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REVIEW 4 major objections 5 minor 52 references

A New Approach for Knowledge Generation Using Active Inference

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read The paper proposes a single free-energy-based model that generates declarative, procedural, and conditional knowledge from sensory stimuli, unifying three separate knowledge categories into one inferential architecture.

desk verdict A conceptual sketch that relabels standard active-inference equations as three knowledge types, but omits the tables that would make the model checkable. read the letter →

arxiv 2501.15105 v1 pith:Y27T3QCC submitted 2025-01-25 cs.AI q-bio.NC

Jamshid Ghasimi , Nazanin Movarraei This is my paper · ORCID
classification cs.AIq-bio.NC
keywords knowledgegenerationactiveinferencefreeenergyprinciplesemanticnetworksdeclarativeproceduralconditionalBayesian
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper proposes that the brain's free-energy principle can be extended from mere perception into a full model of knowledge generation. The central claim is that a single generative model, running two loops—a perceptual loop and an action loop—can produce declarative, procedural, and conditional knowledge from sensory stimuli. If correct, this would replace the semantic-network model, which only explains declarative knowledge, with a unified computational account of how concepts, skills, and conditional rules are all learned. The authors argue that the resulting model is an unsupervised learning system that generates new concepts when novel stimuli arrive, using Bayesian nonparametric updating.

What carries the argument

The central object is the two-loop active-inference architecture: Loop I is perception-driven Bayesian inference that updates predictions to reduce prediction error (declarative knowledge); Loop II is action-driven active inference that selects policies and actions to change sensory input (procedural knowledge); and the simultaneous operation of both loops yields conditional knowledge. The machinery is completed by a generative matrix A linking stimuli to concepts, an energy function Ω(λ) balancing information transfer and concept entropy, and Dirichlet-process Bayesian nonparametric updating that lets new stimuli create new concepts.

What would settle it

Observe a case where an agent acquires procedural knowledge (a skill) without any action on the environment—for example, by purely observational learning—or where declarative knowledge is acquired through an action-driven process. If such learning occurs, the strict loop-to-knowledge mapping in the model is violated.

Watch

Extended reading notes

Core claim

The paper presents the FEP knowledge generation model, in which hidden concepts are inferred from stimuli through Bayesian and active inference. Declarative knowledge is attributed to Loop I, where perception updates predictions to reduce prediction error; procedural knowledge is attributed to Loop II, where active inference selects policies and actions to change sensory input; and conditional knowledge emerges when both loops operate simultaneously. The model represents concepts as a matrix of stimulus associations and frames concept formation as minimizing an information-transfer energy function with an optimal policy at λ≈0.41, showing how a generative model can both compute existing knowledge and generate new concepts from unsupervised stimuli.

Load-bearing premise

The load-bearing assumption is that the brain's perceptual loop produces declarative knowledge and its action loop produces procedural knowledge, with conditional knowledge when both run together; if that mapping does not hold empirically, the model's claim to generate all three knowledge types collapses even though its equations may be internally consistent.

Editorial extensions

If this is right

  • The same computational architecture that explains declarative concept formation can also account for skill acquisition and for knowing when to apply a skill.
  • Removing the action loop from the model reduces it to a semantic-network-style declarative knowledge generator, so active inference is the ingredient that adds procedural and conditional knowledge.
  • The model implies that knowledge generation is unsupervised: new concepts emerge from novel stimuli through Bayesian nonparametric updating, without labeled examples.
  • Because the model is computational, it can be ported to intelligent machines that learn concepts, skills, and conditionals from sensory data by minimizing free energy.
  • Learning in this model is equivalent to updating a generative model of concepts, so knowledge growth can be measured as a reduction in prediction error or free energy.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper does not specify how the two loops are neurologically implemented; taking the mapping seriously would predict that damage to action-related circuitry would selectively impair procedural but not declarative learning, a testable dissociation.
  • The λ≈0.41 policy choice is an assumed balance between information and entropy; different environments might require different λ values, which the paper does not address.
  • Because the model treats concepts as discrete hidden variables over a continuous stimulus space, it suggests a concrete mechanism for abstraction and generalization: the mapping from continuous sensations to discrete concepts is itself the product of free-energy minimization.
  • The paper's two-loop architecture could be extended to account for metacognitive or strategic knowledge, where an agent explicitly reasons about which loop to engage, though the paper does not explore this.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The manuscript proposes a computational model of knowledge generation based on the free energy principle (FEP) and active inference, claiming to unify declarative, procedural, and conditional knowledge. It reviews semantic networks as a model of declarative knowledge, introduces a concept–stimulus generative matrix A and an information-transfer energy function, and then extends the standard active-inference perception–action loops into a two-loop architecture (Figure 3), labeling Loop I as declarative, Loop II as procedural, and their combination as conditional knowledge. The paper asserts that the model is unsupervised, can generate new concepts from stimuli, and can be updated using Bayesian nonparametric methods. However, the defining equations, variable tables, and figure content are not present in the submitted text (Section 9 contains only captions), and no simulation, worked example, or empirical validation is provided. The central claim therefore cannot be evaluated from the manuscript as written.

Significance. If the proposed model were fully specified and validated, it would offer a genuinely integrative account of three knowledge types within a single active-inference framework, with potential applications to cognitive modeling and machine learning. The paper does provide a useful high-level review of semantic networks and an informal analogy between semantic network generation and free-energy minimization. On the negative side, the paper ships no machine-checked proofs, no reproducible code, no parameter-free derivation, and no falsifiable prediction; the central formalism is missing. The contribution as presented is a conceptual proposal, not a working model.

major comments (4)
  1. [Section 9 (Tables 3 and 4; Figure 4)] The defining content of the proposed generative model is absent: Section 9 lists only captions for Tables 3 and 4 and Figures 1–4, with no actual tables, diagrams, or equations. In particular, the variables and distributions of the concept generation model, the perception/planning/action equations, the expected free energy G, the likelihood A, the transition B, and the initial prior D are never specified. Without these, the claim in the Abstract that the model 'is capable to compute and generate concepts from stimuli based on probabilistic mathematics' cannot be checked, and the paper cannot be considered a computational model.
  2. [Section 5 (Figure 3)] The classification of Loop I as 'generation of declarative knowledge', Loop II as 'generation of procedural knowledge', and 'Combining two loops' as 'generation of conditional knowledge' is stipulated rather than derived. The text does not define what output or criterion constitutes each knowledge type, nor does it explain how the co-activation of the two loops produces a distinct third type beyond the simple union of the two processes. The conclusion that the model generates all three knowledge types therefore follows from the labeling of the loops, not from an independent derivation or an empirical test.
  3. [Entire manuscript (no simulation or worked example)] No worked example, simulation, or application of the model is given. The paper claims unsupervised learning and concept generation, but there is no demonstration on a concrete stimulus–concept task, no specification of how the Dirichlet process or categorical distributions would be used in practice, and no comparison against alternative models or baselines. This makes the central claim untestable as written.
  4. [Section 3 (Eqs. 1–7) vs. Section 5 (Table 3)] The notation for the concept–stimulus mapping is internally inconsistent. Section 3 defines the A matrix with binary entries aij ∈ {0,1} and represents each concept as a binary vector, while Equations 3–5 and the surrounding text treat the relation as a joint probability p(si,rj) and call the 'p(si,rj) matrix' the generative model. Table 3, if present, is said to use categorical and Dirichlet distributions, which again conflicts with the binary A matrix. This ambiguity affects the interpretation of the generative model, the likelihood, and the update rules.
minor comments (5)
  1. [Throughout (Abstract and Section 1)] There are numerous grammatical errors and typos, e.g., 'is the researchers proposed', 'because of has been formed', and 'T able1'. The manuscript needs careful editing.
  2. [References] There are several citation errors, including misspelled author names ('Harison' for Harrison, 'Perrit' for Parr), corrupted titles ('Active inference: demystified and compred'), and malformed DOIs. The reference list should be checked and formatted consistently.
  3. [Section 2] The term 'identifier effect' appears to be a typo for 'typicality effect'; consider correcting this and defining the concept.
  4. [Section 3, Eq. (5)] The expression 'Ω(λ)/λ' is notational nonstandard; it should be rewritten to show the energy function directly, and the derivation of λ≈0.41 should be given or cited precisely.
  5. [Section 4, Eq. (9)] The free-energy expressions are not fully defined; in particular, the symbol q is used without specifying its parameterization, and the 'Action to minimize the bound on surprise' expression conflates variational free energy with expected free energy. Clarify the notation.

Circularity Check

2 steps flagged · score 6.0 of 10

The core knowledge-generation claim is stipulated: Figure 3 defines declarative knowledge as Loop I, procedural as Loop II, and conditional as their co-activation, so the model's 'prediction' of three knowledge types follows by construction.

  1. self definitional [Section 5, Figure 3 caption]
    "Loop I: generation of declarative knowledge (without active inference and independent of Loop II) Loop II: generation of procedural knowledge (with active inference and connect to Loop I) Combining two loops: generation of conditional knowledge"

    The three knowledge categories are stipulated to be the two loops and their combination. The paper then presents the model as generating these categories: passive perception is said to yield declarative knowledge, active inference to yield procedural knowledge, and simultaneous activity of both loops to yield conditional knowledge. No independent criterion (behavioral, computational, or empirical) is supplied for what output counts as each type, so the abstract's central claim is a restatement of these labels rather than a derivation from the FEP equations.

  2. self definitional [Section 5, paragraph 4]
    "If both loops are active the agent can perform the desired actions on the environment (usually automatically) simultaneously with the extraction and application of concepts from semantic memory that are already under the process of active inference, perceived and updated them in their brain, it means the generation of conditional knowledge."

    Conditional knowledge is explicitly defined as the co-activation of Loop I and Loop II. Consequently, the claim that the model 'can also generate conditional knowledge' is guaranteed by construction: whenever both loops are active, the resulting state is called conditional knowledge. The conclusion requires no computation beyond the labels in Figure 3, and the missing equations in Tables 3 and 4 cannot add an independent derivation.

full rationale

The paper's derivation chain from FEP principles to knowledge categories breaks at the labeling step. Figure 3 identifies Loop I with declarative knowledge, Loop II with procedural knowledge, and their co-activation with conditional knowledge; Section 5 then treats these identifications as the model's outputs. The Conclusion makes this explicit: 'By classifying it into two loops of declarative knowledge generator and procedural knowledge generator, a model was proposed.' Thus the central advertised result is true by definition. I do not count the missing Table 3/Table 4 equations as circularity; that is a completeness or verifiability defect. The imported λ≈0.41 value from [14,53] is external support, not a self-citation or fitted-input circularity. No load-bearing self-citation chain by the authors appears. The score is 6 rather than higher because the model does embed standard active-inference mathematics (Equations 9-11, A/B/D matrices), so the circularity is in the knowledge-type mapping rather than in every component of the paper.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The central claims rest on imported assumptions from free energy and active inference literature. The paper contributes no new independent evidence, and the only explicit free parameter (lambda) is taken from prior work. The mapping of loops to knowledge types is a stipulation rather than a derived result.

free parameters (1)
  • lambda (trade-off parameter) = ~0.41 (from prior literature)
    In the energy function (Equation 1), lambda controls the balance between information transfer and entropy minimization. The value 0.41 is cited from Ferrer i Cancho and Zipf's law, not derived in this paper.
assumptions (4)
  • domain assumption The brain minimizes free energy (FEP) to generate concepts and guide behavior.
    The entire model is built on the free energy principle, presented without proof in Section 4, citing Friston et al.
  • domain assumption Concepts are hidden variables inferred from sensory stimuli via a Markov process.
    Sections 2 and 3 assert that semantic networks and concept generation follow a Markov process with latent concepts, an assumption used to connect semantic networks to FEP.
  • domain assumption The information transfer energy function (Equation 1) describes concept-stimulus communication.
    The paper adopts this equation and the optimal lambda value from prior work, without derivation here.
  • standard math The generative model of Equation 11 (a POMDP factorisation) is the correct form for concept generation under active inference.
    This is the standard active inference generative model, lifted from the cited literature, with no proof or adaptation for the specific knowledge-generation setting.

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Cite this review

Pith. "Pith review of A New Approach for Knowledge Generation Using Active Inference." pith.science (2026). https://pith.science/paper/Y27T3QCC

@misc{pith2026250115105,
  author       = {Pith},
  title        = {Pith review of: A New Approach for Knowledge Generation Using Active Inference},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Y27T3QCC}},
  note         = {Machine review of arXiv:2501.15105}
}
read the original abstract

There are various models proposed on how knowledge is generated in the human brain including the semantic networks model. Although this model has been widely studied and even computational models are presented, but, due to various limits and inefficiencies in the generation of different types of knowledge, its application is limited to semantic knowledge because of has been formed according to semantic memory and declarative knowledge and has many limits in explaining various procedural and conditional knowledge. Given the importance of providing an appropriate model for knowledge generation, especially in the areas of improving human cognitive functions or building intelligent machines, improving existing models in knowledge generation or providing more comprehensive models is of great importance. In the current study, based on the free energy principle of the brain, is the researchers proposed a model for generating three types of declarative, procedural, and conditional knowledge. While explaining different types of knowledge, this model is capable to compute and generate concepts from stimuli based on probabilistic mathematics and the action-perception process (active inference). The proposed model is unsupervised learning that can update itself using a combination of different stimuli as a generative model can generate new concepts of unsupervised received stimuli. In this model, the active inference process is used in the generation of procedural and conditional knowledge and the perception process is used to generate declarative knowledge.

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.