{"id":"6dc741c3-fe81-478b-b282-75cf171590ac","arxiv_id":"2601.12538","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":4.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"The survey structures agentic reasoning for LLMs into foundational, self-evolving, and collective multi-agent layers while distinguishing in-context orchestration from post-training optimization and reviewing applications across domains.","lead":"This survey organizes agentic reasoning for large language models into three layers: foundational single-agent skills like planning and tool use, self-evolving capabilities through feedback and adaptation, and collective multi-agent collaboration. A smart generalist might read it to see how LLMs can move beyond static reasoning into dynamic, interactive agent behaviors with real-world uses in robotics and science.","discovery_kind":"review","skeptic_critique":{"model":"grok-4.3","headline":"No significant objection identified","rationale":"The reader's weakest-assumption note correctly isolates the taxonomy as the survey's load-bearing organizational choice. Because the work makes no stronger technical claim, that choice does not create a correctness risk requiring verdict change; the existing UNVERDICTED / LOW assessment already reflects the survey nature and limited evaluable content.","tokens_in":1778,"tokens_out":317,"duration_ms":40581,"concrete_test":"Scan the full manuscript for a methods or related-work section that explicitly maps at least 20 representative papers (e.g., ReAct, Reflexion, AutoGen, Voyager) into the three dimensions and flags any that resist clean placement or appear in multiple categories; if more than two major works require substantial qualification, the claimed complementarity is weaker than presented.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The paper is a survey whose central claim is a high-level synthesis: agentic reasoning can be organized along three complementary dimensions (foundational single-agent capabilities, self-evolving adaptation, and collective multi-agent coordination) while distinguishing in-context orchestration from post-training optimization. This produces a roadmap and lists open challenges. No formal derivation, theorem, or quantitative result is asserted whose validity depends on a hidden assumption that could be falsified by a specific counter-example or regime. The taxonomy is presented as a useful organizing lens rather than an exhaustive partition proven to be non-overlapping; any overlap or gap is therefore a matter of editorial judgment, not an internal contradiction that undermines the stated contribution.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"This survey organizes agentic reasoning for LLMs along three complementary dimensions: foundational agentic reasoning establishing core single-agent capabilities (planning, tool use, search) in stable environments; self-evolving agentic reasoning focusing on refinement via feedback, memory, and adaptation; and collective multi-agent reasoning addressing coordination, knowledge sharing, and shared goals. It distinguishes in-context orchestration from post-training optimization, reviews frameworks in applications like science, robotics, healthcare, autonomous research, and mathematics, and outlines open challenges such as personalization, long-horizon interaction, world modeling, scalable multi-agent training, and governance.","tokens_in":1875,"tokens_out":373,"duration_ms":58317,"significance":"If the taxonomy holds, this survey makes a useful contribution by synthesizing a rapidly growing literature into a unified roadmap that connects reasoning processes with agentic action. The explicit separation of in-context scaling from post-training optimization provides a practical lens for comparing approaches, and the enumeration of concrete open challenges (personalization, world modeling, governance) supplies clear signposts for future work. The review of domain-specific frameworks adds concrete grounding to the high-level structure.","major_comments":[],"minor_comments":[{"comment":"Introduction: the positioning of the three dimensions as complementary and comprehensive would be clearer if the manuscript briefly noted selection criteria for the taxonomy and acknowledged possible boundary overlaps (e.g., adaptive multi-agent systems) rather than treating the partition as self-evident.","section":"Introduction"},{"comment":"Applications and benchmarks section: a compact summary table mapping representative frameworks to the three dimensions, listing primary techniques and benchmark results, would make the review more scannable and allow readers to assess coverage at a glance.","section":"Applications and benchmarks"}],"recommendation":"minor_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for their positive assessment of our survey and for the recommendation of minor revision. The referee's summary accurately captures the three-layer taxonomy (foundational, self-evolving, and collective), the distinction between in-context orchestration and post-training optimization, and the enumerated open challenges. We appreciate the recognition that this structure provides a useful roadmap connecting reasoning processes with agentic action.","responses":[],"tokens_in":1285,"tokens_out":94,"duration_ms":26983,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"This survey organizes agentic reasoning for LLMs into three layers: foundational single-agent skills like planning and tool use, self-evolving adaptation through feedback and memory, and collective multi-agent coordination. It also separates in-context orchestration at test time from post-training optimization via RL or fine-tuning. The abstract and structure show an attempt to connect these pieces into a single roadmap that covers applications in robotics, science, healthcare, and math while listing practical open problems such as personalization, long-horizon tasks, world modeling, and governance. That synthesis is the main contribution and can help readers who are new to the area get oriented without reading dozens of separate papers. The review of representative frameworks and benchmarks is straightforward and covers the expected ground. The softer spots are in the taxonomy itself. The three dimensions are presented as complementary, yet real systems frequently combine self-evolution with multi-agent coordination, so the boundaries are not shown to be clean or exhaustive. As a survey the paper contains no new derivations, experiments, or falsifiable claims, which means its value depends on how accurately and completely it cites and groups the prior work. Readers looking for a high-level overview of LLM agents will find it useful. Those seeking original methods or fresh data will not. I would bring this to a reading group to discuss whether the layers hold up in practice. I would not cite it in my own work unless pointing to the listed challenges. It deserves peer review because the field moves quickly and a balanced synthesis can still be worth referee time to tighten the categories and check coverage.","headline":"This survey organizes agentic reasoning into three layers and splits in-context from post-training methods, giving a usable map of the literature but no new techniques or results.","tokens_in":2478,"tokens_out":384,"would_cite":false,"duration_ms":47725,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":{"model":"grok-4.3","evidence":[{"relation":"unclear","rs_module":"IndisputableMonolith.Foundation.LogicAsFunctionalEquation","rs_theorem":null,"paper_passage":"we organize agentic reasoning along three complementary dimensions... foundational agentic reasoning... self-evolving agentic reasoning... collective multi-agent reasoning... distinguish in-context reasoning... from post-training reasoning"},{"relation":"unclear","rs_module":"IndisputableMonolith.Foundation.HierarchyEmergence","rs_theorem":null,"paper_passage":"This survey synthesizes agentic reasoning methods into a unified roadmap bridging thought and action, and outlines open challenges... personalization, long-horizon interaction, world modeling, scalable multi-agent training, and governance"}],"headline":"Survey on agentic reasoning in LLMs organizes AI workflows into foundational/self-evolving/collective layers but shares no machinery with RS forcing chain","alignment":"orthogonal","rationale":"The paper's central contribution is a high-level taxonomy of agentic reasoning (foundational single-agent capabilities, self-evolving adaptation via feedback/memory, collective multi-agent coordination) plus a distinction between in-context orchestration and post-training optimization. It reviews applications and benchmarks but asserts no formal derivation, theorem, or quantitative result that parallels RS primitives. No reference appears to J-cost, golden-ratio identities, 8-tick periodicity, parameter-free derivations of constants, or the distinction-to-spacetime forcing chain. The survey operates entirely within the AI/agent domain; RS has no opinion on LLM workflow taxonomies.","tokens_in":300628,"confidence":"high","tokens_out":345,"duration_ms":32710,"cache_read_input_tokens":64,"cache_creation_input_tokens":0},"lean_confirmation":{"model":"grok-4.3","status":"out_of_scope","citations":[],"rationale":"This is a survey paper whose strongest claim is a high-level synthesis of methods into a roadmap. The load-bearing premise is an assumption about the field's organization, which is not a Lean-provable mathematical claim. No theorem in shape-of-logic establishes or relates to this organizational premise. Status is out_of_scope as the premise is conceptual/empirical.","tokens_in":300390,"confidence":"moderate","tokens_out":225,"duration_ms":39499,"inferential_bridge":"The paper's central synthesis and roadmap rest on this organizational claim about the field, which is conceptual and empirical rather than a specific mathematical or structural identity provable in Lean. Lean theorems in shape-of-logic address formal properties like cost functions, distinctions, and forcing chains, but cannot establish field-wide comprehensiveness or non-overlap of survey categories.","load_bearing_premise":"The assumption that the three complementary dimensions—foundational agentic reasoning, self-evolving agentic reasoning, and collective multi-agent reasoning—provide a comprehensive and non-overlapping organization of the entire field of agentic reasoning for LLMs.","cache_read_input_tokens":64,"cache_creation_input_tokens":0},"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"Agentic reasoning turns large language models into autonomous agents that plan, act, and adapt through interaction.","keywords":["agentic reasoning","large language models","autonomous agents","planning and tool use","self-evolving agents","multi-agent systems","in-context reasoning","reinforcement learning"],"falsifier":"Discovery of a major agentic reasoning method or framework that requires a fourth distinct category or shows substantial overlap across the proposed dimensions would challenge the survey's organizational structure.","tokens_in":2666,"feed_emoji":"🤖","tokens_out":717,"duration_ms":25200,"temperature":0.7,"pith_summary":"This survey organizes methods that let large language models function as agents in open and changing environments instead of closed problems. It divides the approaches into three layers: foundational capabilities for planning and tool use in stable settings, self-evolving processes where agents improve through feedback and memory, and collective systems where multiple agents coordinate and share knowledge. The work also separates in-context orchestration used at test time from optimization through training, and it reviews applications across science, robotics, and healthcare. It ends by identifying open problems such as personalization and long-horizon interaction needed for practical use.","feed_headline":"LLMs gain agency by planning, acting, and learning from interaction","feed_subtitle":"Survey organizes agentic reasoning into foundational, self-evolving, and collective layers to connect thought with real-world action.","key_machinery":"The three complementary dimensions—foundational agentic reasoning for core single-agent capabilities, self-evolving agentic reasoning for refinement through feedback and adaptation, and collective multi-agent reasoning for coordination and shared goals—organize the field and bridge thought with action.","core_discovery":"Agentic reasoning reframes large language models as autonomous agents that plan, act, and learn through continual interaction with their environments. The survey organizes this capability along three complementary dimensions: foundational agentic reasoning that establishes core single-agent skills including planning, tool use, and search in stable environments; self-evolving agentic reasoning that studies refinement through feedback, memory, and adaptation; and collective multi-agent reasoning that extends intelligence to collaborative coordination, knowledge sharing, and shared goals. These layers are further split into in-context reasoning that scales test-time interaction through structed","pith_inferences":["The three-dimension roadmap could guide researchers in systematically identifying gaps for personalization of agent behaviors.","Integrating explicit world modeling may emerge naturally as an extension of the foundational and self-evolving layers.","Governance requirements for real-world agents might be derived from the coordination mechanisms in the collective dimension.","Testable extensions could involve applying the in-context versus post-training split to new benchmarks in mathematics or science."],"forward_implications":["Foundational methods support reliable planning and tool use by single agents in stable environments.","Self-evolving techniques enable agents to improve their own performance using memory and feedback over repeated interactions.","Collective reasoning allows multiple agents to coordinate actions and share knowledge toward common objectives.","Applications in robotics, healthcare, and autonomous research follow directly from applying the organized roadmap.","Open challenges in long-horizon interaction and scalable multi-agent training must be resolved for broader deployment."],"fun_headline_variants":["Reframing LLMs as agents that plan act and learn","Agentic reasoning spans foundational to collective layers","Survey details three layers of agentic reasoning in LLMs","LLMs adapt via planning and multi agent coordination"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"The three dimensions of foundational, self-evolving, and collective agentic reasoning cover the entire field comprehensively without significant overlap or omission.","fun_headline_variants_meta":{"raw":{"variants":["Reframing LLMs as agents that plan act and learn","Agentic reasoning spans foundational to collective layers","Survey details three layers of agentic reasoning in LLMs","LLMs adapt via planning and multi agent coordination"]},"model":"grok-4.3","cost_usd":0.009975,"raw_usage":{"total_tokens":4379,"prompt_tokens":724,"num_sources_used":0,"completion_tokens":54,"cost_in_usd_ticks":99753000,"prompt_tokens_details":{"text_tokens":724,"audio_tokens":0,"image_tokens":0,"cached_tokens":64},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":3601,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":724,"tokens_out":54,"duration_ms":36532,"temperature":1.0,"reasoning_tokens":3601,"cache_read_input_tokens":64,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-05-17T15:08:51.511537+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"Discovery of a major agentic reasoning method or framework that requires a fourth distinct category or shows substantial overlap across the proposed dimensions would challenge the survey's organizational structure.","supporting_citations":[],"review_version":1}