{"id":"d0afda51-55e3-4f8d-b2a6-16451b27d5cc","arxiv_id":"2505.13551","paper_version":2,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":4.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":3,"one_line_summary":"A conceptual framework attributing counter-inferential rigidity in natural and artificial cognitive systems to an imbalance between stability and adaptability rewards.","lead":"This paper argues that rigid, change-resistant behavior in AI agents, animals, and people can emerge from a common mechanism: the balance between rewards for stability and rewards for adapting. It proposes three scenarios where this balance tips, and suggests a design principle to keep systems adaptable even under stable conditions.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The emergence claim in Sec. 5.2 is unfalsifiable as stated: B is left open in Sec. 3.2 and all three mechanism equations reduce to ΔB→Rs, so the taxonomy can absorb any observed rigidity post hoc; a formalized B and a controlled simulation test are needed.","rationale":"The reader's weakest assumption—scalar reward balance with open B—is the same core concern I identify. I agree with CONDITIONAL: the taxonomy is useful and well-referenced, but the strong emergence claim is not yet demonstrated. The paper is honest about leaving B open and deferring formalization, which supports conditional rather than unconditional acceptance. I would not reject because the framework is explicitly a conceptual synthesis and prior literature (stability-plasticity, computational rationality) gives it plausibility. The proposed simulation would settle whether the framework has predictive teeth, i.e., whether the systematic-consequence claim can be made falsifiable.","tokens_in":14919,"tokens_out":3075,"duration_ms":34383,"concrete_test":"Specify an explicit B(RS, RA; λ) and a meta-cognitive layer implementing Meta-assert when ρ(M,E) > θ, then run an agent in a stable environment followed by an abrupt shift. Measure the probability and latency of updating after the shift, comparing agents with and without the meta-layer across a sweep of λ and θ. The claim in Sec. 5.2 requires a systematic, reproducible increase in counter-inferential behavior (e.g., delayed updating) with stability and θ; if no such increase appears, or if it appears only for engineered parameter choices, the 'regular outcome' claim fails.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim (Sec. 5.2) is that counter-inferential behavior is 'a systematic consequence' of reward structures, stability pressures, and environment, not noise or malfunction. For that claim to be more than a redescription, the model must generate predictions. It does not. In Sec. 3.2 the balance function B is explicitly left open ('we leave the specific form of B open'), so the framework is compatible with any observed stability-adaptability trade-off. The three mechanism expressions in Secs. 4.1-4.3 are not derivations: each concludes 'ΔB(RS, RA) → Rs', differing only in unformalized antecedents (η·Sf, a meta-assert triggered by ρ(M,E)≫θ, and a fragility predicate). No thresholds, dynamics, or measurement procedures are specified for ρ, Meta-assert, or Fragile. In Sec. 5, evidence consists of post-hoc analogies (habituation, status quo bias, groupthink, etc.); none is produced or predicted by the reward-balance model, and no observation is described that could disconfirm it, since any rigidity can be attributed to one of the three scenarios. The claim that rigidity is a 'relatively regular outcome' therefore restates the taxonomy rather than establishing emergence. The paper's own Sec. 6 defers formalization to future work, confirming that the systematic-consequence claim currently lacks support rather than being internally inconsistent.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes a unified information-dynamic account of counter-inferential behavior, defined as patterns in which natural or artificial cognitive systems misattribute empirical success or suppress adaptation, yielding epistemic rigidity. It introduces the notion of Autonomous Sustainable Information Models (ASIMs), postulates two cognitive rewards (stability RS and adaptability RA) whose balance B governs updating decisions, and then analyzes three mechanisms—success saturation, overconfidence bias, and inner fragility—each of which shifts the balance toward stability. The paper surveys empirical evidence across artificial systems, animal behavior, human psychology, and collective/social systems, and claims in Section 5.2 that counter-inferential behavior is a systematic, sometimes rational emergent consequence of reward structures, model stability pressures, and environmental conditions. It concludes with a design principle (minimal adaptive activation) and explicitly defers formalization of the framework to future work.","tokens_in":15279,"tokens_out":3052,"duration_ms":33882,"significance":"If the central claim were supported, the paper would provide a valuable cross-domain unification of phenomena as diverse as overfitting, status quo bias, habituation, learned helplessness, and institutional rigidity, all under a single reward-balance mechanism. The three-scenario taxonomy is a useful conceptual organizer, and the literature review, while selective, draws on a broad range of fields. A notable strength is the paper's transparency: Section 3.2 openly leaves the balance function B unspecified, and Section 6 states that formalization is future work. However, as it stands the paper is better characterized as a conceptual redescription than a testable model: the load-bearing claim of emergence is not derived, and no disconfirming observation is proposed. The contribution is therefore preliminary but potentially constructive for future formal and computational work.","major_comments":[{"comment":"The central claim that counter-inferential behavior is a 'systematic consequence' of reward structures, stability pressures, and environment is not supported by the stated framework. Because the balance function B is explicitly left open in Eq. (2), and because the three mechanism expressions in Sections 4.1-4.3 all reduce to ΔB(RS,RA) → RS with unformalized antecedents, the framework is compatible with any observed rigidity: every instance can be assigned post hoc to one of the three scenarios. No observation is described that would disconfirm the framework, so the Sec. 5.2 claim currently restates the taxonomy rather than establishing emergence. A formal or at least semi-formal specification of B and the triggering conditions is necessary before the emergence claim acquires predictive content.","section":"Section 3.2 (Eq. 2) and Section 5.2"},{"comment":"The three mechanism equations are asserted, not derived from the stated assumptions in Section 3. For example, ΔB(RS,RA) → RS ~ η Sf introduces an unspecified constant η and an unmodeled empirical success frequency Sf; the overconfidence expression ρ(M,E) ≫ θ ⇒ Meta-assert(M ≡ M*) ⇒ ΔB(RS,RA) → RS relies on an unmeasured 'assessment threshold' θ and a relation ρ whose definition and measurement are not given; and the fragility expression dE/dt ↘ and ΔM(τ) ↗ ⇒ Fragile(M(τ)) ⇒ ΔB(RS,RA) → RS uses predicates (Fragile, ΔM) with no operational semantics. As written, these are schematic placeholders rather than mechanisms, so the paper's claim in Section 5.2 that these are 'mechanistic' connections across domains is not justified.","section":"Section 4.1 (Eq. 3), 4.2, 4.3"},{"comment":"The empirical review consists of post-hoc analogies: known phenomena (habituation, status quo bias, learned helplessness, groupthink, etc.) are mapped onto the three scenarios after the fact. The paper does not specify which phenomena should map to which scenario under which conditions, nor does it compare the framework's predictions against alternative explanations, nor does it report any quantitative or experimental evaluation. Thus the cross-domain evidence does not discriminate the proposed account from a generic stability-plasticity framing. The paper would need at least one concrete, testable prediction (e.g., a simulation with an instantiated B, or a controlled experiment comparing conditions that should trigger one scenario versus another) to support the 'systematic consequence' claim.","section":"Section 5 and Sections 5.1-5.2"}],"minor_comments":[{"comment":"Equation (1) defining RC is printed twice; the duplicate should be removed.","section":"Section 3.2"},{"comment":"The citation [24] is used to support 'overconfidence in predictive coding systems,' but [24] is the Madry et al. adversarial robustness paper, which does not discuss overconfidence; a correct reference is needed.","section":"Section 5.1 (Artificial Cognitive Systems)"},{"comment":"The text 'Limited cognitive or computational capacity, architectural sh' contains a truncation; 'architectural sh' should be completed (e.g., 'architectural shortcuts' or 'architectural shifts').","section":"Section 4.3"},{"comment":"The phrase 'but prone to overwriting prior knowledge (less unstable)' appears to be a typo; it should likely read 'unstable' or 'less stable'.","section":"Section 2"},{"comment":"'Sections 6 provides' should be 'Section 6 provides.'","section":"Section 6"},{"comment":"The paper would benefit from a consolidated table of notation for RS, RA, B, M(τ), Sf, θ, and related quantities, as several are introduced informally in prose rather than defined once in one place.","section":"Throughout"}],"recommendation":"major_revision","confidential_remarks":"The manuscript is a conceptual position paper rather than a technical contribution. The journal may wish to consider whether such a paper fits its scope for theoretical contributions, especially given the lack of formalization. The author's self-citation [54] is not problematic in itself, but the paper relies heavily on a prior framework that is not summarized sufficiently for a standalone reader. My main concern is that the emergence claim in Sec. 5.2 is presented as a central result despite the paper's own admission that the framework is unformalized; a revision that either supplies a formal model and testable predictions or substantially tempers the claim would be needed for me to support acceptance."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"You should know upfront: this is a taxonomy paper, not a result paper. There is no new data, no simulation, and no derived model. What it does well is organize a large, disparate literature around three named mechanisms — success saturation, overconfidence, and inner fragility — and the writing is unusually clear for a cross-domain synthesis. The design principle it ends on, preserving an exploration baseline even under stable conditions, is sensible and actionable. The author also deserves credit for honestly deferring formalization to future work.\n\nThe soft spots are real but proportionate. The balance function B is explicitly left open in Section 3.2, which means the framework cannot yet generate predictions. The three mechanism expressions in Section 4 are not derivations; they are labeled arrows with unspecified thresholds and constants. And the evidence in Section 5 is post-hoc analogy: every example is chosen to illustrate an already-named mechanism, so the taxonomy can absorb virtually any observed rigidity. That makes the strong claim in Section 5.2 — that counter-inferential behavior is a \"systematic consequence\" rather than noise or malfunction — currently unsupported, even if it is plausible.\n\nThat said, the stress-test note is right that the lack of a concrete B is the load-bearing issue, but I would not call the paper unfalsifiable in principle. A concrete B could be tested. The problem is that none is offered. The citation pattern looks fine; the relevant stability-plasticity, active inference, and cognitive bias literature is cited, and the one self-citation is not inappropriate.\n\nWho is this for? People who want a conceptual map of rigidity phenomena across AI, biology, and psychology, and who are comfortable treating it as a starting point rather than a demonstrated theory. It deserves a serious referee, but the referee should ask the author to either instantiate B for at least one toy model or explicitly reframe the paper as a perspective piece with softer claims. I would not cite it for a result, but I would cite it as a useful taxonomy. Send it out, but expect revision.","headline":"A clear conceptual synthesis of rigidity phenomena under a reward-balance taxonomy, but the central emergence claim is not yet supported because the balance function is left open.","tokens_in":15757,"tokens_out":1398,"would_cite":true,"duration_ms":16778,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Counter-inferential bias — misattributing success or suppressing adaptation — is a systematic emergent outcome of stability-adaptability reward dynamics across natural and artificial cognitive systems.","keywords":["counter-inferential behavior","stability-plasticity dilemma","cognitive rewards","epistemic rigidity","meta-cognition","adaptive systems","active inference","reward balance"],"falsifier":"A controlled experiment could settle it: train a reinforcement-learning agent in a stationary environment, then switch the reward structure, and measure whether the delay in updating grows with the length of prior success and with the strength of the agent's self-confidence signal; if rigidity appears with no such reward-driven dependence, or disappears when rewards are balanced, the reward-balance mechanism is falsified.","tokens_in":14684,"feed_emoji":"🧠","tokens_out":4271,"duration_ms":47977,"temperature":0.7,"pith_summary":"The paper proposes a unified account of why intelligent systems, natural and artificial, sometimes ignore or suppress evidence that should update their beliefs. It claims that such counter-inferential behavior emerges from the balance between two cognitive rewards, one for keeping the internal model stable and one for adapting to new information, rather than from random noise or faulty design. The authors identify three recurring scenarios: stability reinforced by prolonged success, metacognitive attribution of success to the model's own superiority, and defensive rigidity when the model feels fragile. If correct, epistemic rigidity is a systematic vulnerability of advanced cognition, and preserving a baseline of adaptability becomes a design principle for resilient systems.","feed_headline":"Epistemic rigidity is a reward-rational response, not just a bug","feed_subtitle":"Three routes to rigidity, traced to a stability-adaptability reward imbalance in minds and machines.","key_machinery":"The central object is the balance function $B(R_S, R_A)$, defined on the space of cognitive states and sensory stimuli, which maps a stability reward $R_S$ and an adaptability reward $R_A$ to a scalar influence on decision-making; the paper explicitly leaves the form of $B$ open. It carries the argument because all three mechanisms are described as shifts of this balance toward $R_S$ (written as $\\Delta B \\to R_S$), making reward imbalance the common cause behind otherwise diverse rigid behaviors. The framework also introduces the class of Autonomous Sustainable Information Models (ASIM), models that persist with minimal empirical feedback, and uses that class to define which systems are susceptible to counter-inferential dynamics.","core_discovery":"The paper's central claim is that counter-inferential behavior is \"not merely a product of accidental malfunction or random noise\" but can emerge as a \"relatively regular outcome\" of specific configurations of cognitive and sensory dynamics. The theoretical heart is a reward balance $B(R_S, R_A)$: at each cognitive state the system weighs a stability reward $R_S$ against an adaptability reward $R_A$, and when $R_S$ dominates, the system avoids new information, filters evidence, or prioritizes internal coherence. Three mechanisms drive that dominance: success saturation, where repeated empirical success reinforces the current model; overconfidence, where a meta-cognitive layer asserts the model is optimal or final; and inner fragility, where perceived model brittleness triggers protective suppression of updates. The paper gathers parallels from machine learning, animal behavior, human psychology, and social institutions to argue that the bias is cross-domain, systematic, and often rational within the system's own reward structure.","pith_inferences":["A testable extension: in a reinforcement-learning agent, the weight of the stability reward should rise monotonically with the duration of uninterrupted success and predict a measurable drop in exploration, which could be checked in a bandit or MDP environment with an abrupt regime shift.","If the fragility mechanism is right, agents under a high rate of forced model updates should show active suppression of informative input even when that input is reliable, a prediction that distinguishes the account from simple overfitting.","One concrete formalization of the open balance function would be $B = R_S - \\lambda R_A$ with $\\lambda$ adjusted by perceived environmental volatility; this would yield quantitative predictions about when rigidity should appear and how fast it should reverse after environmental change.","The social-system analogy suggests a comparative historical test: institutions with long success records and strong self-narratives should respond more slowly to warning signals than comparable institutions without such reinforcement, which could be examined with archival case data."],"forward_implications":["Rigidity in artificial systems should often be treated as reward-rational behavior, so mitigations such as entropy regularization, exploration baselines, or uncertainty bonuses can be designed deliberately rather than applied as ad hoc fixes.","Long periods of stable success become a recognized risk factor: if the stability reward grows with empirical success, agents should maintain a minimal adaptive activation channel even when the environment appears unchanged.","Meta-cognitive self-evaluation layers can amplify bias, so architectures that feed confidence estimates directly into reward tuning need decoupling or regularization to avoid self-reinforcing stasis.","Counter-inferential bias is expected to appear across biological, human, and artificial systems as a general property of bounded information processing, not as a domain-specific failure mode.","Institutional and social inertia may share the same information-dynamic source, suggesting that interventions should target reward and reinforcement structures rather than only individual beliefs."],"supporting_citations":[{"why":"Defines the stability-plasticity dilemma that the paper uses as the background tension between retaining knowledge and incorporating new inputs.","marker":"[1]"},{"why":"Supplies the framing of basic functional trade-offs in cognition, which the paper extends to reward-based regulation.","marker":"[4]"},{"why":"Provides the active-inference notion of high-precision priors as inertia, supporting the stability side of the reward balance.","marker":"[9]"},{"why":"Documents anti-Bayesian updating in perception and cognition, giving the paper its core phenomenon to explain.","marker":"[12]"},{"why":"Contributes the free-energy principle, which the paper uses to explain how systems settle into stable, surprise-minimizing states.","marker":"[23]"},{"why":"Supplies evidence of status quo bias in human decision-making, supporting the success-saturation mechanism.","marker":"[35]"},{"why":"Provides the confirmation-bias literature as evidence that humans preferentially seek and value belief-consistent information.","marker":"[36]"},{"why":"Documents the Dunning-Kruger effect, supporting the overconfidence mechanism where success is attributed to internal superiority.","marker":"[38]"},{"why":"Grounds the claim that rigidity can be computationally rational within a system's own reward structure.","marker":"[55]"},{"why":"Supports the broader idea of shared organizational principles across adaptive systems, which the paper invokes for cross-domain recurrence.","marker":"[56]"}],"fun_headline_variants":["Stability rewards can turn success into rigidity","Why smart systems sometimes refuse to adapt","The rational trap: when stability beats adaptability","Not a bug: rigidity evolves from reward balance","Across minds and machines, a common rigidity bias"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The entire account rests on the premise, stated in Section 3.2, that a cognitive system's choices can be effectively modeled as a balance between a stability reward and an adaptability reward, with the balance function itself left open; if real cognitive systems do not reduce to such a scalar trade-off, the unified explanation collapses.","fun_headline_variants_meta":{"raw":{"variants":["Stability rewards can turn success into rigidity","Why smart systems sometimes refuse to adapt","The rational trap: when stability beats adaptability","Not a bug: rigidity evolves from reward balance","Across minds and machines, a common rigidity bias"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000258,"raw_usage":{"total_tokens":1549,"prompt_tokens":878,"completion_tokens":671,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":494,"completion_tokens_details":{"reasoning_tokens":604}},"tokens_in":494,"tokens_out":671,"duration_ms":7400,"temperature":1.0,"reasoning_tokens":604,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-15T20:27:49.756019+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"A controlled experiment could settle it: train a reinforcement-learning agent in a stationary environment, then switch the reward structure, and measure whether the delay in updating grows with the length of prior success and with the strength of the agent's self-confidence signal; if rigidity appears with no such reward-driven dependence, or disappears when rewards are balanced, the reward-balance mechanism is falsified.","supporting_citations":[{"cited_title":"The stability-plasticity dilemma: investigating the con- tinuum from catastrophic forgetting to age -limited learning effects","cited_arxiv_id":null,"evidence_quote":"Defines the stability-plasticity dilemma that the paper uses as the background tension between retaining knowledge and incorporating new inputs."},{"cited_title":"Basic functional trade-offs in cognition: an integrative framework","cited_arxiv_id":null,"evidence_quote":"Supplies the framing of basic functional trade-offs in cognition, which the paper extends to reward-based regulation."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Provides the active-inference notion of high-precision priors as inertia, supporting the stability side of the reward balance."},{"cited_title":"Anti - Bayesian","cited_arxiv_id":null,"evidence_quote":"Documents anti-Bayesian updating in perception and cognition, giving the paper its core phenomenon to explain."},{"cited_title":"The Free-Energy Principle: a unified brain theory? Nat","cited_arxiv_id":null,"evidence_quote":"Contributes the free-energy principle, which the paper uses to explain how systems settle into stable, surprise-minimizing states."},{"cited_title":"Status quo bias in decision making","cited_arxiv_id":null,"evidence_quote":"Supplies evidence of status quo bias in human decision-making, supporting the success-saturation mechanism."},{"cited_title":"Confirmation bias: A ubiquitous phenomenon in many guises","cited_arxiv_id":null,"evidence_quote":"Provides the confirmation-bias literature as evidence that humans preferentially seek and value belief-consistent information."},{"cited_title":"Unskilled and unaware of it: how difficulties in recognizing one’s own incompetence lead to inflated self-assessments","cited_arxiv_id":null,"evidence_quote":"Documents the Dunning-Kruger effect, supporting the overconfidence mechanism where success is attributed to internal superiority."},{"cited_title":"Computational rationality: A converging para- digm for intelligence in brains, minds, and machines","cited_arxiv_id":null,"evidence_quote":"Grounds the claim that rigidity can be computationally rational within a system's own reward structure."},{"cited_title":"Life as we know it","cited_arxiv_id":null,"evidence_quote":"Supports the broader idea of shared organizational principles across adaptive systems, which the paper invokes for cross-domain recurrence."}],"review_version":1}