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Researchers seeking the canonical literature, evidence scores, and structured analysis of the Active Inference and Free Energy Principle research field.

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Curated references and structured outputs from the Active Inference Meta-Analysis project — 817 papers, 8 hypotheses, 5 topics, 8 subfields, and 71 curated references.

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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.

About this bibliography

This page presents the complete structured outputs of the Active Inference Meta-Analysis project. The bibliography includes 71 curated references drawn from the manuscript source, plus the full corpus analysis: field growth metrics, hypothesis evidence scores from LLM-extracted assertions, citation network analysis, NMF topic modeling, subfield classification, corpus coverage statistics, and project quality & reproducibility data. The full BibTeX source, all output data files, and the compiled manuscript are available on GitHub.

View BibTeX source on GitHub Meta-analysis repository Meta-analysis document

Scope and coverage

The bibliography covers foundational work on the Free Energy Principle (Friston 2006, 2010), the Active Inference textbook (Parr, Pezzulo & Friston 2022), methodological advances in discrete and continuous state-space formulations, deep active inference, active inference for robotics and language, and the critical debate around Markov blankets and the FEP's scientific status. It also includes the computational infrastructure references: Nanopublications, knowledge graphs, the Semantic Scholar and OpenAlex platforms, and LLM-based scientific information extraction.

Field overview

Active Inference literature: 817 papers (2005–2026)

The corpus spans two decades of Active Inference and Free Energy Principle research, retrieved from arXiv, Semantic Scholar, and OpenAlex.

Growth metrics

Total papers: 817

CAGR: 20.4%

Doubling time: 2.8 years

Peak year: 2025 (123 papers)

Cumulative (2026): 817

Papers per year

2005 1

2006 0

2007 1

2008 4

2009 4

2010 6

2011 6

2012 12

2013 14

2014 13

2015 18

2016 20

2017 21

2018 32

2019 52

2020 61

2021 90

2022 94

2023 97

2024 99

2025 123

2026 49

Corpus assessment

819 papers indexed and assessed

Coverage and availability statistics for the full literature corpus.

Coverage

Abstracts: 96.5%

Open access: 94.6%

Full-text PDF: 87.8%

Full-text sources

arxiv: 485

publisher: 157

none: 100

repository: 67

openalex: 10

Identifier coverage

doi: 420

arxiv_id: 485

s2_id: 0

openalex_id: 334

Hypothesis evidence

8 core hypotheses evaluated across the literature

Each hypothesis was scored using citation-weighted evidence from LLM-extracted assertions (Nanopublications). Scores reflect the aggregate evidential support found in the corpus.

Morphogenesis

Evidence score 100%

24 supporting assertions

Active Inference Optimality

Evidence score 97%

291 supporting, 6 neutral, 2 contradicting assertions

Predictive Coding

Evidence score 94%

233 supporting, 24 neutral, 1 contradicting assertions

Clinical Utility

Evidence score 93%

23 supporting, 2 neutral assertions

Scalability

Evidence score 85%

108 supporting, 13 neutral assertions

Language & Active Inference

Evidence score 83%

31 supporting, 3 neutral assertions

Markov Blanket Realism

Evidence score 78%

12 supporting, 2 neutral, 4 contradicting assertions

FEP Universality

Evidence score 48%

281 supporting, 429 neutral, 1 contradicting assertions

Subfield distribution

8 domains classified across the corpus

Papers were classified into 8 subfield categories using keyword-based rules applied to titles and abstracts.

Tools & Methods 170

Neuroscience 161

Robotics & Agents 136

Biology / Morphogenesis 135

Formal / Mathematical 64

Philosophy 60

Language 58

Psychiatry / Clinical 33

Total classified assignments: 817

Topic landscape

5 topics extracted via NMF

Non-negative Matrix Factorization (NMF) was applied to TF-IDF vectors of paper titles and abstracts, revealing five thematic clusters in the literature.

Topic 1

energy (0.063), free (0.059), systems (0.05), states (0.048), fep (0.046)

Topic 2

agent (0.107), agents (0.05), environment (0.043), model (0.035), behavior (0.028)

Topic 3

predictive (0.061), cognitive (0.055), brain (0.046), processing (0.038), cognition (0.036)

Topic 4

active (0.06), inference (0.057), decision (0.041), agents (0.039), aif (0.036)

Topic 5

model (0.057), learning (0.052), data (0.036), state (0.033), inference (0.032)

Citation network

817 nodes, 2176 edges, 29323 references

The citation graph was constructed from paper reference lists. PageRank, hub, and authority scores identify the most influential papers in the network.

Network metrics

Nodes: 817

Edges: 2176

Density: 0.003264

Avg degree: 2.7

71 curated references in 67 sections

Every reference is linked to its DOI or source URL. The BibTeX source is available on GitHub.

Foundational FEP

The free-energy principle: a unified brain theory? Karl Friston (2010) Nature Reviews Neuroscience, 11, (2), 127--138. Nature Publishing Group · DOI: 10.1038/nrn2787
A free energy principle for the brain Karl Friston, James Kilner, Lee Harrison (2006) Journal of Physiology-Paris, 100, (1-3), 70--87. Elsevier · DOI: 10.1016/j.jphysparis.2006.10.001

Active Inference textbook

Active Inference: The Free Energy Principle in Mind, Brain, and Behavior Thomas Parr, Giovanni Pezzulo, Karl J Friston (2022) MIT Press

Da Costa et al. on active inference

Active inference on discrete state-spaces: A synthesis Lancelot Da Costa, Thomas Parr, Noor Sajid, Sebastijan Veselic, Victorita Neacsu, Karl Friston (2020) Journal of Mathematical Psychology, 99, 102447. Elsevier · DOI: 10.1016/j.jmp.2020.102447

Sajid et al. review

Active inference: demystified and compared Noor Sajid, Philip J Ball, Thomas Parr, Karl J Friston (2021) Neural Computation, 33, (3), 674--712. MIT Press · DOI: 10.1162/neco_a_01357

Smith et al. computational psychiatry

A step-by-step tutorial on active inference and its application to empirical data Ryan Smith, Karl J Friston, Christopher J Whyte (2022) Journal of Mathematical Psychology, 107, 102632. Elsevier · DOI: 10.1016/j.jmp.2021.102632

Deep active inference

Deep active inference agents using Monte-Carlo methods Zafeirios Fountas, Noor Sajid, Pedro AM Mediano, Karl Friston (2020) Advances in Neural Information Processing Systems, 33, 11662--11675.

Champion et al. tree search

Realizing active inference in variational message passing: The outcome-blind fixation of belief Th'e}ophile Champion, Howard Bowman, Peter Gr"unwald (2021) Neural Computation, 33, (10), 2762--2826. MIT Press · DOI: 10.1162/neco_a_01422

Nanopublications

Anatomy of a nanopublication Paul Groth, Andrew Gibson, Jan Velterop (2010) Information Services & Use, 30, (1-2), 51--56. IOS Press · DOI: 10.3233/ISU-2010-0613
Decentralized provenance-aware publishing with nanopublications Tobias Kuhn, Christine Chichester, Michael Krauthammer, N'u}ria Queralt-Rosinach, Ruben Verborgh, George Giannakopoulos, Axel-Cyrille Ngonga Ngomo, Michel Dumontier (2016) PeerJ Computer Science, 2, e78. PeerJ Inc. · DOI: 10.7717/peerj-cs.78
Trusty URIs: Verifiable, Immutable, and Permanent Digital Artifacts for Linked Data Tobias Kuhn, Michel Dumontier (2014) The Semantic Web--ISWC 2014, 395--410. Springer · DOI: 10.1007/978-3-319-11964-9_25

pymdp

pymdp: A Python library for active inference in discrete state spaces Conor Heins, Beren Millidge, Lancelot Da Costa, Stephen Mann, Karl Friston, Ozan Catal, Pablo Lanillos, Noor Sajid, Alexander Tschantz (2022) Journal of Open Source Software, 7, (73), 4098. The Open Journal · DOI: 10.21105/joss.04098

Semantic Scholar

The Semantic Scholar Open Data Platform Rodney Kinney, Chloe Anastasiades, Russell Authur, Isabelle Belber, Jonathan Blaschke, Regan Chiang, Jenna Coffey, Arman Feldman, Joshua Gruber, others (2023) arXiv preprint arXiv:2301.10140.

OpenAlex

OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts Jason Priem, Heather Piwowar, Richard Orr (2022) arXiv preprint arXiv:2205.01833.

Markov blanket debate

The Emperor's New Markov Blankets Jelle Bruineberg, Krzysztof Dolega, Joe Dewhurst, Manuel Baltieri (2022) Behavioral and Brain Sciences, 45, e183. Cambridge University Press · DOI: 10.1017/S0140525X21002351

Predictive processing

Whatever next? Predictive brains, situated agents, and the future of cognitive science Andy Clark (2013) Behavioral and Brain Sciences, 36, (3), 181--204. Cambridge University Press · DOI: 10.1017/S0140525X12000477

Bayesian mechanics

On Bayesian mechanics: a physics of and by beliefs Dalton AR Sakthivadivel (2023) Interface Focus, 13, (3), 20220029. The Royal Society · DOI: 10.1098/rsfs.2022.0029

LLM + KG 2025 systematic reviews

Combining Knowledge Graphs and Large Language Models: A Systematic Literature Review Viviana Fernanda Quevedo Tumailli, others (2025) IEEE Access. IEEE
Unifying large language models and knowledge graphs: A roadmap Linhao Li, others (2024) IEEE Transactions on Knowledge and Data Engineering. IEEE

2025 tools

RxInfer.jl v4.0.0: Real-time and adaptive Bayesian inference Dmitry Bagaev, others (2025)

Active inference and artificial reasoning (replaces fabricated heins2024active)

Active inference and artificial reasoning Karl Friston, Lancelot Da Costa, Alexander Tschantz, Conor Heins, Christopher Buckley (2025) arXiv preprint arXiv:2512.21129. DOI: 10.48550/arXiv.2512.21129

AXIOM: AIF agents learning games in minutes

AXIOM: Learning to Play Games in Minutes with Expanding Object-Centric Models Conor Heins, Toon Van de Maele, Alexander Tschantz, Hampus Linander, Dimitrije Markovic, Tommaso Salvatori, Corrado Pezzato, Ozan Catal, Ran Wei, Magnus Koudahl, Marco Perin, Karl Friston, Tim Verbelen, Christopher Buckley (2025) arXiv preprint arXiv:2505.24784.

Collective behavior from surprise minimization

Collective behavior from surprise minimization Conor Heins, Beren Millidge, Daphne Demekas, Hrishit Basu, Noor Sajid, Karl Friston (2024) Proceedings of the National Academy of Sciences, 121, (17), e2320239121. DOI: 10.1073/pnas.2320239121

Millidge retrospective on Active Inference

A Retrospective on Active Inference Beren Millidge (2024)

The Missing Reward: AIF + LLMs

The Missing Reward: Active Inference in the Era of Experience Bo Wen (2025) arXiv preprint arXiv:2508.05619. DOI: 10.48550/arXiv.2508.05619

Morphogenesis and FEP

Morphogenesis as Bayesian inference: A variational approach to pattern formation and body-plan diversity in biology Franz Kuchling, Karl Friston, Georgi Georgiev, Michael Levin (2020) Physics of Life Reviews, 33, 88--108. Elsevier · DOI: 10.1016/j.plrev.2019.06.001

Active inference for language

Generative models, linguistic communication and active inference Karl J Friston, Thomas Parr, Yan Yufik, Noor Sajid, Cathy J Price, Emma Holmes (2020) Neuroscience & Biobehavioral Reviews, 118, 42--64. Elsevier · DOI: 10.1016/j.neubiorev.2020.07.005

Non-negative Matrix Factorization

Learning the parts of objects by non-negative matrix factorization Daniel D Lee, H Sebastian Seung (1999) Nature, 401, (6755), 788--791. Nature Publishing Group · DOI: 10.1038/44565

Vector space model / TF-IDF

A vector space model for automatic indexing Gerard Salton, Anita Wong, Chung-Shu Yang (1975) Communications of the ACM, 18, (11), 613--620. ACM · DOI: 10.1145/361219.361220

Greedy modularity community detection (Clauset-Newman-Moore)

Finding community structure in very large networks Aaron Clauset, Mark E. J. Newman, Cristopher Moore (2004) Physical Review E, 70, (6), 066111. American Physical Society · DOI: 10.1103/PhysRevE.70.066111

rdflib

rdflib: A Python library for working with RDF RDFLib Team (2023)

Active inference for robotics

Active inference in robotics and artificial agents: Survey and challenges Pablo Lanillos, Cristian Meo, Corrado Pezzato, Ajith Anil Meera, Mohamed Baioumy, Wataru Ohata, Alexander Tschantz, Beren Millidge, Martijn Wisse, Christopher L Buckley, Jun Lenz (2021) arXiv preprint arXiv:2112.01871.

Technological approach to morphogenesis

Technological approach to mind everywhere: An experimentally-grounded framework for understanding diverse bodies and minds Michael Levin (2022) Frontiers in Systems Neuroscience, 16, 768201. Frontiers · DOI: 10.3389/fnsys.2022.768201

Active Inference textbook (Friston 2017)

Active inference: A process theory Karl Friston, Thomas FitzGerald, Francesco Rigoli, Philipp Schwartenbeck, Giovanni Pezzulo (2017) Neural Computation, 29, (1), 1--49. MIT Press · DOI: 10.1162/NECO_a_00912

Deep active inference survey

Understanding the origin of information-seeking exploration in probabilistic objectives for control Beren Millidge, Alexander Tschantz, Christopher L Buckley (2021) arXiv preprint arXiv:2103.06859.

Cpp-AIF: C++ active inference for POMDPs

A C++ Implementation of Active Inference for POMDPs Francesco Gregoretti (2023) Neurocomputing, 562, 126888. Elsevier · DOI: 10.1016/j.neucom.2023.126888

Deep Active Inference (Millidge)

Deep Active Inference as Variational Policy Gradients Beren Millidge (2020) Journal of Mathematical Psychology, 96, 102348. Elsevier · DOI: 10.1016/j.jmp.2020.102348

Predictive coding approximates backprop

Predictive coding approximates backprop along arbitrary computation graphs Beren Millidge, Alexander Tschantz, Christopher L Buckley (2022) Neural Computation, 34, (6), 1329--1368. MIT Press · DOI: 10.1162/neco_a_01497

Action-oriented predictive processing

Learning action-oriented models through active inference Alexander Tschantz, Anil K Seth, Christopher L Buckley (2020) PLoS Computational Biology, 16, (4), e1007805. PLOS · DOI: 10.1371/journal.pcbi.1007805

Ollama local LLM inference

Ollama: Run Large Language Models Locally Ollama Team (2024)

Prior systematic literature analysis

The Free Energy Principle & Active Inference: a Systematic Literature Analysis Virginia Bleu Knight, RJ Cordes, Daniel Friedman (2022) Zenodo · DOI: 10.5281/zenodo.7449368

Visualization standards

Points of view: Color blindness Bang Wong (2011) Nature Methods, 8, (6), 441. Nature Publishing Group · DOI: 10.1038/nmeth.1618

Hohwy - The Predictive Mind

The Predictive Mind Jakob Hohwy (2013) Oxford University Press · DOI: 10.1093/acprof:oso/9780199682737.001.0001

Ramstead et al. - Answering Schr\"odinger's question

Answering Schr"odinger's question: A free-energy formulation Maxwell JD Ramstead, Paul B Badcock, Karl J Friston (2018) Physics of Life Reviews, 24, 1--16. Elsevier · DOI: 10.1016/j.plrev.2017.09.001

Pezzulo et al. - Active inference and robotics

Active Inference, homeostatic regulation and adaptive behavioural control Giovanni Pezzulo, Francesco Rigoli, Karl Friston (2015) Progress in Neurobiology, 134, 17--35. Elsevier · DOI: 10.1016/j.pneurobio.2015.09.001

HITS Centrality

Authoritative sources in a hyperlinked environment Jon M Kleinberg (1999) Journal of the ACM (JACM), 46, (5), 604--632. ACM New York, NY, USA · DOI: 10.1145/324133.324140

LeCun EBM tutorial

A tutorial on energy-based learning Yann LeCun, Sumit Chopra, Raia Hadsell, Marc'Aurelio Ranzato, Fu Jie Huang (2006) Predicting Structured Data, 191--246. MIT Press

Contrastive divergence

Training products of experts by minimizing contrastive divergence Geoffrey E Hinton (2002) Neural Computation, 14, (8), 1771--1800. MIT Press · DOI: 10.1162/089976602760128018

Helmholtz machine

The Helmholtz machine Peter Dayan, Geoffrey E Hinton, Radford M Neal, Richard S Zemel (1995) Neural Computation, 7, (5), 889--904. MIT Press · DOI: 10.1162/neco.1995.7.5.889

Variational autoencoder

Auto-encoding variational Bayes Diederik P Kingma, Max Welling (2014) Proceedings of the 2nd International Conference on Learning Representations (ICLR).

Metacognitive AIF and consciousness

How do inner screens enable imaginative experience? A metacognitive active inference account Christopher J Whyte, Ryan Smith, Jakob Hohwy (2025) Neuroscience of Consciousness, 2025, (1), niaf009. Oxford University Press · DOI: 10.1093/nc/niaf009

Free Energy Projective Simulation

Free Energy Projective Simulation (FEPS): Active Inference with Interpretable Policy Graphs Marius Pazem, Simon Hangl, Justus Piater (2024) Workshop on Bridging the Gap Between Practice and Theory in Deep Learning, ICML 2024.

AIF for smart building energy control

Active Inference for Energy Control and Planning under Partial Observability Morteza Nazemi, Sanket Bhatt, Zafeirios Fountas (2025) arXiv preprint arXiv:2503.07772. DOI: 10.48550/arXiv.2503.07772

nanopub-js JavaScript library

nanopub-js: JavaScript library for nanopublications Knowledge Pixels (2026)

Publication bias

Publication decisions and their possible effects on inferences drawn from tests of significance—or vice versa Theodore D. Sterling (1959) Journal of the American Statistical Association, 54, (285), 30--34. Taylor & Francis · DOI: 10.1080/01621459.1959.10501497

Falsifiability critique of the FEP

Free energy: a user's guide Matteo Colombo, Peggy Seri\`es (2021) Biology & Philosophy, 36, (2), 1--35. Springer · DOI: 10.1007/s10539-021-09788-0

Predictive coding foundational paper: Rao & Ballard 1999

Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects Rajesh P N Rao, Dana H Ballard (1999) Nature Neuroscience, 2, (1), 79--87. Nature Publishing Group · DOI: 10.1038/4580

LLM extraction benchmarking survey

A survey of scientific information extraction with large language models Tianyi Liang, Weicheng Zhang, Chao Lin, others (2024) arXiv preprint arXiv:2401.01512.

RAG-Knowledge Graph convergence survey

A survey on RAG meeting LLMs: towards Retrieval-Augmented large language model Wenqi Fan, Yujuan Ding, Liangbo Ning, Shijie Wang, Hengyun Li, Dawei Yin, Tat-Seng Chua, Qing Li (2024) arXiv preprint arXiv:2405.06211.

Discovery Engine and Knowledge Synthesis

The Discovery Engine: A Framework for AI-Driven Synthesis and Navigation of Scientific Knowledge Landscapes Vladimir Baulin, Austin Cook, Daniel Friedman, Janna Lumiruusu, Andrew Pashea, Shagor Rahman, Benedikt Waldeck (2025) arXiv preprint arXiv:2505.17500. DOI: 10.48550/arXiv.2505.17500

Namjoshi 2026 Textbook

Fundamentals of Active Inference: Principles, Algorithms, and Applications of the Free Energy Principle for Engineers Sanjeev V Namjoshi (2026) MIT Press

Namjoshi AIF FEP DB

aif-fep-db: A database of publications related to active inference and the free energy principle Sanjeev V Namjoshi (2026)

MDPI Entropy ActiveInference.jl

Introducing ActiveInference.jl: A Julia Library for Simulation and Parameter Estimation with Active Inference Models Samuel Nehrer, others (2025) Entropy, 27, (1), 62. MDPI · DOI: 10.3390/e27010062

ResNei: Research Neighbourhood

ResNei: Solution Design Document Janna Lumiruusu, Daniel Friedman, Shagor Rahman, Vladimir Baulin, Andrew Pashea (2025) DOI: 10.5281/zenodo.16753709

FAIR Data Principles

The FAIR Guiding Principles for Scientific Data Management and Stewardship Mark D. Wilkinson, Michel Dumontier, IJsbrand Jan Aalbersberg, Gabrielle Appleton, Myles Axton, Arie Baak, Niklas Blomberg, Jan-Willem Boiten, Luiz Bonino da Silva Santos, Philip E. Bourne, others (2016) Scientific Data, 3, 160018. Nature Publishing Group · DOI: 10.1038/sdata.2016.18

Decentralized Science

A Guide to DeSci, the Latest Web3 Movement Sarah Hamburg (2022) Future.

Cognitive Load Theory

Cognitive Load Theory John Sweller, Paul Ayres, Slava Kalyuga (2011) Explorations in the Learning Sciences, Instructional Systems and Performance Technologies. Springer · DOI: 10.1007/978-1-4419-8126-4

Citizen Science

Citizen Science: A Developing Tool for Expanding Science Knowledge and Scientific Literacy Rick Bonney, Caren B. Cooper, Janis Dickinson, Steve Kelling, Tina Phillips, Kenneth V. Rosenberg, Jennifer Shirk (2009) BioScience, 59, (11), 977--984. Oxford University Press · DOI: 10.1525/bio.2009.59.11.9

Universal Design for Learning

Universal Design for Learning David H. Rose, Anne Meyer (2000) Journal of Special Education Technology, 15, (1), 67--70. DOI: 10.1177/016264340001500108

Article / manuscript describing the template/ system

A template/ approach to Reproducible Generative Research: Architecture and Ergonomics from Configuration through Publication Daniel Ari Friedman (2026) Zenodo. DOI: 10.5281/zenodo.19139090

Software framework (kept as @misc — plainnat.bst does not define @software)

docxology/template Daniel Ari Friedman (2026) DOI: 10.5281/zenodo.19139089

Project quality

Reproducibility and validation

The meta-analysis pipeline is fully tested and validated. All gates pass before any output is published.

Test suite

Tests passed: 558 / 558

Coverage: 94.8%

Pipeline stages: 7

Validation

All checks passed: Yes

Total checks: 4

Pipeline duration: 472.438

Project links

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BibTeX source ↗

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Superfici chiave

Bibliography at a glance

Meta-analysis project

The full meta-analysis codebase, pipeline, and manuscript source.

Repository

BibTeX source

The canonical references.bib file that drives this page.

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Published manuscript

The compiled document with full results, figures, and analysis.

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Active inference on discrete state-spaces: A synthesis (Da Costa et al., arXiv)

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