"].strip() for s in bundle["sessions"]) pending = [] @@ -200,7 +208,7 @@ def filters(prefix: str, categories: list[str] | None = None) -> str: document = f""" {_escape(event["title"])} · Active Inference Institute{event_metadata(bundle, base)}
Active Inference InstituteSymposium
-

Annual Symposium

{_escape(event["title"])}

{_escape(event["start_date"])} — {_escape(event["end_date"])} · UTC

{actions}

Downloads & working-book guidance

{pending_summary}
+

Annual Symposium

{_escape(event["title"])}

{_escape(event["start_date"])} — {_escape(event["end_date"])} · UTC

{snapshot_notice}
{actions}

Downloads & working-book guidance

{pending_summary}

The research

Presentations & abstracts

Full abstracts from the approved program. Open a presentation to read and explore its sources. Missing abstracts are marked pending.

{filters("presentations", types)}{"".join(rows) or "

No approved presentations published yet.

"}

The community

Participant directory

Self-reported interests, applications, and learning needs shared for this public directory. Available participant briefs appear inside each profile.

{filters("participants")}{"".join(participant_rows) or "

No approved participant profiles published yet.

"}

Gather together

Schedule & live sessions

Only explicitly scheduled sessions are shown. Abstracts do not imply a speaking time.

{'' if sessions else ""}{"".join(sessions) or "

Session times have not been published in this bundle. See the official program for updates.

"}
{livestreams}
diff --git a/src/symposium/organizing/static/site.js b/src/symposium/organizing/static/site.js index b98741c..5ac49e3 100644 --- a/src/symposium/organizing/static/site.js +++ b/src/symposium/organizing/static/site.js @@ -31,7 +31,7 @@ if (timezone) { const options = {dateStyle: "medium", timeStyle: "short"}; if (timezone.value === "UTC") options.timeZone = "UTC"; const zone = timezone.value === "UTC" ? "UTC" : Intl.DateTimeFormat().resolvedOptions().timeZone; - document.querySelectorAll("time[datetime]").forEach(time => { + document.querySelectorAll("#schedule time[datetime]").forEach(time => { time.textContent = `${new Intl.DateTimeFormat(undefined, options).format(new Date(time.dateTime))} ${zone}`; }); }; diff --git a/tests/test_organizing_artifacts.py b/tests/test_organizing_artifacts.py index df8c323..2d35f6f 100644 --- a/tests/test_organizing_artifacts.py +++ b/tests/test_organizing_artifacts.py @@ -158,3 +158,39 @@ def test_book_has_one_cross_format_page_break_per_contribution(tmp_path): assert "{=openxml}" not in source assert "Public ID:" not in source and "Abstract SHA256:" not in source assert data["presentations"][0]["abstract"] in html.unescape(source) + + +def test_staged_font_preserves_source_okina_and_spacing(tmp_path): + from matplotlib.ft2font import FT2Font + + data = bundle() + abstract = "Hawaiʻi uses thin\u2009space." + data["presentations"][0]["abstract"] = abstract + data["presentations"][0]["abstract_sha256"] = hashlib.sha256(abstract.encode()).hexdigest() + project = stage_abstract_book(data, tmp_path / "unicode") + manifest = json.loads((project / "abstract_manifest.json").read_text()) + assert manifest["pdf_font_files"] + for name, digest in manifest["pdf_font_files"].items(): + font = project / "fonts" / name + assert hashlib.sha256(font.read_bytes()).hexdigest() == digest + face = FT2Font(str(font)) + assert all(face.get_char_index(ord(c)) for c in abstract if not c.isspace()) + assert face.get_char_index(0x2009) + + +@pytest.mark.parametrize("state", ["clean", "missing-glyph", "missing-log"]) +def test_native_pdf_glyph_check_requires_real_clean_log(tmp_path, state): + from symposium.organizing.artifacts import _check_pdf_glyphs + + log = tmp_path / "compiler.log" + if state != "missing-log": + log.write_text( + "Output written." + if state == "clean" + else "Missing character: There is no ʻ (U+02BB) in font" + ) + if state == "clean": + _check_pdf_glyphs(log) + else: + with pytest.raises(ArtifactError): + _check_pdf_glyphs(log) diff --git a/tests/test_organizing_core.py b/tests/test_organizing_core.py index f122d06..b6b74be 100644 --- a/tests/test_organizing_core.py +++ b/tests/test_organizing_core.py @@ -528,3 +528,57 @@ def test_invalid_source_values_excluded_with_reason(inputs): bundle, ledger = reproject(inputs) assert not bundle["participants"] assert any(e["kind"] == "participant" for e in ledger["excluded"]) + + +@pytest.mark.parametrize( + ("epoch", "expected"), + [(1793534400, "2026-11-01T12:00:00Z"), (1793534400.5, "2026-11-01T12:00:00.500000Z")], +) +def test_typed_datetime_uses_utc_epoch(epoch, expected): + assert cell_text({"type": "dt", "epoch": epoch, "input": "11/1/2026 12:00 PM"}) == expected + + +@pytest.mark.parametrize( + "epoch", + [ + True, + None, + "1793534400", + float("nan"), + float("inf"), + float("-inf"), + 10**1000, + -62135596801, + 253402300800, + ], +) +def test_invalid_typed_epoch_never_falls_back_to_input(epoch): + with pytest.raises(PublicationError): + cell_text({"type": "dt", "epoch": epoch, "input": "2026-11-01T12:00:00Z"}) + + +def test_typed_utc_session_reaches_public_schedule(inputs): + set_cell( + inputs[1], + "sessions", + "start_utc", + {"type": "dt", "epoch": 1793534400, "input": "11/1/2026 12:00 PM"}, + ) + set_cell( + inputs[1], + "sessions", + "end_utc", + {"type": "dt", "epoch": 1793538000, "input": "11/1/2026 1:00 PM"}, + ) + bundle, ledger = reproject(inputs) + assert [(s["start_utc"], s["end_utc"]) for s in bundle["sessions"]] == [ + ("2026-11-01T12:00:00Z", "2026-11-01T13:00:00Z") + ] + assert not [r for r in ledger["excluded"] if r["kind"] == "session"] + + +@pytest.mark.parametrize("stamp", ["2026-02-30", "20261101", "2026-11-01T12:00:00Z", "", None]) +def test_invalid_snapshot_date_rejected(inputs, stamp): + inputs[0]["event"]["snapshot_date_utc"] = stamp + with pytest.raises(PublicationError): + validate_bundle(inputs[0]) diff --git a/tests/test_organizing_publication.py b/tests/test_organizing_publication.py index e2e76a9..56a604b 100644 --- a/tests/test_organizing_publication.py +++ b/tests/test_organizing_publication.py @@ -513,3 +513,38 @@ def test_pdf_initial_view_rejects_actions_or_nonpage_destinations(inputs, tmp_pa (artifacts / "book.pdf").write_bytes(pdf_fixture(catalog_extra="/OpenAction " + view)) with pytest.raises(PublicationError): write_artifact_index(inputs[0], artifacts) + + +def test_snapshot_stamp_bound_to_source_capture(inputs, tmp_path): + bundle, snapshot, _ = inputs + event = {**bundle["event"], "snapshot_date_utc": "2026-10-25"} + projected, ledger = project_snapshot(snapshot, event, bundle["resources"]) + with pytest.raises(PublicationError, match="Snapshot date"): + stage((projected, snapshot, ledger), tmp_path / "rejected-date") + assert not (tmp_path / "rejected-date").exists() + + +def test_snapshot_stamp_accepts_matching_utc_day(inputs, tmp_path): + bundle, snapshot, _ = inputs + snapshot["observed_at"] = "2026-10-26T01:30:00Z" + event = {**bundle["event"], "snapshot_date_utc": "2026-10-26"} + projected, ledger = project_snapshot(snapshot, event, bundle["resources"]) + destination = tmp_path / "matching-date" + stage_public(projected, snapshot, ledger, destination, apply=True, as_of="2026-10-26T01:30:01Z") + assert verify_public_tree(destination)["event"]["snapshot_date_utc"] == "2026-10-26" + + +def test_snapshot_capture_retains_explicit_utc_contract(inputs, tmp_path): + bundle, snapshot, _ = inputs + snapshot["observed_at"] = "2026-10-25T23:30:00-02:00" + event = {**bundle["event"], "snapshot_date_utc": "2026-10-26"} + projected, ledger = project_snapshot(snapshot, event, bundle["resources"]) + with pytest.raises(PublicationError, match="Expected explicit UTC timestamp"): + stage_public( + projected, + snapshot, + ledger, + tmp_path / "non-utc-capture", + apply=True, + as_of="2026-10-26T01:30:01Z", + ) diff --git a/tests/test_organizing_review.py b/tests/test_organizing_review.py index fbd9efd..9339be8 100644 --- a/tests/test_organizing_review.py +++ b/tests/test_organizing_review.py @@ -207,3 +207,15 @@ def test_empty_program_is_not_content_complete(inputs, tmp_path, capsys): assert report["warnings"] == ["No presentations have been published."] assert invoke("readiness", "--public-dir", tree, "--require-complete") == 1 assert json.loads(capsys.readouterr().out)["content_complete"] is False + + +@pytest.mark.parametrize("reverse", [False, True]) +def test_review_reports_optional_snapshot_date_added_or_removed(inputs, tmp_path, reverse): + old, new = tmp_path / "before", tmp_path / "after" + stage(inputs, old) + original, snapshot, _ = inputs + event = {**original["event"], "snapshot_date_utc": "2026-10-26"} + bundle, ledger = project_snapshot(snapshot, event, original["resources"]) + stage((bundle, snapshot, ledger), new) + report = review_update(new, old) if reverse else review_update(old, new) + assert report["event_changed_fields"] == ["snapshot_date_utc"] diff --git a/tests/test_organizing_site.py b/tests/test_organizing_site.py index b2ad8e7..9565d0f 100644 --- a/tests/test_organizing_site.py +++ b/tests/test_organizing_site.py @@ -313,7 +313,8 @@ def test_public_bundle_build_preserves_source_bytes(tmp_path): assert len( [attrs for _, attrs in tags if attrs.get("id", "").startswith("presentation-")] ) == len(bundle["presentations"]) - assert "2 abstracts pending; 1 session title pending" in index.read_text() + assert "3 abstracts pending" in index.read_text() + assert "session title pending" not in index.read_text() assert (index.parent / "program.json").read_bytes() == canonical_bytes(validate_bundle(bundle)) @@ -334,3 +335,15 @@ def test_whitespace_abstract_has_readable_pending_body(bundle, tmp_path): bundle["presentations"][0]["abstract_sha256"] = text_hash(" \n ") source = build_site(bundle, tmp_path / "site").read_text() assert "No program abstract has been published for this presentation yet." in source + + +def test_snapshot_stamp_and_live_authority(bundle, tmp_path): + bundle["event"]["snapshot_date_utc"] = "2026-11-01" + source = build_site(bundle, tmp_path / "dated-site").read_text() + notice = source.split('

', 1)[1].split("

", 1)[0] + tags = Tags(notice).tags + assert any(tag == "time" and attrs.get("datetime") == "2026-11-01" for tag, attrs in tags) + assert "Program snapshot as of 2026-11-01 (UTC)" in source + assert any( + tag == "a" and attrs.get("href") == bundle["event"]["program_url"] for tag, attrs in tags + ) diff --git a/uv.lock b/uv.lock index 44b8a0b..5ca5dea 100644 --- a/uv.lock +++ b/uv.lock @@ -1769,7 +1769,7 @@ wheels = [ [[package]] name = "symposium" -version = "3.1.0" +version = "3.2.0" source = { editable = "." } dependencies = [ { name = "adjusttext" },