{"id":"1cda183b-96f4-49f6-b532-c0cd55cee1a5","arxiv_id":"2501.15105","paper_version":1,"verdict":"REJECT","confidence":"MODERATE","novelty_score":4.0,"correctness_risk":"high","formal_verification":"none","parameter_count":1,"one_line_summary":"Active inference's perception and action loops are relabeled as declarative, procedural, and conditional knowledge generation in a conceptual model with no implementation.","lead":"The paper proposes a conceptual model that maps three knowledge types, declarative, procedural, and conditional, onto the perception and action loops of active inference. It claims this free energy based framework can generate all three types of knowledge from sensory stimuli in an unsupervised way.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The central claim is unevaluable because the defining equations in Tables 3 and 4 are absent from the manuscript, and the loop-to-knowledge mapping is stipulated rather than derived.","rationale":"The strongest claim is that a single FEP-based active-inference architecture 'is capable to compute and generate concepts from stimuli' and produces declarative, procedural, and conditional knowledge. For that to be true, the manuscript must at least provide a fully specified generative model and update rules. It does not. The full text gives standard free-energy equations (8-11) but the model-specific content, Tables 3 and 4, is absent; only captions appear in Section 9. This is a correctness risk at the level of specification, not merely a stylistic flaw: no reader or reviewer can determine what computation is being proposed. The mapping of Loop I and Loop II to knowledge types is also not derived from the equations; Section 5 asserts that action-driven minimization of free energy means learning concepts that generate procedural knowledge, but no algorithm, cost function, or output criterion distinguishes procedural from declarative generation. The paper contains no implementation, no experiments, and no machine-checked proof; the one explicit parameter, λ in Eq. 1, is imported from a communication model and is not shown to control knowledge-type generation. Under the review rules, missing support and missing references must be flagged; here, the missing tables are decisive. I therefore agree with the REJECT verdict but would base it primarily on under-specification rather than only on the empirical validity of the loop mapping.","tokens_in":12711,"tokens_out":4124,"duration_ms":38359,"concrete_test":"Download the arXiv source (including any ancillary files) and inspect Tables 3 and 4 as they appear in the published PDF. If either table is missing the promised equations, the model is under-specified; if both are present, implement the generative model of Eq. 11 with those distributions on a minimal two-concept, three-stimulus task and verify that the updates produce distinct outputs corresponding to declarative, procedural, and conditional knowledge as Section 5 labels them.","verdict_should_be":"UNCHANGED","load_bearing_attack":"Section 4 and Section 5 promise a computational model: Table 3 lists variables and distributions, Table 4 lists the perception, planning, and action equations, and Figure 4 shows the Bayesian network. In the submitted full text, Section 9 contains only captions for these tables; the equations themselves are absent. Without them, the central claim that the model 'is capable to compute and generate concepts from stimuli' cannot be checked: the posterior updates, expected free energy G, likelihood mapping A, transition B, and policy prior have no specified form. The only concrete quantitative object is the communication energy (Eq. 1) with one parameter λ, which is about language cost and is not connected to procedural or conditional knowledge. Section 5's classification of Loop I as declarative, Loop II as procedural, and co-activation as conditional is a stipulative labeling of standard active-inference diagrams; it does not define what output counts as each knowledge type or how the generation differs from ordinary perceptual inference. Thus the paper's central claim rests on absent formalism, not on an empirically or computationally supported model.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","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.","tokens_in":12852,"tokens_out":6702,"duration_ms":55426,"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":[{"comment":"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.","section":"Section 9 (Tables 3 and 4; Figure 4)"},{"comment":"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.","section":"Section 5 (Figure 3)"},{"comment":"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.","section":"Entire manuscript (no simulation or worked example)"},{"comment":"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.","section":"Section 3 (Eqs. 1–7) vs. Section 5 (Table 3)"}],"minor_comments":[{"comment":"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.","section":"Throughout (Abstract and Section 1)"},{"comment":"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.","section":"References"},{"comment":"The term 'identifier effect' appears to be a typo for 'typicality effect'; consider correcting this and defining the concept.","section":"Section 2"},{"comment":"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.","section":"Section 3, Eq. (5)"},{"comment":"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.","section":"Section 4, Eq. (9)"}],"recommendation":"reject","confidential_remarks":"The submitted version appears incomplete: Section 9 contains only captions for Tables 1–4 and Figures 1–4, with no table content, equations, or diagrams. If this is a submission error, the authors should provide the full version; however, even with the missing equations, the paper would need substantial additional work (e.g., a worked example or simulation) to support its central claim. The loop-to-knowledge-type mapping is stipulative, and the paper does not engage with alternative computational accounts of procedural or conditional knowledge. This manuscript is not ready for review as a research article in its current form."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"The paper is a conceptual proposal, not a working model. It imports the communication energy function from Ferrer i Cancho and the standard active-inference free energy equations, then says Loop I is declarative knowledge, Loop II is procedural knowledge, and joining them is conditional knowledge. There is nothing wrong with the ambition, but the central claim that this generates three knowledge types is asserted, not shown. The decisive problem is structural: Section 9 contains only captions for Tables 3 and 4, which are supposed to specify the generative model, the perception equations, and the planning/action equations. Without those, the model is not defined. You cannot check the posterior updates, the expected free energy, or the likelihood and transition mappings. The claim that the model is capable to compute and generate concepts from stimuli is therefore unevaluable.\n\nTo give credit where it is due: the paper addresses a real gap, most knowledge-generation models only cover declarative knowledge, and an integrated treatment of all three types under one Bayesian framework would be useful. The literature review is adequate, and the authors explicitly acknowledge the FEP and active-inference sources from which they draw. The motivation is clear, and the use of the A matrix to relate concepts and stimuli is a reasonable starting point.\n\nThe soft spots are large. The loop-to-knowledge mapping is stipulative: nothing in the model defines what output counts as declarative, procedural, or conditional knowledge, or how generation differs from ordinary perceptual inference. There are no simulations, no worked examples, and no independent evidence for the mapping. The notation is inconsistent: the A matrix is binary in one place and probabilistic in another, and the text moves between p(si,rj) and a binary aij without reconciling them. The optimal lambda value is imported from earlier work and plays no role in the procedural or conditional knowledge claims. In short, the paper is a relabeling of existing equations with the actual formalism left out.\n\nWho is this for? A reader who wants a high-level sketch of how someone might map active inference onto knowledge categories could get something from the conceptual parts. But a serious researcher would need the missing equations and a demonstration that the model does what is claimed. As it stands, the paper is not ready for peer review. It should be desk-rejected, with encouragement to resubmit if the authors supply the tables and either simulate the model or provide a derivation that the loop structure actually produces the three knowledge types. If that happens, the core idea could become testable.","headline":"A conceptual sketch that relabels standard active-inference equations as three knowledge types, but omits the tables that would make the model checkable.","tokens_in":742,"tokens_out":3420,"would_cite":false,"duration_ms":44200,"reading_group":"no","serious_thinker":"unclear","would_accept_peer_review":false},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"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.","keywords":["knowledge generation","active inference","free energy principle","semantic networks","declarative knowledge","procedural knowledge","conditional knowledge","Bayesian inference"],"falsifier":"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.","tokens_in":12477,"feed_emoji":"🧠","tokens_out":4865,"duration_ms":42361,"temperature":0.7,"pith_summary":"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.","feed_headline":"Active inference model yields all three knowledge types","feed_subtitle":"Perception makes declarative knowledge; action makes procedural; both together make conditional.","key_machinery":"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.","core_discovery":"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.","pith_inferences":["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."],"forward_implications":["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."],"supporting_citations":[{"why":"Supplies the information-transfer energy function and the λ≈0.41 optimal balance used to define the concept-generation matrix.","marker":"[14]"},{"why":"Provides the free-energy principle as prediction-error minimization, which underpins the model's perceptual and action loops.","marker":"[17]"},{"why":"Frames the free-energy principle as a unified brain theory that motivates extending the model beyond declarative knowledge.","marker":"[18]"},{"why":"Supplies the free-energy formulation for perception and action that the two-loop structure is built on.","marker":"[22]"},{"why":"Provides the active-inference perception, planning, and action equations used for procedural and conditional knowledge.","marker":"[37]"},{"why":"Supplies active-inference structure learning that the model borrows for unsupervised generation of new concepts.","marker":"[48]"},{"why":"Supplies the Dirichlet-process Bayesian nonparametric machinery used to update the model with new observations and generate new concepts.","marker":"[2]"}],"fun_headline_variants":["Active inference model generates three knowledge types","Active inference yields declarative, procedural, conditional knowledge","Free energy principle model generates all knowledge types","Unsupervised active inference model creates new concepts"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"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.","fun_headline_variants_meta":{"raw":{"variants":["Active inference model generates three knowledge types","Active inference yields declarative, procedural, conditional knowledge","Free energy principle model generates all knowledge types","Unsupervised active inference model creates new concepts"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000597,"raw_usage":{"total_tokens":2755,"prompt_tokens":872,"completion_tokens":1883,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":488,"completion_tokens_details":{"reasoning_tokens":1826}},"tokens_in":488,"tokens_out":1883,"duration_ms":12800,"temperature":1.0,"reasoning_tokens":1826,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-10T14:36:24.135473+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"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.","supporting_citations":[{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Provides the active-inference perception, planning, and action equations used for procedural and conditional knowledge."}],"review_version":1}