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The Active Inference Institute is a volunteer-led 501(c)(3) nonprofit dedicated to improving the accessibility, rigor, and applicability of Active Inference. We welcome people of all backgrounds, time zones, and familiarity levels.
Active Inference is the central scientific and practical framework around which the Institute organizes education, research, applications, and ecosystem support.
The Active Inference Institute is organized around a governing Board of Directors, executive Officers, a Scientific Advisory Board, and two operational units for education and research.
Activities are the Institute's live participation surface: learning groups, model streams, project meetings, public media, and recurring updates.
The Institute's history runs from a 2020 co-founder team meeting around a shared interest in Active Inference, through the Active Inference Lab, to the nonprofit Active Inference Institute it is today. This page traces that story year by year. For the current and upcoming years, see the 2025 and 2026 annual overviews.
InstituteOS keeps operational knowledge in the private docs and library trees, then exports only public-safe slices into this static website. This page makes the interface explicit: authored public copy, sanitized public tables, exported graphs, public project pages, and the checks that block private material before publication.
Institute Programs turn interest into participation through volunteer, internship, mentorship, fellowship, partnership, philanthropy, grants, and open-source pathways. All programs are open to people of any background and familiarity level.
Projects convert shared ideas into repositories, research outputs, learning infrastructure, media, events, and applied work.
Learning resources point newcomers and experienced participants toward living references, livestreams, Active Inference Insights, textbook groups, readings, code implementations, and domain-specific courses.
The Active Inference ecosystem includes challenge areas, user segments, information architecture, organizations, projects, and domains of application.
All backgrounds, time zones, and familiarity levels are welcome. Start with public channels, attend activities, and choose a contribution pathway that fits your context.
An internship is a structured contribution pathway — work on real Institute projects, develop skills, and build relationships with researchers and educators in the Active Inference field.
Volunteering is how most people begin participating in the Institute. There is no expertise threshold — contributions at every level of familiarity with Active Inference are meaningful and welcome.
The Board of Directors provides formal governance, fiduciary oversight, strategic direction, and institutional accountability for the Active Inference Institute.
The Active Inference Institute proposes a system of open community working groups: one Field Working Group for field-level comparison and a shared assessment method, and domain working groups that pair active-inference experts with domain practitioners. The draft Charter and Operating Procedures are open for public review on GitHub now. The groups are proposed for founding at the Charter Development Session of the 6th Applied Active Inference Symposium, held online on 12-14 November 2026.
Mentorship at the Institute runs in both directions — experienced contributors support the development of newer participants, and everyone benefits from the exchange of perspectives, context, and guidance.
The Officers of the Active Inference Institute are responsible for executing the Institute's day-to-day operations, administration, and finances under the governance of the Board of Directors.
Fellows develop research, applications, or public resources aligned with the Institute and Ecosystem, provide regular progress reports, and produce outputs that advance the field and remain publicly accessible.
The Scientific Advisory Board (SAB) provides scientific, technical, and scholarly guidance to the Active Inference Institute, advising on research direction, rigor, and the development of the field.
The Institute supports grant proposals and collaborative funding efforts that advance Active Inference research, education, and application. Participants and projects can seek Institute support for proposal preparation, institutional affiliation, and co-investigation.
Partnerships align organizations and collaborators around concrete shared work — co-hosting events, co-authoring research, sharing infrastructure, or developing joint education and application programs.
The Active Inference Institute is a registered 501(c)(3) nonprofit. Donations from individuals and organizations directly fund public education, research infrastructure, community events, and ecosystem stewardship.
EduActive is the Institute's education arm. It hosts the Textbook Group, courses, ontology work, the Active Inference Journal, audio-visual production, seasonal school, and related learning initiatives. EduActive projects connect Active Inference to learning infrastructure, pedagogy, and public educational resources.
ReInference is the Institute's research and development arm. It hosts software projects, theoretical work, applied modeling, geospatial analysis, knowledge engineering, and interdisciplinary research under the Active Inference framework. ReInference projects range from widely-used open-source tools to specialized research initiatives.
The default license for Institute materials — including software repositories, research outputs, and other public work products — is Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). Specific products and collaborations may use different terms; check the individual repository or resource for details.
Active Blockference develops Active Inference examples and tools applicable to decentralized and blockchain-adjacent systems. The project has produced a GitHub repository, blog posts, and video overviews, and serves as an integration point for multiagent modeling work from Active InferAnts.
AICACP — the AI Capabilities & Alignment Consensus Project — is a multi-year initiative designed to reshape the conversation around AI capabilities, alignment, and regulation. By combining high-impact journal collections, in-person discussion-oriented workshops, and academic media content for public outreach, the project aims to bridge the divide between AI “doomers” and “accelerationists” by deeply exploring the meanings of “world models” and “agency” — and what these concepts mean for AI development.
The Applied Active Inference Symposium is a yearly online event that convenes the global Active Inference community. It has run five times from 2021 through 2025, and the 6th Symposium takes place on 12-14 November 2026. Each edition features research presentations, panels, workshops, and collaborative sessions spanning computational neuroscience, AI, robotics, ecology, economics, health, education, and other domains, with full recordings and proceedings published openly afterward.
Cognitive Agent Modeling is a ReInference Institute project focused on developing minimal cognitive agent models grounded in Active Inference. The work explores how perception, action, and learning can be formalized and implemented using the Active Inference framework.
FarmWorks develops miniature Active Inference models and applications for agricultural and ecological contexts. The project has produced a public FarmWorks page and a 2024 publication describing the approach of treating farm and soil systems as active inference agents.
Generalized Notation Notation (GNN) is a text-based notation project for communicating and specifying generative models. It provides a shared language for describing Active Inference and related models in a way that is both human-readable and machine-processable, enabling interoperability across tools and codebases.
GEO-INFER develops geospatial modeling methods grounded in Active Inference. The project integrates spatial data analysis with the Active Inference framework, opening applications in ecology, urban planning, resource management, and other domains where geographic context matters.
The Graphical Interface project develops visual and interactive tools for working with Active Inference models. It focuses on making model structure, dynamics, and outputs more accessible through well-designed graphical interfaces and visualization layers.
Knowledge Engineering develops and maintains the public knowledge infrastructure for the Active Inference Institute, including the public frontend, literature meta-analysis, and organizational knowledge systems. The project connects Institute outputs to the broader literature and makes them machine-readable and navigable.
Active InferAnts is a multiagent modeling project that applies Active Inference to collective behavior, inspired by ant colony dynamics. It has produced a GitHub repository, a 2021 paper, and code developed within the Active Blockference project, with multiple realizations across different modeling contexts.
The RxInfer.jl Learning and Development Group supports learning and extending RxInfer.jl, a Julia package for reactive Bayesian inference. The group produces learning resources, code examples, and contributes to the broader development of RxInfer as a platform for Active Inference.
The Theoretical Neurobiology (TNB) Group has fostered interdisciplinary research and collaboration for decades. Its mission is to advance the understanding and application of active inference — the theoretical framework developed by Prof. Karl Friston — through regular online meetings featuring presentations and discussions that may include empirical data and analysis, simulations, and mathematical development. The group welcomes contributions from neuroscience, mathematics, machine learning, psychology, philosophy, medicine, and biology.
Active Inference for Social Sciences develops courses, curricula, research, and writing connecting Active Inference to the social sciences. The project produced a 2023 course and continues to build educational and research resources for social scientists engaging with the Active Inference framework.
The Active Inference Journal is an Institute publication and community knowledge channel launched in 2021. It supports the development and dissemination of Active Inference research, discussion, and learning through volunteer-led editorial work and open publishing.
The Active Inference Ontology project maintains and extends the public ontology for the Active Inference framework. It provides structured, machine-readable definitions of concepts and their relationships, supporting decentralized science, reproducibility, and knowledge reuse.
Audio-Visual Production is a sustained Institute project responsible for planning, recording, and publishing the Institute's livestreams, podcasts, video events, and educational recordings. The project has produced a continuously updated table of all livestreams and videos from 2020 onward.
Educational Course Development is a sustained Institute project producing structured courses in Active Inference and related topics. It maintains an Obsidian repository and course catalog, developing educational materials for a range of backgrounds and learning goals.
The Physics Course is a historical, archived 2023 educational lecture series that explored physical and thermodynamic foundations of Active Inference and the Free Energy Principle. It is preserved for archival reference and is not an active or ongoing Institute educational offering.
The Seasonal School is an Institute educational program providing intensive, structured, and in-depth engagement with Active Inference theory, modeling, and applications. It has run multiple cohorts and developed a track record as a concentrated learning experience for participants from varied backgrounds.
The Textbook Group is a sustained Institute educational program running structured cohort-based learning through Active Inference textbooks. Since 2022 it has run 9 cohorts on the 2022 textbook 'Active Inference: The Free Energy Principle in Mind, Brain, and Behavior' by Thomas Parr, Giovanni Pezzulo, and Karl J. Friston. As of mid-2026 the group is live in its first cohort on the 2026 textbook 'Fundamentals of Active Inference: Principles, Algorithms, and Applications of the Free Energy Principle for Engineers' by Sanjeev V. Namjoshi.
The Video Improvement Project focuses on enhancing the quality, organization, and accessibility of the Institute's extensive video library — covering hundreds of livestreams, educational sessions, and project recordings from 2020 onward.
This Ecosystem project develops a formal Active Inference account of belief updating in PTSD. It applies predictive processing and free energy frameworks to model how traumatic experience disrupts normal belief updating, offering a principled theoretical basis for understanding and potentially treating PTSD.
This page presents the complete structured outputs of the Active Inference Meta-Analysis project — a computational meta-analysis of 817 papers across the Active Inference and Free Energy Principle literature (2005–2026). The project employs multi-source retrieval from arXiv, Semantic Scholar, and OpenAlex; LLM-based assertion extraction into Nanopublications; citation-weighted hypothesis scoring; NMF topic modeling; and subfield classification. All 71 curated references are listed with full bibliographic information and DOI links.
This Ecosystem project develops an Active Inference agent model of human translation processes. It applies the free energy framework to the cognitive and linguistic tasks involved in translating between languages, producing models of human translation behavior.
Anima is an Ecosystem project exploring how Active Inference principles apply to interactions with large language models. It examines how policy-based, blanket-aware Active Inference architectures can inform the design and understanding of current AI systems.
Artificial Sentience is an Ecosystem project examining the theoretical conditions for sentience in artificial systems. Drawing on Active Inference, it explores what it would mean for a machine to be sentient, using the free energy principle as a framework for understanding experience and self-organization.
Clinical Waveform Data Based Agent develops Active Inference systems for bedside clinical monitoring. It applies the free energy framework to real-time waveform data — plethysmograph, arterial pressure line, and ventilator curves — to support clinical decision-making in a pediatric intensive care setting.
CogNarr (Cognitive Narrative) is an Ecosystem project developing infrastructure for facilitating group cognition at scale. It builds tools and frameworks that enable communities to coordinate shared understanding through structured narrative and cognitive scaffolding, with an initial focus on minimal viable incubation.
Energy Modeling Human Brain Metabolism applies Active Inference and the Free Energy Principle to model metabolic processes in the human brain. The project develops quantitative models of how the brain manages energy resources as an inference problem.
This Ecosystem project turns a robotic microscope into an Active Inference-driven intelligent agent for managing soil biology at scale. It develops AI systems that autonomously operate microscopy hardware, acquire data, and make decisions — treating the instrument as an inference agent in a biological environment.
Geometric Inquiry Theory develops a geometric framework for understanding inquiry as a structured dynamic process — establishing Q-State Dynamics and the structural basis of inquiry as a coherent mathematical theory, drawing on a multi-decade background spanning paramedicine, network engineering, and culinary arts as informal laboratories.
Graphspeak / Blorbbe is an Ecosystem project developing open-source tools for graph-based communication and knowledge representation, aiming to make structured, relational knowledge more accessible and usable for broad audiences.
Humanity's Story of an Uncertain Self is an Ecosystem project developing a broad account of human self-knowledge as an inference problem. Drawing on Active Inference, it examines how humans construct and maintain stories about themselves as agents in an uncertain world.
This Ecosystem project improves the model visualization capabilities of RxInfer.jl — developing tools that make it easier to see, explore, and understand the structure and dynamics of generative models built with the RxInfer system.
HDPLS-TARS is a speculative and exploratory Ecosystem project testing the limits and foundations of M-Theory through the lens of hyper-dimensional field interconnections. It examines new functional and experiential insights by treating M-Theory as a function of prismatic light scattering dynamics.
Model-Centric Cognition is an Ecosystem project developing a model-centric theory of cognition anchored in the Wave Hypothesis. It examines how cognitive systems represent and update internal models, drawing on Active Inference and existing wave-based theoretical traditions.
Project Sweet (Sus) Dogg applies Active Inference principles to understanding and improving human-animal relationships — particularly focusing on alignment and trust-building in interactions between humans and domestic animals.
Symbolic Cognitive Robotics is an Ecosystem project applying Active Inference to robotic systems that combine symbolic reasoning with embodied action. Work draws on papers in robotics and embodied cognition, and includes implementation on physical robot platforms.
This Ecosystem project develops a formal bridge between the Einstein model of a solid and the mental apparatus, viewed through the economic perspective of psychoanalytic theory. It applies Active Inference to bridge psychoanalytic concepts with modern neuroscience and artificial intelligence.
The Universal Basic Income Experiment applies Active Inference to study Universal Basic Income (UBI) — using token economics, policy simulation, and behavioral modeling to examine UBI's effects on human flourishing and economic dynamics.
Action Research on Collective Foraging (Negotiation Affordances) applies Active Inference to collective foraging behavior — studying how groups form coalitions, negotiate opportunities, and sustain value exchanges. The project has a focus on long-term sustainability and social dynamics.
The Active Inference Cycle Book for Self-Knowing develops a practical framework for personal growth and self-knowledge grounded in Active Inference. It uses the inference cycle — perceiving, modeling, acting, learning — as a scaffold for reflective practice and long-term personal development.
This Ecosystem project develops accounts of creativity and creative agents under the Free Energy Principle — examining how generative models and free energy minimization explain, predict, and enhance creative behavior.
Froebel's System studies the educational philosophy and methods of Friedrich Froebel — the inventor of kindergarten — through the lens of Active Inference and integral studies. The project captures and analyzes Froebel's approach as a cohort-based study, using Common Concepts as a prototyping platform.
Fundamentals of Active Inference is an Ecosystem project supporting the development and dissemination of the Fundamentals of Active Inference textbook — a comprehensive introduction to Active Inference principles, algorithms, and applications for engineers. The Institute hosts a Textbook Group cohort working through this book.
MathArt Conversations is an Ecosystem project creating a space for exploring the profound connections between mathematics and the arts. Through collaborative conversations, streams, and shared inquiry, it surfaces deep structural resonances between mathematical structures and artistic creativity.
Neurodivergent Learning Sessions develops Active Inference learning resources and sessions designed for neurodivergent participants — building curriculum, milestones, and community structures that support autistic, ADHD, and other neurodivergent learners in engaging deeply with Active Inference.
Numinia is an Ecosystem project developing an autonomous AI and educational adventure game grounded in Active Inference principles. The first mission embeds Active Inference in the values of the Numinia agent, creating an environment where players and agents co-learn through play.
This Ecosystem project develops a framework for solving the Tower of Babel Problem — the challenge of cross-domain knowledge communication and translation. Drawing on philosophical and physical principles, it outlines, develops, and implements UniFysica Philo-sophia as a universal conceptual bridge.
The Three Mosqueteers is an Ecosystem project creating a live science communication show for people without a scientific background. It develops a format that makes scientific information — including Active Inference and related work — engaging and genuinely accessible.
Healthcare concerns the regulation of biological states, the reduction of uncertainty about disease processes, and the continual selection of diagnostic and therapeutic actions under incomplete evidence — a set of problems that maps directly onto active inference's core constructs of generative models, prediction error, expected free energy, and policy selection. Over the past decade a literature has grown around computational psychiatry, interoception and pain, digital twins, medical robotics, and — most recently — the safety of large language models used as clinical decision-support tools. Most of this work remains theoretical or simulation-based, but reviews in computational psychiatry and interoceptive psychopathology have begun to consolidate the field, and conceptual roadmaps for "Active Inference AI" in medicine sketch how the framework could reach digital twins, precision diagnostics, and clinical decision support.
Robots continuously perceive, act, and learn under uncertainty using noisy, multimodal sensors and imperfect actuators — precisely the conditions the free energy principle was formulated to describe. A growing but still heterogeneous literature has explored active inference as a way to unify state estimation, control, planning, and model learning in robotic systems, spanning theoretical papers, simulated proofs of concept, and experiments on physical humanoids and manipulators. This page synthesizes that literature's application patterns, named tools and labs, and the open problems that separate compelling demonstrations from a routinely deployed engineering methodology.
Ecological systems — populations, communities, ecosystems, and the biosphere itself — are composed of interacting subsystems that exchange matter and energy across semi-permeable boundaries while maintaining stable macrostates over time. That structure closely mirrors the Markov blanket and Bayesian mechanics formalism underlying the free energy principle, which is why a research program under the banner of "variational ecology" has emerged to model organisms and their niches as coupled inference systems. The literature here is heavier on theory and simulation than on field data: this page surveys the conceptual foundations, the modeling patterns researchers are using, the tools available, and the empirical gaps the field still needs to close.
Active inference and the free energy principle recast medicine's core activities — diagnosis, treatment selection, and the maintenance of physiological stability — as processes of building and updating generative models under uncertainty. The literature is conceptually mature in several specialties (psychiatry, pain medicine, neurology) but empirically early: most models are theoretical, phenomenological, or simulation-based, with very few fully deployed clinical systems. This page surveys why medicine fits the framework, where the literature currently stands, what concrete projects and tools exist, and what evidence is still missing.
Active inference and the free energy principle model brains as hierarchical generative systems that infer the hidden causes of sensory data and act to minimize expected surprise. Over the past decade this formalism has moved from theoretical neuroscience into psychological science and clinical psychiatry, producing a growing literature on emotion, interoception, selfhood, social cognition, psychotherapy, and psychopathology. The literature remains methodologically young: most models are still at the level of simulation and theory, with relatively few large-scale empirical tests and only nascent translational tools.
Entomology offers an unusually rich and experimentally tractable arena for testing active inference and the free energy principle, spanning single-neuron computation in insect brains to colony-level collective intelligence and insect-inspired robotics. Social insects exhibit sophisticated perceptual, navigational, and social behaviors that unfold under strong observational control, with dense datasets from behavioral tracking, electrophysiology, and molecular biology. This page synthesizes the emerging literature connecting active inference to ant colonies, honeybee foraging, and insect neurobiology, distinguishing peer-reviewed empirical work from simulation and theory.
Active inference and the free energy principle offer a unifying Bayesian account of perception, action, and learning, and over the past decade this framework has begun to reach into economics — from individual choice behavior to general equilibrium models and financial markets. The literature remains nascent: most contributions are theoretical or simulation-based, and rigorous empirical validation against real economic data is still limited.
Climate science already runs on Bayesian filtering, generative modeling, and decision-making under deep uncertainty — it just rarely calls this active inference. This report surveys the case for treating climate perception, model updating, and policy selection as a single free-energy-minimization problem, and takes stock of where that vision is theoretical, where it is simulation-tested, and where it remains aspirational.
Education is a socially organized process for reducing uncertainty about the world and about other minds, which makes it an archetypal fit for active inference and the free energy principle. A small but growing body of peer-reviewed work has begun to formalize students and teachers as agents who build hierarchical generative models, select policies under expected free energy, and update beliefs through prediction error — but this literature remains conceptually oriented, with direct empirical tests of active inference in real classrooms still largely absent.
Law and policy can be read as collective inference processes: courts, legislatures, and regulators continuously revise generative models of social behavior in response to evidence, while norms encode prior beliefs about acceptable conduct. This page traces the emerging literature connecting active inference and the free energy principle to jurisprudence, regulatory design, and — most concretely — AI governance, drawing on a research synthesis prepared for the Active Inference Institute. The literature here is foundationally rich in theory and social-cognitive extensions, but explicit legal application is still nascent and largely at the proof-of-principle stage.
The Institute's strategy is organized around its motto — Act. Infer. Serve. — and pursued across four areas: Education, Research, Outreach & Engagement, and Methods. Each area is approached at three scales: the participant, the Institute, and the wider ecosystem. Together these give a single, legible frame for how the Institute advances Active Inference as a scientific, educational, and applied practice.
Active inference casts the brain as a system that maintains an internal generative model of the world and minimizes variational free energy through perception, action, and learning. Neuroscience has been the primary proving ground for this framework since Karl Friston's 2010 "unified brain theory" paper, with a concentrated body of theoretical, simulation, and early clinical work testing how far the idea can be pushed. This page surveys what that literature actually shows, the modeling patterns and tools researchers use, and the open questions that still separate active inference from a validated neuroscientific theory.
Project Measurement is how the Active Inference Institute tracks the health and progress of its projects — and the short Measurement form is how you contribute an update. Anyone can submit a measurement about a project or a Domain of Application for Active Inference; submitting one is the way to have your update included in Institute communications and to keep your project visible and active.
Project Preparation is how you let the Active Inference Institute know what you are setting out to do. Submitting the Preparation form is the way to get your project listed on the public site and to receive relevant support — it is the "Prepare" half of the Institute's Prepare-and-Measure system, paired with the Measurement form you use later to report progress.
Language use couples perception, action, and uncertainty resolution as tightly as any domain active inference has been applied to: speakers and listeners continually update generative models and act to reduce prediction error across a conversation. This report traces the literature from foundational generative models of synthetic dialogue through extensions into speech motor control, stuttering, inner speech, syntax, and large language model architectures, while also surfacing the empirical caution urged by predictive-coding and Bayesian-brain critiques. What follows synthesizes that literature for researchers assessing where active inference genuinely explains linguistic phenomena and where it remains a normative scaffold awaiting evidence.
Urban planning increasingly confronts deep uncertainty, multi-scale dynamics, and heterogeneous stakeholders, conditions under which classical equilibrium or static optimization approaches grow less adequate. Active inference, grounded in the free energy principle, offers a unifying framework for perception, action, and learning under uncertainty, and peer-reviewed and preprint work has begun applying it to traffic signal control, multi-agent energy optimization, urban water governance, and geospatial land-use modeling. This page synthesizes the state of that literature, the application patterns it uses, the concrete projects and tools it has produced, and the open problems the field still needs to resolve.
Affordances is an Institute research thread that studies affordances — the action possibilities an environment offers an agent — through the lens of Active Inference and the Free Energy Principle. The concept originates in James J. Gibson's ecological psychology, where perception and action form a single coupled loop rather than separate stages. This thread reads affordances as relations defined jointly by agent and environment, and asks how an Active Inference agent comes to perceive and act on them. The work is part of the ReInference Unit's open research program.
Music and sound intertwine perception, motor action, prediction, and affect in a temporally structured, quantifiable way, making them an unusually tractable domain for active inference research. A converging body of work on predictive processing in music, Bayesian auditory perception, and active inference in audition and psychopathology suggests that listening is organized around continual updating of hierarchical internal models to minimize prediction error and expected free energy. This page synthesizes what that literature currently supports, the concrete tools and projects it names, and the gaps it identifies for future work.
The Wave Hypothesis thread gathers Robert Worden's work on the Brain Wave Hypothesis and the Projective Wave Theory of Consciousness, which he presented at the Active Inference Institute during 2024 through the GuestStream #082 series. These are Worden's proposals; the Institute hosts the presentations, links the underlying papers, and invites public commentary on the ideas.
Agriculture is intrinsically about perception, action, and adaptation under uncertainty, spanning plant physiology, field management, regional food systems, and biospheric climate dynamics. This report surveys how active inference and the free energy principle have begun to be applied across that range, from edge AI pest detection to sustainability theory for resilient food systems, while distinguishing peer-reviewed evidence from more speculative industrial claims.
Active Entity Ontology for Science (AEOS) is a 2022 Institute project that applies Active Inference principles to model scientific activity as a collective cognitive process. It provides a composable, versionable framework for understanding both traditional institutional science and decentralized science (DeSci) approaches, integrating a BOLTS perspective (Business, Operations, Legal, Technical, Social) for comprehensive analysis.
AI²C — the Active Inference Alignment Coalition — is a multi-institution initiative led by Research Fellow Mahault Albarracin and hosted by the Active Inference Institute. It is a separate project from AICACP (the AI Capabilities & Alignment Consensus Project, led by Adam Safron): the two share an alignment focus and a name-adjacent acronym, but different leads, teams, and scope. AI²C aims to turn Active Inference research into a platform for AI alignment, governance, and embodied experimentation, and is building a research coalition, a funding pipeline, and shared infrastructure for alignment work.
The Foundations of Ideology is a book project by Active Inference Institute Research Fellow Alexander Hemming, developed with Dr. Dylan Grove, that applies the Free Energy Principle and Active Inference to how political ideologies form, persist, and evolve. It reads ideology through the brain's imperative to minimize prediction error, and looks at cognitive rigidity, heuristic adherence, and category conflation as constraints on adaptive political thinking.
Recorded livestreams, learning-group sessions, interviews, lectures, and presentations — published as video on YouTube and as audio on podcast platforms, so anyone can follow the Institute's work as it happens or return to it later. The full, searchable library is below.
Cybersecurity asks defenders to infer the hidden state of systems and adversaries from noisy, incomplete telemetry, then act to keep those systems within acceptable bounds — a structure that maps directly onto active inference's perception-action loop. This page surveys the emerging literature connecting the free energy principle to anomaly detection, causal attack diagnosis, cyber-physical security, and large-scale network defense, and identifies where the evidence is thin.
The Myth of Objectivity Hypothesis is a research project investigating how morality and symbolic thought co-evolved, using multi-agent Active Inference simulations and transcendental model selection. It explores how implicit and explicit moral beliefs form the foundation of symbolic identities, formalizing the relationship between moral reasoning and symbolic cognition across hierarchical social levels (individual, dyadic, group, and cultural).
The Institute runs a weekly rhythm of public sessions, learning groups, and project meetings. The most current view of what's happening this week is the public Calendar, which lists upcoming sessions soonest-first — so this week's events appear at the top — and lets you search and filter to find a specific session.
2025 was the Active Inference Institute's fifth full year: four Quarterly Roundtables, the 5th Applied Active Inference Symposium (November 12–14), the Theoretical Neurobiology Group joining the Institute, a new RxInfer.jl partnership, completion of the Project ~ Measurement system, and selection of the 2026 Board — a completed year in review, organized month by month. The detailed record continues to be maintained on the 2025 hub.
Active inference is only as useful as the code that implements it. Across the ecosystem, a small number of toolchains carry most of the practical weight: Python packages for discrete-state simulation, a Julia framework for reactive message passing at scale, a decades-old MATLAB statistical platform that helped originate the theory, and symbolic-reasoning systems that pair free energy minimization with logical planning for robotics. This page surveys those toolchains, the Institute-affiliated development and learning activity around them, and the fragmentation that still separates them from a single coherent software stack.
Perspective Architectures for Coherent and Attuned Artificial Agency is an 18-month Institute research project led by Research Fellow Hongju Pae through CEAR Lab (Computational Emergent Alignment Research Lab). The project develops a computational account of how an artificial agent might form a stable internal perspective, sustain coherent affect, and become mutually interpretable with other agents, proposed as a complement to AI alignment approaches built on externally specified objectives and reward functions.
2026 is the Active Inference Institute's ongoing year — a consolidated, month-by-month entry point to the activities, programs, events, and newsletters that ran across the organization through August, under the motto Act. Infer. Serve. Every month links to its archived newsletter, and project, program, and governance entries link to their own pages. The Activities page and Calendar remain the most reliable way to see what is happening week to week. The detailed record continues to be maintained on the 2026 hub.
Active inference research in the social domain focuses on modeling communication and the sharing of belief models within groups — treated as normative processes of group cognition rather than only individual perception and action. A large and growing body of work, running to what one Institute report estimates as thousands of papers touching the social setting, extends the free energy principle from single agents to the dynamics of consensus building, shared narrative, and social coordination. The Institute's own CogNarr Ecosystem project is the clearest attempt to turn this theory into working infrastructure, though — like most of the domain — it remains early-stage relative to the conceptual literature.
START is a modular content-generation pipeline that produces high-quality educational materials on Active Inference and the Free Energy Principle, tailored to professional domains and individual learners. It integrates live web research via Perplexity and large language models via OpenRouter to produce evidence-based, professionally-contextualized learning content.
The scientific method is, at its core, a disciplined cycle of forming beliefs, acting to gather evidence, and revising those beliefs when prediction fails — the same cycle active inference formalizes as perception, action, and generative-model updating. A small body of Institute-affiliated work extends this parallel from individual inquiry to the organization of research itself: how teams, institutions, and open or decentralized science communities structure the collective minimization of uncertainty about the world. The literature here is early and largely conceptual, with the Institute's own projects — rather than a mature external field — carrying most of the current work.
pymdp is an open-source Python package for discrete-state (POMDP) Active Inference, widely used across the research community for building and simulating generative models. It is an external ecosystem implementation, distinct from the Institute's own projects, that the Institute references and builds learning materials around.
Each year the Active Inference Institute publishes a consolidated, month-by-month overview of its activities, programs, and events. This index lists every published annual report.
As a participatory, open-science 501(c)(3) nonprofit, the Institute publishes a recurring newsletter, weekly announcements, and periodic reports as part of its regular work. This page lists the current public record of those communications.
The Unordinary Bible Study (TUBS) is a monthly Ecosystem session cross-referencing biblical verses with contemporary and inter-faith perspectives, connecting Active Inference to process theology.
Digital Agents was a weekly Ecosystem Discord session exploring digital agent design through an Active Inference lens.
This Ecosystem project applied Active Inference to designing, managing, and evolving built environments that prioritize the flourishing of all life, via distributed intelligence and digital twins.
Ed4All (Active Inference for Grounded Educational Knowledge Environments) is an open, locally deployable, empirically evaluated framework for grounded educational agents that operate within explicit knowledge and evidentiary boundaries.
cpomdp is an open-source Python library for continuous-state Active Inference. It provides factor-graph message passing, Kalman and reference filtering, state-dependent observation and process noise, certifiable tolerances, and matched classical control baselines.
Coherence Density is an exploratory Ecosystem research project establishing a measurement-focused framework to quantify internal coordination, stability, and information flow in complex adaptive systems under Active Inference.
Cognitive Somatic Regulator is an Ecosystem project exploring how Active Inference generative models can represent somatic regulation, ethical threshold gating, and neuro-somatic feedback loops.
Regime-Dependent Active Inference develops a generalized generative-model framework combining continuous latent cognitive-affective states with discrete regime transitions to model the dynamics of addiction, relapse, and recovery.
생태계 주제
| Topic | Projects | Narrative |
|---|---|---|
| Biology | 0 | Yes |
| Bioregional Modeling | 4 | Yes |
| Category Theory | 0 | Yes |
| Computational | 0 | No |
| DeSci | 0 | Yes |
| Domains of Application | 0 | Yes |
| Economics | 0 | Yes |
| Education | 15 | Yes |
| Implementations of Active Inference | 0 | Yes |
| Legal | 0 | Yes |
| Logistics | 0 | Yes |
| Mental Health | 0 | Yes |
| Neuroscience | 5 | Yes |
| Philosophy | 2 | Yes |
| Physics | 2 | Yes |
| PyMDP | 0 | Yes |
| Robotics | 4 | Yes |
| RxInfer.jl | 1 | Yes |
| Scientific Method | 0 | Yes |
| Social | 0 | Yes |
| SPM (Statistical Parametric Mapping) | 0 | Yes |
| Symbolic Cognitive Robotics | 4 | Yes |
자원 그룹
공개 토론, 정기 업데이트, 그리고 낮은 마찰의 진입점
공식 기관의 맥락과 웹사이트 표면화.
코스, 연구 그룹, 소개 자료 및 구현 학습 경로.
공개 녹음, 라이브스트림, 팟캐스트 및 미디어 지수.
자원봉사, 인턴십, 장학금 및 기여 경로
개방형 저장소, 프로젝트 페이지, 측정 및 적용 작업
연구 페이지, 참고 문헌, 저장소, 논문
공개 사회 채널
기부, 파트너십 및 지원 경로
실시 도구, 소프트웨어 표면, 기술 학습 자원.
공식 페이지
공식 단축 링크
| Official page | Group | Audience | Related |
|---|---|---|---|
| 2026 planning shortlink | Community | Contributor | Activities Programs |
| Fellows pathway | Participation | Contributor | Programs Get Involved |
| Mentorship pathway | Participation | Contributor | Programs Learning and Research |
| Active Inference knowledge base | Learning | Learner | Learning and Research Active Inference |
| Active Inference Ontology shortlink | Research | Researcher | Ecosystem Projects |
| Project Preparation shortlink | Projects | Contributor | Projects Programs |
| RxInfer learning group | Learning | Developer | Learning and Research Projects |
| Strategy shortlink | Institute | Newcomer | About the Institute Ecosystem |
| Wave Hypothesis shortlink | Research | Researcher | Ecosystem Learning and Research |
| Welcome shortlink | Institute | Newcomer | About the Institute Get Involved |
저장소
| Repository | Group | Language | Stars | Updated |
|---|---|---|---|---|
| act_inf_metaanalysis | Research | TeX | 4 | 2026-05-04 |
| ActInf_RxInfer | Tools | Unspecified | 2 | 2025-04-03 |
| Active_Inference_Ontology | Research | Unspecified | 14 | 2026-05-18 |
| ActiveBlockference | Projects | Jupyter Notebook | 33 | 2026-05-27 |
| ActiveInferAnts | Projects | Python | 29 | 2026-05-18 |
| ActiveInferenceCategoryTheory | Research | Unspecified | 10 | 2025-02-03 |
| ActiveInferenceImplementations | Learning | Jupyter Notebook | 0 | 2024-09-21 |
| ActiveInferenceJournal | Research | HTML | 43 | 2026-05-25 |
| AEOS | Research | Unspecified | 8 | 2025-05-27 |
| aii-org | Institute | HTML | 0 | 2026-04-04 |
| ants | Projects | Unspecified | 0 | 2021-08-29 |
| ATLAS | Projects | Unspecified | 0 | 2024-08-21 |
| Biofirm | Projects | Python | 5 | 2025-06-15 |
| CEREBRUM | Research | Python | 13 | 2026-06-09 |
| COGANT | Research | Python | 2 | 2026-06-30 |
| cognitive | Projects | Python | 18 | 2026-03-09 |
| courses | Learning | HTML | 10 | 2026-05-18 |
| fep_lean | Research | Python | 3 | 2026-04-27 |
| fundamentals | Learning | Python | 19 | 2026-06-05 |
| GEN24 | Projects | Jupyter Notebook | 1 | 2024-10-27 |
| gen25 | Projects | Unspecified | 0 | 2025-03-14 |
| GeneralizedNotationNotation | Projects | Pkl | 24 | 2026-05-25 |
| GEO-INFER | Projects | HTML | 11 | 2026-05-26 |
| institute_website | Institute | HTML | 0 | 2026-06-09 |
| Journal-Utilities | Research | HTML | 11 | 2026-02-21 |
| Knowledge-Engineering | Projects | Jupyter Notebook | 5 | 2026-05-29 |
| NoOrg | Projects | HTML | 1 | 2026-01-31 |
| on_policy_distillation | Research | Python | 2 | 2026-06-30 |
| Parr_et_al_2022_ActInf_Textbook | Learning | Julia | 6 | 2024-07-24 |
| policy_entanglement | Projects | Python | 1 | 2026-05-30 |
| pymcp | Tools | Python | 2 | 2025-07-28 |
| pymdp | Tools | Python | 2 | 2024-11-09 |
| Research-Discovery-Engine | Research | HTML | 6 | 2025-12-15 |
| Start | Learning | Python | 5 | 2026-01-19 |
| Symposium | Projects | Python | 12 | 2026-03-18 |
검증된 링크
| Link | Type | Group | Audience |
|---|---|---|---|
| ActiveInference.org landing page | Official page | Institute | Newcomer |
| Discord invite | Community | Community | Newcomer |
| Institute website | Official page | Institute | Newcomer |
| Weekly updates | Community | Community | Newcomer |
| Start repository | Learning | Learning | Learner |
| Ecosystem shortlink | Research | Research | Researcher |
| YouTube channel | Media | Media | Newcomer |
| History | Official page | Institute | Newcomer |
| Videos and podcasts shortlink | Media | Media | Newcomer |
| Podcast feed | Media | Media | Newcomer |
| GitHub organization | Repository | Projects | Developer |
| Active InferAnts repository | Repository | Projects | Developer |
| Generalized Notation Notation repository | Repository | Projects | Developer |
| GEO-INFER repository | Repository | Projects | Developer |
| Active Inference Journal repository | Research | Research | Researcher |
| Donation page | Support | Support | Supporter |
| X profile | Social | Social | Newcomer |
| Bluesky profile | Social | Social | Newcomer |
| Facebook page | Social | Social | Newcomer |
| Get involved overview | Official page | Participation | Contributor |
| LinkedIn company page | Social | Social | Newcomer |
| START documentation site | Official page | Learning | Learner |
| Newsletter | Official page | Community | Newcomer |
| Activities shortlink | Official page | Community | Contributor |
| Projects shortlink | Official page | Projects | Contributor |
| Project Measurement shortlink | Official page | Projects | Contributor |
| Textbook Group shortlink | Official page | Learning | Learner |
| Volunteer pathway | Official page | Participation | Contributor |
| Internship pathway | Official page | Participation | Contributor |
| Register for the 2026 Symposium | Official page | Community | Newcomer |
| Applied Active Inference Symposium | Official page | Community | Partner |
| Active Inference livestream introduction | Media | Learning | Learner |
| cpomdp repository | Repository | Projects | Developer |
| cpomdp documentation | Learning | Projects | Developer |
| FEP and Active Inference papers repository | Research | Research | Researcher |
| cpomdp on PyPI | Repository | Tools | Developer |
| Ramstead et al. Bayesian Mechanics precis (arXiv) | Research | Research | Researcher |
| Active inference on discrete state-spaces: A synthesis (Da Costa et al., arXiv) | Research | Research | Researcher |
| State-Dependent Observation Noise Reintroduces Epistemic Value in Linear-Gaussian Active Inference (arXiv:2607.20306) | Research | Research | Researcher |
| Active Inference & Behavior Engineering for Teams (Vyatkin, 2020) | Research | Research | Researcher |
| Narrative Information Management (Cordes, 2021) | Research | Research | Researcher |
| Active Inference in Modeling Conflict (David, 2021) | Research | Research | Researcher |
| Transcript of: Karl Friston, 1st Applied Active Inference Symposium (Friston, 2021) | Research | Research | Researcher |
| Predictive Processing Interpretation of the Mirror Test (O'Connor, 2022) | Research | Research | Researcher |
| Catechism for: "Towards Active Diffusion: A Tale of Multiple (den)Cities" (Smékal, 2022) | Research | Research | Researcher |
| The Free Energy Principle & Active Inference: a Systematic Literature Analysis (Knight, 2022) | Research | Research | Researcher |
| An Active Inference Ontology for Decentralized Science (Friedman, 2022) | Research | Research | Researcher |
| Generalized Notation Notation for Active Inference Models (Smékal, 2023) | Research | Research | Researcher |
| Generative Research Teams: Active Inference Compositions For Research and Meta-Science (Friedman, 2023) | Research | Research | Researcher |
| The Active Inference Institute and Active Inference Ecosystem (Active Inference Institute, 2023) | Research | Research | Researcher |
| 2026 planning shortlink | Official page | Community | Contributor |
| Fellows pathway | Official page | Participation | Contributor |
| Mentorship pathway | Official page | Participation | Contributor |
| Active Inference knowledge base | Official page | Learning | Learner |
| Active Inference Ontology shortlink | Official page | Research | Researcher |
| Project Preparation shortlink | Official page | Projects | Contributor |
| RxInfer learning group | Official page | Learning | Developer |
| Strategy shortlink | Official page | Institute | Newcomer |
| Wave Hypothesis shortlink | Official page | Research | Researcher |
| Welcome shortlink | Official page | Institute | Newcomer |
| act_inf_metaanalysis | Repository | Research | Researcher |
| ActInf_RxInfer | Repository | Tools | Learner |
| Active_Inference_Ontology | Repository | Research | Researcher |
| ActiveBlockference | Repository | Projects | Developer |
| ActiveInferAnts | Repository | Projects | Developer |
| ActiveInferenceCategoryTheory | Repository | Research | Researcher |
| ActiveInferenceImplementations | Repository | Learning | Learner |
| ActiveInferenceJournal | Repository | Research | Researcher |
| AEOS | Repository | Research | Researcher |
| aii-org | Repository | Institute | Newcomer |
| ants | Repository | Projects | Developer |
| ATLAS | Repository | Projects | Developer |
| Biofirm | Repository | Projects | Developer |
| CEREBRUM | Repository | Research | Researcher |
| cognitive | Repository | Projects | Developer |
| courses | Repository | Learning | Learner |
| fep_lean | Repository | Research | Researcher |
| fundamentals | Repository | Learning | Learner |
| GEN24 | Repository | Projects | Developer |
| gen25 | Repository | Projects | Developer |
| GeneralizedNotationNotation | Repository | Projects | Developer |
| GEO-INFER | Repository | Projects | Developer |
| institute_website | Repository | Institute | Newcomer |
| Journal-Utilities | Repository | Research | Researcher |
| Knowledge-Engineering | Repository | Projects | Developer |
| NoOrg | Repository | Projects | Developer |
| Parr_et_al_2022_ActInf_Textbook | Repository | Learning | Learner |
| policy_entanglement | Repository | Projects | Developer |
| pymcp | Repository | Tools | Learner |
| pymdp | Repository | Tools | Learner |
| Research-Discovery-Engine | Repository | Research | Researcher |
| Start | Repository | Learning | Learner |
| Symposium | Repository | Projects | Developer |
| COGANT | Repository | Research | Researcher |
| on_policy_distillation | Repository | Research | Researcher |
오픈 소스 맵
리positories, 아이디어, 오 Ontology 관계, 지 원, 게시물, 정책, 프로그램, 문헌에 대한 안전한 공용 테이블 행
Active Inference Institute의 GitHub 네임스페이스에서 파생된 공개 저장소 행들.
개념 그래프에서 개념, 방법, 도구, 가치, 그리고 응용 분야의 행.
개념 그래프에서 방향성 관계를 가진 행들을 추출하다.
이사회, 임원 및 등록된 조직 역할과 같은 공공 거버넌스 구성원들.
승인된 공지 사항 포함 보고서, 발표 및 뉴스레터.
카테고리, 상태, 버전 및 설명을 포함한 공공 거버넌스 정책 등록부.
참여자와 지원 경로를 위한 봉사자, 인턴, 연구원, 지도자, 파트너 및 지원자
연구 분야 페이지를 기반으로 한 공개 학술 논문 레코드
| Table row | Table | Summary |
|---|---|---|
| Adam Safron | 지부 멤버 | Primary point of contact / IE project facilitator for AICACP SAB membership, Unit Member |
| Adeel Razi | 지부 멤버 | SAB membership |
| Alexander Ororbia | 지부 멤버 | SAB membership |
| Alexander Sabine | 지부 멤버 | Board Member Board of Directors |
| Alexandra Mikhailova | 지부 멤버 | Vice President & Secretary Vice President, Secretary |
| Ali Rahmjoo | 지부 멤버 | SAB membership |
| Alianna Maren | 지부 멤버 | Board Member Board of Directors |
| Ana Magdalena Hurtado | 지부 멤버 | Board Member Board of Directors, Unit Member |
| Andrew Pashea | 지부 멤버 | SAB membership |
| Ann Stapleton | 지부 멤버 | Board Member Board of Directors |
| Anna Pereira | 지부 멤버 | Unit Member, Fellow |
| Antonio Lucas-Alba | 지부 멤버 | SAB membership |
| Arun Niranjan | 지부 멤버 | SAB membership |
| Austin Cook | 지부 멤버 | Board Member Board of Directors |
| Avel Guénin-Carlut | 지부 멤버 | SAB membership |
| Bradly Alicea | 지부 멤버 | SAB membership |
| Candice Pattisapu | 지부 멤버 | SAB membership |
| Chris Fields | 지부 멤버 | SAB membership |
| Christo Kurisummoottil Thomas | 지부 멤버 | SAB membership |
| Cory Slater | 지부 멤버 | SAB membership |
| Daniel Friedman | 지부 멤버 | President & Treasurer President, Treasurer |
| Dean Tickles | 지부 멤버 | Board Member (2022-2024) Board of Directors |
| Ed Ober | 지부 멤버 | Board Member Board of Directors |
| Elliott Hauser | 지부 멤버 | SAB membership |
| Ellynne Dec | 지부 멤버 | Board Member Board of Directors |
| Fraser Paterson | 지부 멤버 | Primary point of contact / IE project facilitator for Improving RxInfer.jl's Model Visualization Capabilities Unit Member |
| Haris Neophytou | 지부 멤버 | SAB membership |
| Héctor Manrique | 지부 멤버 | SAB membership, Unit Member |
| Holly Grimm | 지부 멤버 | SAB membership |
| Ian Tennant | 지부 멤버 | SAB membership |
| Ivan Metelkin | 지부 멤버 | Primary point of contact / IE project facilitator for Active Inference for Built Environments & CooperActive Systems Unit Member |
| Jana Lumi | 지부 멤버 | SAB membership |
| Jean-François Cloutier | 지부 멤버 | Primary point of contact / IE project facilitator for Symbolic cognitive robotics Unit Member, Fellow |
| Jeremy Cooper | 지부 멤버 | Primary point of contact / IE project facilitator for Active Inference Account of Belief Updating in PTSD Unit Member |
| Jesse G | 지부 멤버 | Unit Member |
| Joel Dietz | 지부 멤버 | SAB membership |
| John Boik | 지부 멤버 | Primary point of contact / IE project facilitator for CogNarr Ecosystem: Facilitating Group Cognition at Scale Unit Member, Fellow |
| John Clippinger | 지부 멤버 | Scientific Advisory Board; Board Member (2022-2025) SAB membership |
| Joshua Shane | 지부 멤버 | SAB membership |
| Karl John Friston | 지부 멤버 | Member, SAB membership |
| Luca Possati | 지부 멤버 | SAB membership |
| Matt Brown | 지부 멤버 | SAB membership |
| Maxwell J D Ramstead | 지부 멤버 | SAB membership |
| Michael Lennon | 지부 멤버 | SAB membership |
| Mike Smith | 지부 멤버 | Scientific Advisory Board; Board Member (2022-2025) SAB membership |
| PabloFM | 지부 멤버 | Unit Member |
| Rafael Kaufmann | 지부 멤버 | Board Member (2022-2024) Board of Directors |
| Robert Worden | 지부 멤버 | Primary point of contact / IE project facilitator for Model-Centric cognition Unit Member, Fellow |
| Scott David | 지부 멤버 | SAB membership |
| Sebastian Alvarado | 지부 멤버 | SAB membership |
| Shady El Damaty | 지부 멤버 | SAB membership |
| Shagor (Shaggy) Rahman | 지부 멤버 | Primary point of contact / IE project facilitator for Humanity's Story of an Uncertain Self Unit Member |
| Theodoros Aliferis | 지부 멤버 | Primary point of contact / IE project facilitator for The Einstein Model of a Solid as a Model of the Mental Apparatus from the Economic Perspective of Psychoanalytic Theory Unit Member |
| Thomas Kehler | 지부 멤버 | SAB membership |
| Virginia Bleu Knight | 지부 멤버 | Board Member Board of Directors, Member |
| Vladimir Baulin | 지부 멤버 | Board Member Board of Directors, Unit Member |
| Bylaws of Active Inference Institute, Inc. | 지배구조 정책 | Governance Ethics / Accepted |
| Accessibility | 이dea와 방법 | Value / Established |
| Action as Active Inference | 이dea와 방법 | Application / Established |
| Active Inference | 이dea와 방법 | Concept / Established |
| Allostasis | 이dea와 방법 | Concept / Advanced |
| Autopoiesis | 이dea와 방법 | Concept / Established |
| Bayesian Inference | 이dea와 방법 | Method / Established |
| Consciousness Theories | 이dea와 방법 | Research Area / Emerging |
| Entropy | 이dea와 방법 | Concept / Established |
| Expected Free Energy | 이dea와 방법 | Concept / Established |
| Free Energy Principle | 이dea와 방법 | Concept / Established |
| Free Energy Principle (Core) | 이dea와 방법 | Concept / Established |
| Generative Models | 이dea와 방법 | Concept / Established |
| Homeostasis | 이dea와 방법 | Concept / Established |
| Information Theory | 이dea와 방법 | Research Area / Established |
| Karl Friston | 이dea와 방법 | Person / Established |
| Learning as Model Update | 이dea와 방법 | Application / Established |
| Markov Blankets | 이dea와 방법 | Concept / Established |
| Perception as Inference | 이dea와 방법 | Application / Established |
| Perception-Action Loop | 이dea와 방법 | Concept / Established |
| Policy Selection | 이dea와 방법 | Method / Established |
| Precision Weighting | 이dea와 방법 | Concept / Established |
| Predictive Processing | 이dea와 방법 | Concept / Established |
| pymdp | 이dea와 방법 | Tool / Emerging |
| Real-World Applicability | 이dea와 방법 | Value / Established |
| RxInfer.jl | 이dea와 방법 | Tool / Emerging |
| Scientific Rigor | 이dea와 방법 | Value / Established |
| Self-Organization | 이dea와 방법 | Concept / Established |
| Statistical Mechanics | 이dea와 방법 | Research Area / Established |
| Surprise Minimization | 이dea와 방법 | Concept / Established |
| Variational Bayes | 이dea와 방법 | Method / Established |
| Active Digital Twins via Active Inference | 문헌 | 2026 / Engineering Applications of Artificial Intelligence |
| Active Inferants: An Active Inference Framework for Ant Colony Behavior | 문헌 | 2021 / Frontiers in Behavioral Neuroscience |
| Active Inference AI and the Spatial Web for Medicine: A New Paradigm for Medical Research, Treatment, and Education | 문헌 | 2025 / The Cancer Journal |
| Active inference and robot control: a case study | 문헌 | 2016 / Journal of the Royal Society Interface |
| Active Inference in Psychology and Psychiatry: Progress to Date? | 문헌 | 2024 / Entropy |
| Active Inference in Robotics and Artificial Agents: Survey and Challenges | 문헌 | 2021 / arXiv |
| Active inference on discrete state-spaces: A synthesis | 문헌 | 2020 / Journal of Mathematical Psychology |
| Active Inference, homeostatic regulation and adaptive behavioural control | 문헌 | 2017 / Progress in Neurobiology |
| Active inference, morphogenesis, and computational psychiatry | 문헌 | 2022 / Frontiers in Computational Neuroscience |
| Active Inference: The Free Energy Principle in Mind, Brain, and Behavior | 문헌 | 2022 / MIT Press |
| An Active Inference Approach to Interoceptive Psychopathology | 문헌 | 2019 / Annual Review of Clinical Psychology |
| An active inference strategy for prompting reliable responses from large language models in medical practice | 문헌 | 2025 / npj Digital Medicine |
| Computational Models of Interoception and Body Regulation | 문헌 | 2021 / Trends in Neurosciences |
| From Broken Models to Treatment Selection: Active Inference as a Tool to Guide Clinical Research and Practice | 문헌 | 2022 / Clinical Psychology in Europe |
| On Bayesian Mechanics: A Physics of and by Beliefs | 문헌 | 2023 / arXiv |
| The free energy principle for action and perception: A mathematical review | 문헌 | 2017 / Journal of Mathematical Psychology |
| The free-energy principle: a unified brain theory? | 문헌 | 2010 / Nature Reviews Neuroscience |
| Accessibility -> Active Inference | 온톨로지 | Active Inference / governs |
| Active Inference -> Action as Active Inference | 온톨로지 | Active Inference / explains |
| Active Inference -> Expected Free Energy | 온톨로지 | Active Inference / includes |
| Active Inference -> Learning as Model Update | 온톨로지 | Active Inference / explains |
| Active Inference -> Perception as Inference | 온톨로지 | Active Inference / explains |
| Active Inference -> Precision Weighting | 온톨로지 | Active Inference / includes |
| Allostasis -> Homeostasis | 온톨로지 | Free Energy Principle / extends |
| Autopoiesis -> Self-Organization | 온톨로지 | Free Energy Principle / related to |
| Bayesian Inference -> Entropy | 온톨로지 | Free Energy Principle / prerequisite for |
| Bayesian Inference -> Free Energy Principle (Core) | 온톨로지 | Free Energy Principle / implements |
| Entropy -> Surprise Minimization | 온톨로지 | Free Energy Principle / prerequisite for |
| Expected Free Energy -> Policy Selection | 온톨로지 | Active Inference / enables |
| Free Energy Principle -> Active Inference | 온톨로지 | Active Inference / grounds |
| Free Energy Principle -> Generative Models | 온톨로지 | Active Inference / requires |
| Free Energy Principle -> Markov Blankets | 온톨로지 | Active Inference / defines boundary via |
| Free Energy Principle -> Variational Bayes | 온톨로지 | Active Inference / implemented via |
| Free Energy Principle (Core) -> Consciousness Theories | 온톨로지 | Free Energy Principle / applies to |
| Free Energy Principle (Core) -> Homeostasis | 온톨로지 | Free Energy Principle / applies to |
| Free Energy Principle (Core) -> Self-Organization | 온톨로지 | Free Energy Principle / explains |
| Information Theory -> Entropy | 온톨로지 | Free Energy Principle / defines |
| Information Theory -> Free Energy Principle (Core) | 온톨로지 | Free Energy Principle / foundations of |
| Karl Friston -> Free Energy Principle | 온톨로지 | Active Inference / originated |
| Markov Blankets -> Generative Models | 온톨로지 | Active Inference / prerequisite for |
| Perception-Action Loop -> Free Energy Principle (Core) | 온톨로지 | Free Energy Principle / core mechanism of |
| Predictive Processing -> Free Energy Principle | 온톨로지 | Active Inference / extends |
| pymdp -> Active Inference | 온톨로지 | Active Inference / implements |
| Real-World Applicability -> Action as Active Inference | 온톨로지 | Active Inference / governs |
| Real-World Applicability -> Perception as Inference | 온톨로지 | Active Inference / governs |
| RxInfer.jl -> Active Inference | 온톨로지 | Active Inference / implements |
| Scientific Rigor -> Free Energy Principle | 온톨로지 | Active Inference / governs |
| Statistical Mechanics -> Free Energy Principle (Core) | 온톨로지 | Free Energy Principle / foundations of |
| Surprise Minimization -> Free Energy Principle (Core) | 온톨로지 | Free Energy Principle / instantiates |
| Variational Bayes -> Generative Models | 온톨로지 | Active Inference / prerequisite for |
| Ecosystem Support | 프로그램 | Other / Active |
| Fellows | 프로그램 | Participation / Active |
| Grants | 프로그램 | Funding / Active |
| Internship | 프로그램 | Participation / Active |
| Mentorship | 프로그램 | Partnership / Active |
| Open Source | 프로그램 | Open Source / Active |
| Partnership | 프로그램 | Partnership / Active |
| Philanthropy | 프로그램 | Funding / Active |
| Volunteer | 프로그램 | Participation / Active |
| ActInfLab ~ June 2022 Newsletter with Special Announcement ~ 🅰️👀 | 출판물 | Newsletter / 2022-07-04 |
| Active Inference Institute — 2025 Annual Review | 출판물 | Report / 2026-01-15 |
| Active Inference Institute — 2026 Annual Preview & Strategic Plan | 출판물 | Report / 2026-01-01 |
| Active Inference Institute — Weekly Update #10 | 출판물 | Announcement / 2026-03-04 |
| Active Inference Institute ~ October 2022 Newsletter | 출판물 | Newsletter / 2022-10-31 |
| April 2023 Newsletter ☂️🧷🎵 Active Inference Institute | 출판물 | Newsletter / 2023-04-28 |
| April 2024 Newsletter ➰❓🐞 Active Inference Institute | 출판물 | Newsletter / 2024-04-30 |
| April 2025 Newsletter 🐧📒🐢 Active Inference Institute | 출판물 | Newsletter / 2025-04-30 |
| April 2026 Newsletter 🌿 🧅 🛠️ Active Inference Institute | 출판물 | Newsletter / 2026-04-30 |
| August 2023 Newsletter 🧟🌪️🥑Active Inference Institute | 출판물 | Newsletter / 2023-08-31 |
| August 2024 Newsletter 🦋♻️🐧 Active Inference Institute | 출판물 | Newsletter / 2024-08-30 |
| August 2025 Newsletter ♣️🔗🐞 Active Inference Institute | 출판물 | Newsletter / 2025-08-31 |
| August 2026 Newsletter 🎡🎞🐅 Active Inference Institute | 출판물 | Newsletter / 2026-08-31 |
| December 2022 Newsletter 🐜🐝🏠 Active Inference Institute | 출판물 | Newsletter / 2022-12-30 |
| December 2023 Newsletter 🥂🕛🧑🎤 Active Inference Institute | 출판물 | Newsletter / 2023-12-31 |
| December 2024 Newsletter 🎹🎡💾 Active Inference Institute | 출판물 | Newsletter / 2024-12-27 |
| December 2025 Newsletter 🗝️🎲🐧 Active Inference Institute | 출판물 | Newsletter / 2025-12-26 |
| February 2023 Newsletter 🐃🎨🧭 Active Inference Institute | 출판물 | Newsletter / 2023-02-28 |
| February 2024 Newsletter 🌼🐞🕯️ Active Inference Institute | 출판물 | Newsletter / 2024-02-29 |
| February 2025 Newsletter ⌚️🌷👂 Active Inference Institute | 출판물 | Newsletter / 2025-02-28 |
| February 2026 Newsletter 🕸️💓🚋 Active Inference Institute | 출판물 | Newsletter / 2026-02-27 |
| January 2023 Newsletter 🚗💭🌠 Active Inference Institute | 출판물 | Newsletter / 2023-01-31 |
| January 2024 Newsletter 🪁🛠️🌐 Active Inference Institute | 출판물 | Newsletter / 2024-01-31 |
| January 2025 Newsletter 💫🎱🌳 Active Inference Institute | 출판물 | Newsletter / 2025-01-31 |
| January 2026 Newsletter 🥧 🌤️ 🖌️ Active Inference Institute | 출판물 | Newsletter / 2026-01-30 |
| July 2023 Newsletter 🧠➿🦾 Active Inference Institute | 출판물 | Newsletter / 2023-07-31 |
| July 2024 Newsletter 🎮👽🌀 Active Inference Institute | 출판물 | Newsletter / 2024-08-01 |
| July 2025 Newsletter 🌊🐋🎢 Active Inference Institute | 출판물 | Newsletter / 2025-07-31 |
| July 2026 Newsletter ☀️🏖️🐝 Active Inference Institute | 출판물 | Newsletter / 2026-07-31 |
| June 2023 Newsletter ✂️🎨🧭 Active Inference Institute | 출판물 | Newsletter / 2023-06-29 |
| June 2024 Newsletter 📖✍🌐 Active Inference Institute | 출판물 | Newsletter / 2024-06-28 |
| June 2025 Newsletter 🎁🏄🐛 Active Inference Institute | 출판물 | Newsletter / 2025-06-27 |
| June 2026 Newsletter 🌙🌵🕶️ Active Inference Institute | 출판물 | Newsletter / 2026-06-30 |
| March 2023 Newsletter 🎢 🫠💮 Active Inference Institute | 출판물 | Newsletter / 2023-03-31 |
| March 2024 Newsletter 🌹🎪🎺 Active Inference Institute | 출판물 | Newsletter / 2024-03-29 |
| March 2025 Newsletter ❇️🐞🌗 Active Inference Institute | 출판물 | Newsletter / 2025-03-31 |
| March 2026 Newsletter 🐐 📰 🏵️ Active Inference Institute | 출판물 | Newsletter / 2026-03-27 |
| May 2023 Newsletter 🌊📢🦌 Active Inference Institute | 출판물 | Newsletter / 2023-05-31 |
| May 2024 Newsletter 📤⛎🎹 Active Inference Institute | 출판물 | Newsletter / 2024-05-31 |
| May 2025 Newsletter 🌛🌷📜 Active Inference Institute | 출판물 | Newsletter / 2025-05-30 |
| May 2026 Newsletter 📡 🐟 🐌 Active Inference Institute | 출판물 | Newsletter / 2026-05-29 |
| November 2022 Newsletter 🎉🎱🏗️ Active Inference Institute | 출판물 | Newsletter / 2022-11-30 |
| November 2023 Newsletter ☃️👽🖽 Active Inference Institute | 출판물 | Newsletter / 2023-11-30 |
| November 2024 Newsletter 🔰🔌🏐 Active Inference Institute | 출판물 | Newsletter / 2024-12-01 |
| November 2025 Newsletter 🌙🍄🌲 Active Inference Institute | 출판물 | Newsletter / 2025-11-30 |
| October 2023 Newsletter 🚀🎨🌟 Active Inference Institute | 출판물 | Newsletter / 2023-10-31 |
| October 2024 Newsletter 🔭🌲🍲 Active Inference Institute | 출판물 | Newsletter / 2024-10-31 |
| October 2025 Newsletter 🍃🚌🍎 Active Inference Institute | 출판물 | Newsletter / 2025-10-31 |
| September 2023 Newsletter 🎃🐧🥼Active Inference Institute | 출판물 | Newsletter / 2023-09-29 |
| September 2024 Newsletter 🎑🏔🎃 Active Inference Institute | 출판물 | Newsletter / 2024-10-01 |
| September 2025 Newsletter 🦔🛠️🐚 Active Inference Institute | 출판물 | Newsletter / 2025-09-25 |
| act_inf_metaanalysis | 저장소 | Research and publications / TeX |
| ActInf_RxInfer | 저장소 | Learning and implementations / Unspecified |
| Active_Inference_Ontology | 저장소 | Research and publications / Unspecified |
| ActiveBlockference | 저장소 | Open-source projects / Jupyter Notebook |
| ActiveInferAnts | 저장소 | Open-source projects / Python |
| ActiveInferenceCategoryTheory | 저장소 | Research and publications / Unspecified |
| ActiveInferenceImplementations | 저장소 | Learning and implementations / Jupyter Notebook |
| ActiveInferenceJournal | 저장소 | Research and publications / HTML |
| AEOS | 저장소 | Research and publications / Unspecified |
| aii-org | 저장소 | Public web infrastructure / HTML |
| ants | 저장소 | Open-source projects / Unspecified |
| ATLAS | 저장소 | Open-source projects / Unspecified |
| Biofirm | 저장소 | Open-source projects / Python |
| CEREBRUM | 저장소 | Research and publications / Python |
| COGANT | 저장소 | Research and publications / Python |
| cognitive | 저장소 | Open-source projects / Python |
| courses | 저장소 | Learning and implementations / HTML |
| fep_lean | 저장소 | Research and publications / Python |
| fundamentals | 저장소 | Learning and implementations / Python |
| GEN24 | 저장소 | Open-source projects / Jupyter Notebook |
| gen25 | 저장소 | Open-source projects / Unspecified |
| GeneralizedNotationNotation | 저장소 | Open-source projects / Pkl |
| GEO-INFER | 저장소 | Open-source projects / HTML |
| institute_website | 저장소 | Public web infrastructure / HTML |
| Journal-Utilities | 저장소 | Research and publications / HTML |
| Knowledge-Engineering | 저장소 | Open-source projects / Jupyter Notebook |
| NoOrg | 저장소 | Open-source projects / HTML |
| on_policy_distillation | 저장소 | Research and publications / Python |
| Parr_et_al_2022_ActInf_Textbook | 저장소 | Learning and implementations / Julia |
| policy_entanglement | 저장소 | Open-source projects / Python |
| pymcp | 저장소 | Learning and implementations / Python |
| pymdp | 저장소 | Learning and implementations / Python |
| Research-Discovery-Engine | 저장소 | Research and publications / HTML |
| Start | 저장소 | Learning and implementations / Python |
| Symposium | 저장소 | Open-source projects / Python |