{"id":"21f2fbe7-0058-4bad-9bea-9d6145a40ccf","arxiv_id":"2606.19924","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":4.0,"correctness_risk":"high","formal_verification":"none","parameter_count":0,"one_line_summary":"Autotelic AI requires agents to generate and relativize their own self-boundaries in embedded settings, with the paper consolidating this into a framework extended to quantum, philosophical, and LLM contexts.","lead":"The paper explores autotelic AI in which agents generate their own goals rather than receiving them from designers, arguing that the core challenge is how such agents define and relativize their own self or boundary. A smart generalist might read it to consider how concepts of agency and identity could reshape the design of autonomous systems.","discovery_kind":"extension","skeptic_critique":{"model":"grok-4.3","headline":"No significant objection identified","rationale":"The reader's verdict and weakest-assumption identification already capture the conceptual character and absence of formal or quantitative results. No load-bearing technical flaw is detectable in the argument structure itself.","tokens_in":1734,"tokens_out":227,"duration_ms":11473,"concrete_test":"Extract the exact description of the LLM-based agentic instantiation from the manuscript and attempt to reproduce its core loop; if the description is insufficient to implement or yields no observable non-unique partitions under fixed dynamics, the framework's applicability to concrete agents remains unverified.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The paper advances a conceptual synthesis linking autotelic agency, embeddedness, and non-unique self-partitions. Its central claim—that the deepest issue is relativizing the self rather than generating goals—is interpretive and does not rest on a specific dynamical model, theorem, or empirical generalization whose validity depends on a contestable technical assumption. The sketched extensions (quantum cut, contemplative traditions, LLM instantiation) are presented without derivations or implementations that could contain hidden inconsistencies.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper claims that autotelic AI—where agents generate their own goals—leads through intrinsic motivation, homeostasis, and especially embeddedness to the conclusion that individuation of the agent is non-unique, with multiple valid partitions each defining a different self. Consequently, the core challenge shifts from goal generation to generating and relativizing the self-boundary; the agent must both believe in and see through this boundary. The work consolidates these ideas into a framework and extends it via a quantum formulation of the agent-environment cut, comparisons to non-dual contemplative traditions, and a sketched LLM-based instantiation.","tokens_in":1802,"tokens_out":500,"duration_ms":15989,"significance":"If the interpretive synthesis holds, the paper contributes a philosophical reframing that connects autotelic agency concepts with ideas of self-dissolution, potentially broadening discussion in AI about boundaries and embeddedness. No machine-checked proofs, reproducible code, or falsifiable predictions are provided, so the significance rests on conceptual integration rather than technical advance.","major_comments":[{"comment":"Abstract: the assertion that embeddedness is 'a necessary but not sufficient condition for autotelic agency' is presented without a formal definition of sufficiency, a counter-example demonstrating insufficiency, or reference to a specific dynamical model (e.g., active inference or RL), which is load-bearing for the subsequent claim that self-relativization becomes the deepest problem.","section":"Abstract"},{"comment":"Abstract: the non-uniqueness of self-partitions is asserted as following directly from embedded dynamics, yet no explicit construction or theorem shows how the same dynamics admit multiple valid partitions; this circularity in the definition of 'self' undermines evaluation of the central claim that the agent must 'believe in its own boundary in order to act, and see through that boundary in order to understand.'","section":"Abstract"}],"minor_comments":[{"comment":"The three extensions (quantum cut, contemplative traditions, LLM instantiation) are listed in the abstract but receive no technical detail or pseudocode, leaving their connection to the core framework unclear.","section":null}],"recommendation":"major_revision","confidential_remarks":"The manuscript is almost entirely conceptual and interpretive with no empirical results or formal derivations; it may fit better in a philosophy-of-AI or interdisciplinary venue than a core cs.AI journal focused on technical contributions."},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for their constructive comments on the abstract. The manuscript is a conceptual synthesis consolidating ideas from autotelic AI, embedded agency, and related traditions rather than a formal technical derivation. We address the two major points below and will revise the abstract for greater clarity on the status of the claims.","responses":[{"response":"We agree that the abstract states the necessity claim without an accompanying formal definition of sufficiency or an explicit counter-example. The argument is developed conceptually through the sections on intrinsic motivation, homeostasis, and embedded dynamics rather than via a single dynamical model. In revision we will add a brief reference to active-inference treatments of embedded agency (e.g., the work on Markov blankets and self-evidencing) to indicate where sufficiency fails in those frameworks, thereby grounding the claim without converting the paper into a formal model.","revision_made":"partial","referee_comment":"[Abstract] Abstract: the assertion that embeddedness is 'a necessary but not sufficient condition for autotelic agency' is presented without a formal definition of sufficiency, a counter-example demonstrating insufficiency, or reference to a specific dynamical model (e.g., active inference or RL), which is load-bearing for the subsequent claim that self-relativization becomes the deepest problem."},{"response":"The non-uniqueness is presented as a direct consequence of the fact that embedded dynamics do not privilege a unique agent-environment cut; the same trajectory can be partitioned in multiple observer-consistent ways. This is argued in the embeddedness section by reference to the relativity of boundaries in complex systems. We acknowledge that no explicit theorem or construction is supplied. In revision we will insert a short illustrative example (e.g., alternative Markov-blanket partitions of a single sensorimotor loop) to make the multiplicity concrete while preserving the paper's conceptual character.","revision_made":"yes","referee_comment":"[Abstract] Abstract: the non-uniqueness of self-partitions is asserted as following directly from embedded dynamics, yet no explicit construction or theorem shows how the same dynamics admit multiple valid partitions; this circularity in the definition of 'self' undermines evaluation of the central claim that the agent must 'believe in its own boundary in order to act, and see through that boundary in order to understand.'"}],"tokens_in":1400,"tokens_out":488,"duration_ms":16930,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"Dear colleague,\n\nThe main thing to know is that the paper argues the real issue in autotelic AI is not goal generation but how an agent creates and relativizes its own self-boundary, using embeddedness to show that any partition into agent and environment is non-unique.\n\nIt pulls together existing threads on intrinsic motivation, resource-driven priors, causal-interventional learning, and homeostasis into one narrative. The observation that embeddedness is necessary but not sufficient because the same dynamics support multiple valid selves is a clear way to frame the problem. The three extensions—a quantum cut where the boundary is physical, a reading against non-dual traditions, and an LLM instantiation—are presented as directions rather than worked-out results.\n\nThe limitation is that none of this is backed by math, experiments, or independent checks. The claims rest on interpretive steps, so the self-referential definitions create the circularity the reader noted. Without external benchmarks it is hard to tell how far this moves past the literature it cites.\n\nThis is for people who follow the philosophical side of agency and autonomy in AI. It will not interest readers who want algorithms or data. The thinking is coherent on its own terms and engages the cited work directly, so it deserves a serious referee to get feedback from specialists in autotelic AI.\n\nI would send it for peer review.","headline":"This paper synthesizes ideas from autotelic AI and embedded agency to claim that self-relativization is the deeper problem, but stays at the level of conceptual arguments without new derivations or evidence.","tokens_in":2274,"tokens_out":359,"would_cite":false,"duration_ms":22760,"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":"Autotelic AI's core issue is how agents generate and relativize the self to which goals are assigned rather than how they generate the goals.","keywords":["autotelic AI","embedded agency","self-relativization","agent-environment boundary","intrinsic motivation","non-dual traditions","LLM agents"],"falsifier":"A fully specified embedded autotelic system in which only one unique self-partition is consistent with the observed dynamics and successful goal-directed behavior.","tokens_in":2617,"feed_emoji":"","tokens_out":638,"duration_ms":19326,"temperature":0.7,"pith_summary":"The paper traces the development of autotelic AI through intrinsic motivation, resource-driven priors, causal learning, homeostasis, and embeddedness. It finds embeddedness necessary but not sufficient, because the same underlying dynamics support many valid partitions, each defining a different candidate self. This non-uniqueness shifts the central difficulty from goal generation to the construction and relativization of the self-boundary. The agent must sustain belief in its own boundary to act effectively while seeing through that boundary to gain understanding. The work consolidates these elements into one framework and extends it in quantum, philosophical, and practical directions.","feed_headline":"Autotelic AI requires relativizing the self to which goals attach","feed_subtitle":"Embedded dynamics allow many valid self-partitions, shifting focus from goal generation to boundary belief and dissolution.","key_machinery":"The agent-environment cut, whose non-unique partitions in embedded dynamics force the agent to both maintain and relativize its self-boundary.","core_discovery":"Embeddedness individuates the agent at the cost of revealing that the individuation is non-unique, such that the same dynamics admit many valid partitions, each defining a different candidate self. The deepest problem with autotelic AI is therefore not how the agent generates goals, but how it generates and relativizes the self to which the goals are assigned. The agent must believe in its own boundary in order to act, and see through that boundary in order to understand.","pith_inferences":["Design of autotelic systems may need to prioritize mechanisms for dynamic self-modeling before goal discovery.","Varying the partition boundaries in simulation could produce measurable differences in observed agency.","The same logic may apply to multi-agent settings where boundaries between participants remain fluid.","Agents could switch between alternative self-partitions depending on task demands."],"forward_implications":["Autotelic agents must handle self-relativization alongside goal generation.","Multiple valid selves can arise from identical underlying dynamics.","A quantum formulation renders the agent-environment cut a physical distinction.","The framework aligns with non-dual contemplative traditions.","LLM-based systems provide a concrete instantiation of the required dynamics."],"fun_headline_variants":["Relativizing the self is autotelic AI's core challenge","Embeddedness creates non unique agent selves","Autotelic agents dissolve boundaries to understand","Many self partitions arise in embedded autotelic AI","Self belief and dissolution drive autotelic agency"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"Embedded dynamics admit many valid partitions that each define a different candidate self, rendering self-relativization the central challenge.","fun_headline_variants_meta":{"raw":{"variants":["Relativizing the self is autotelic AI's core challenge","Embeddedness creates non unique agent selves","Autotelic agents dissolve boundaries to understand","Many self partitions arise in embedded autotelic AI","Self belief and dissolution drive autotelic agency"]},"model":"grok-4.3","cost_usd":0.008682,"raw_usage":{"total_tokens":3922,"prompt_tokens":683,"num_sources_used":0,"completion_tokens":63,"cost_in_usd_ticks":86824500,"prompt_tokens_details":{"text_tokens":683,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":3176,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":683,"tokens_out":63,"duration_ms":24956,"temperature":1.0,"reasoning_tokens":3176,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-26T17:32:46.370758+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"A fully specified embedded autotelic system in which only one unique self-partition is consistent with the observed dynamics and successful goal-directed behavior.","supporting_citations":[],"review_version":1}