{"id":"cbaef50a-536f-4751-9423-4bdf9db6b507","arxiv_id":"2605.24238","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":4.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"Advocates integrating four enactive concepts—experience, action-perception inseparability, autonomy, and embodiment—into mainstream AI and reinforcement learning.","lead":"The paper argues that AI research should incorporate enactive ideas viewing perception as active, embodied engagement with the world rather than passive internal processing. A smart generalist might read it to consider how this could guide development of more interactive and autonomous AI systems.","discovery_kind":"new_application","skeptic_critique":{"model":"grok-4.3","headline":"No significant objection identified","rationale":"The reader's weakest_assumption correctly flags the lack of translation steps or evidence. Because the paper makes no stronger claim than interpretive resonance plus a suggestion, that gap does not constitute an internal inconsistency or correctness risk. The assessment on abstract alone is also appropriate given the absence of technical content.","tokens_in":1774,"tokens_out":260,"duration_ms":23569,"concrete_test":"Scan the full manuscript (sections after the abstract) for any explicit mapping of the four concepts onto RL mechanisms or any falsifiable prediction about performance gains; if none exists, the UNVERDICTED classification is appropriate.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The paper is a conceptual advocacy piece that identifies four enactive concepts (experience, action-perception inseparability, autonomy, embodiment) and notes loose structural resonances with RL (action, interaction, feedback, agent-centered evaluation) while explicitly denying equivalence. Its central suggestion—that broader incorporation would be valuable—does not rest on a falsifiable technical claim, derivation, or empirical prediction. The absence of implementation details or evidence of preserved meaning is consistent with the paper's stated scope rather than a hidden assumption that must hold for the argument to succeed.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper advocates incorporating enactive approaches to perception and cognition into AI. It contrasts these with classical views, identifies four key concepts (experience, action-perception inseparability, autonomy, and embodiment), notes that mainstream AI (including LLMs and rule-based systems) has neglected them, and argues that RL shows structural resonance with enactive principles via action, agent-environment interaction, feedback-driven adaptation, and agent-centered evaluation—while explicitly denying theoretical equivalence and calling for broader incorporation into AI and RL.","tokens_in":1833,"tokens_out":276,"duration_ms":23143,"significance":"If the resonances identified hold under further development, the analysis could usefully frame directions for embodied and interactive AI research, drawing attention to intrinsic normativity and lived experience as potential gaps in current paradigms.","major_comments":[],"minor_comments":[{"comment":"Abstract: the resonance claim is supported only by a high-level list of four shared emphases; a brief table or paragraph mapping each enactive concept to specific RL mechanisms (e.g., reward as normativity) would strengthen the argument without altering scope.","section":null},{"comment":"The manuscript would benefit from an explicit scope statement early on clarifying that it offers conceptual analysis rather than implementation recipes or empirical predictions.","section":null}],"recommendation":"minor_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for their constructive summary of the manuscript, recognition of its potential significance for embodied and interactive AI research, and recommendation of minor revision. We are pleased that the structural resonances identified with reinforcement learning, along with the explicit caveats against theoretical equivalence, were accurately captured.","responses":[],"tokens_in":1197,"tokens_out":74,"duration_ms":18464,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The main takeaway is that this is a position paper arguing AI should draw more from enactive cognitive science, specifically four ideas: experience, action-perception inseparability, autonomy, and embodiment. It contrasts these with classical internal-processing views and points out that reinforcement learning already shares some structural features like action, interaction, feedback, and agent-centered evaluation, while correctly stating this is not full equivalence.\n\nWhat the paper handles cleanly is the organization of those four concepts and the measured tone on RL. It gives credit where RL aligns without forcing a deeper match, and the writing stays direct about what mainstream AI has tended to ignore. The citations to enactive literature look appropriate for the scope.\n\nThe soft spots are exactly what you would expect from a conceptual piece: no mappings to algorithms, no examples of preserved meaning in an AI system, and no empirical or formal checks on whether adding these elements would change outcomes. The resonance claim is asserted at a high level rather than shown through concrete cases or comparisons. This leaves the suggestion for broader incorporation as a direction rather than a worked-out path.\n\nThe paper is aimed at people already thinking about embodied or interactive agents, or RL researchers open to cognitive-science framing. It will not help with scaling or new theorems. The argument is coherent on its own terms and engages the cited traditions honestly, so it deserves a serious referee at a venue that accepts position papers, even if revisions would likely focus on adding next-step sketches.","headline":"Sutton and Rafiee lay out four enactive concepts and note loose RL overlaps but stay at advocacy level with no implementations or tests.","tokens_in":2333,"tokens_out":368,"would_cite":false,"duration_ms":35606,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"Enactive approaches to perception as active engagement should be incorporated into AI, as reinforcement learning approximates but does not fully match them.","keywords":["enactive AI","reinforcement learning","embodiment","autonomy","action-perception","experience","cognition","artificial intelligence"],"falsifier":"An implementation of autonomy and embodiment mechanisms in an RL agent that produces no change in its capacity for self-directed adaptation compared with a standard RL baseline in the same environment.","tokens_in":2642,"feed_emoji":"🤖","tokens_out":731,"duration_ms":12235,"temperature":0.7,"pith_summary":"The paper establishes that enactive views treat perception as skillful action that shapes an agent's experience, unlike classical internal processing or even reinforcement learning. It identifies four concepts—experience, action-perception inseparability, autonomy, and embodiment—as the basis for this shift, noting that mainstream AI has neglected them while RL shows partial resonance through interaction and feedback but leaves key aspects underdeveloped. A reader would care because the claim points to a route for AI agents that evaluate and adapt from their own embodied position rather than external rules or rewards alone. If the argument holds, AI design would prioritize dynamic agent-environment loops over detached computation. The authors conclude by calling for broader integration of these ideas into both general AI and RL systems.","feed_headline":"Enactive ideas can extend reinforcement learning toward embodied AI","feed_subtitle":"RL captures interaction and feedback but leaves autonomy and lived experience underdeveloped, pointing to a route for agents grounded in the","key_machinery":"The four enactive concepts of experience, action-perception inseparability, autonomy, and embodiment, which serve to contrast classical detached processing with dynamic, interactive, and intrinsically normative cognition.","core_discovery":"The central claim is that reinforcement learning exhibits structural resonance with enactive principles through its emphasis on action, agent-environment interaction, feedback-driven adaptation, and agent-centered evaluation, yet this should not be taken as theoretical equivalence since key elements remain absent or weakly developed; therefore a broader incorporation of enactive ideas into mainstream AI and RL is needed, centered on the four concepts of experience, action-perception inseparability, autonomy, and embodiment.","pith_inferences":["Testing the four concepts in existing RL environments could show whether adding action-perception coupling changes sample efficiency in long-horizon tasks.","The partial resonance noted in RL suggests that enactive framing might help explain why certain agent architectures generalize better across changing environments.","Extending the argument to multi-agent settings could link autonomy to emergent coordination without centralized rewards.","If the translation succeeds, evaluation benchmarks in AI might need to include measures of an agent's self-generated goals rather than only external task success."],"forward_implications":["Mainstream AI systems would shift from modeling cognition as internal detached processing to modeling it as embodied interaction.","RL agents would incorporate intrinsic normativity and lived experience beyond external reward signals.","Perception in AI would be treated as arising from action rather than from passive sensory input.","Evaluation of AI performance would become more agent-centered, based on the agent's own autonomy rather than solely on task metrics.","AI development would emphasize feedback loops grounded in the agent's embedding in its environment."],"fun_headline_variants":["RL shows resonance with enactive action and interaction","Enactive autonomy remains weak in reinforcement learning","Incorporate enactive experience into mainstream AI models","Action perception inseparability for embodied artificial agents"],"cache_read_input_tokens":64,"weakest_assumption_plain":"That the four enactive concepts can be translated into AI systems while preserving their core meaning and yielding practical improvements.","fun_headline_variants_meta":{"raw":{"variants":["RL shows resonance with enactive action and interaction","Enactive autonomy remains weak in reinforcement learning","Incorporate enactive experience into mainstream AI models","Action perception inseparability for embodied artificial agents"]},"model":"grok-4.3","cost_usd":0.005217,"raw_usage":{"total_tokens":2536,"prompt_tokens":683,"num_sources_used":0,"completion_tokens":56,"cost_in_usd_ticks":52174500,"prompt_tokens_details":{"text_tokens":683,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":1797,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":683,"tokens_out":56,"duration_ms":23675,"temperature":1.0,"reasoning_tokens":1797,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-30T15:26:17.605999+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"An implementation of autonomy and embodiment mechanisms in an RL agent that produces no change in its capacity for self-directed adaptation compared with a standard RL baseline in the same environment.","supporting_citations":[],"review_version":1}