Evidence map›Paper›PMID 40607319›Full record

ArticleFrontiers in network physiology2025

From pixels to planning: scale-free active inference.

Karl Friston, Conor Heins, Tim Verbelen, Lancelot Da Costa, Tommaso Salvatori, Dimitrije Markovic, Alexander Tschantz, Magnus Koudahl, Christopher Buckley, Thomas Parr

Abstract read
In one paragraph

Article in Frontiers in network physiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

  1. Active inference and artificial reasoning.Nature communications · 2026
    Article
  2. The active construction of past episodes.Translational neuroscience · 2026
    Article
  3. Article
  4. Article
  5. Review
  6. Gradient-Free De Novo Learning.Entropy (Basel, Switzerland) · 2025
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Karl FristonQueen Square Institute of Neurology, University College London, London, United Kingdom.
Conor HeinsVERSES Research Lab, Los Angeles, CA, United States.
Tim VerbelenVERSES Research Lab, Los Angeles, CA, United States.
Lancelot Da CostaQueen Square Institute of Neurology, University College London, London, United Kingdom.
Tommaso SalvatoriVERSES Research Lab, Los Angeles, CA, United States.
Dimitrije MarkovicVERSES Research Lab, Los Angeles, CA, United States.
Alexander TschantzVERSES Research Lab, Los Angeles, CA, United States.
Magnus KoudahlVERSES Research Lab, Los Angeles, CA, United States.
Christopher BuckleyVERSES Research Lab, Los Angeles, CA, United States.
Thomas ParrNuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper describes a discrete state-space model and accompanying methods for generative modeling. This model generalizes partially observed Markov decision processes to include paths as latent variables, rendering it suitable for active inference and learning in a dynamic setting. Specifically, we consider deep or hierarchical forms using the renormalization group. The ensuing

Indexed as

active inferenceactive learningBayesian model selectioncompressionnetwork-physiologyrenormalization groupstructure learning

Identifiers

PMID40607319
PMCPMC12217590

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.