{"id":"1ded9d5f-70cb-4422-8ab0-05682e834e8d","arxiv_id":"2505.13729","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":1,"one_line_summary":"LLM-generated collaboration strategies for decentralized heterogeneous robot teams reduce multi-object search time by up to 53% and support adaptive re-planning when a robot slows down, in simulation.","lead":"SayCoNav uses large language models to automatically write collaboration plans for a team of robots with different skills, then lets each robot plan its own movements while sharing updates. In simulations, the team finds multiple objects in a house faster than a baseline where each robot searches alone.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Unvalidated custom multi-agent simulator wrapper is the load-bearing assumption; all reported speedups depend on it.","rationale":"The reader's weakest_assumption identifies the same load-bearing concern I would stress: the custom multi-agent simulation mechanism over AI2-THOR/ProcTHOR is unvalidated, and all reported numerical claims depend on it. The paper explicitly admits the mechanism was implemented but gives no details, making the evaluation irreproducible and potentially misleading. This concern is serious enough to justify the CONDITIONAL verdict: the paper should not be fully accepted until the wrapper is validated and ideally released. I do not see evidence that the wrapper is definitely broken, so REJECT would be too strong. Secondary concerns include the absence of error bars, the missing MHRC baseline, and the abstract's claim of 'at most 44.28%' which conflicts with the 53.1% improvement shown in the two-agent heterogeneous row of Table 1; these are real but less fundamental than the simulator mechanism. Because my concern aligns with the reader's and the verdict remains CONDITIONAL, no verdict change is needed.","tokens_in":9398,"tokens_out":4431,"duration_ms":45053,"concrete_test":"Release the multi-agent wrapper code and reproduce the Table 1 three-heterogeneous-agent row (SayNav 537.43s vs SayCoNav 299.45s) with a controlled variant: replace the wrapper's inter-agent observation propagation with ground-truth global state. If the ~44% speedup persists, the unvalidated wrapper is not the source; if it vanishes or inverts, the claimed collaboration benefit is an artifact of the unverified mechanism.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central empirical claim—that SayCoNav's LLM-generated collaboration strategy improves search efficiency by up to 44.28% (and by as much as 53.1% in Table 1)—rests entirely on a custom multi-agent mechanism layered onto the single-agent AI2-THOR/ProcTHOR simulator. Section 4 states only: \"we also implemented a mechanism on top of it to enable multi-agent simulation for our experiments.\" No design details, correctness checks, or validation are provided. For the reported results to be meaningful, this wrapper must correctly model simultaneous perception and action, inter-agent observation of shared-state changes (e.g., one robot opening a fridge that another robot must see inside), collision and occupancy semantics, and communication message passing. If the wrapper inadvertently serializes agents, grants access to global state, or fails to propagate state changes promptly, the measured improvements could be artifacts of the simulation mechanism rather than of the LLM collaboration strategy. The baseline SayNav runs inside the same wrapper, so a broken wrapper could handicap the baseline and inflate the reported gains. No code or data are released, making the claim unverifiable from the manuscript text. This is not an internal inconsistency, but an unvalidated primitive on which every quantitative conclusion depends; the paper's own Limitation section (Section 6) does not address this issue.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper presents SayCoNav, a decentralized multi-robot navigation system in which large language models (LLMs) generate and adapt a collaboration strategy for a team of heterogeneous robots. The architecture has three levels: a top-level global planner that uses an LLM to produce a team-wide collaboration strategy from robot skill and condition information, a middle-level local planner that generates step-by-step per-robot plans using local scene graphs and communication messages, and a bottom-level action planner that executes each step with oracle actions. The method is evaluated on Multi-Object Navigation (MultiON) tasks built with ProcTHOR/AI2-THOR, using 50 episodes per condition and comparing against a SayNav-based baseline. The paper reports speedups in episode time, an adaptive-strategy experiment in which one robot's battery degrades mid-episode, ablation studies on prompt components and LLM choice, and a comparison with a human-refined strategy. The central claim is that LLM-generated collaboration improves search efficiency by up to 44.28% relative to independent single-robot planning.","tokens_in":1860,"tokens_out":2006,"duration_ms":45048,"significance":"If validated, the contribution is potentially useful: it extends LLM-based navigation from single robots to heterogeneous teams with automatically generated, adaptable collaboration strategies, and it does so in a decentralized framework. The paper makes several good methodological choices, including using an oracle low-level planner to isolate the effect of high-level planning, ablating the feedback and summary prompt components, comparing two LLMs, and candidly reporting failure cases in the limitation section. These features make the architecture reproducible in principle. However, the significance is currently limited by the lack of validation of the custom multi-agent simulation mechanism on which every quantitative result depends, by the absence of statistical rigor over the 50-episode evaluation, and by the omission of the closest related baseline, MHRC, from the experiments. The headline quantitative claim also appears inconsistent with the reported table.","major_comments":[{"comment":"The custom multi-agent wrapper is the load-bearing component of the entire evaluation, yet Section 4 gives it only one sentence: 'we also implemented a mechanism on top of it to enable multi-agent simulation for our experiments.' No details are provided about how simultaneous perception and action, inter-robot observation of shared state changes (e.g., one robot opening a fridge that another must inspect), collision and occupancy semantics, or communication message passing are modeled. If the wrapper serializes agents, exposes global state, or fails to propagate state changes promptly, the reported speedups could be artifacts of the simulation mechanism rather than of the LLM collaboration strategy. Because the SayNav baseline runs inside the same wrapper, a flawed wrapper could also handicap the baseline and inflate the measured gains. This issue requires either a detailed description and correctness validation of the wrapper, experiments on known multi-agent scenarios to validate it, or release of code and data so that the simulation primitives can be inspected. The limitation section does not acknowledge this concern.","section":"Section 4, 'implemented a mechanism on top of it to enable multi-agent simulation'"},{"comment":"The abstract claims that SayCoNav 'can improve search efficiency by at most 44.28%,' but Table 1 reports a reduction from 546.32 to 256.17 seconds in the two-heterogeneous-agent case (A1(H), A2(F,M)), which is a 53.1% improvement. This contradicts the abstract's 'at most' wording and the later statement that the three-heterogeneous-agent case gives a 44.28% reduction. The headline claim must be corrected to match the reported data, and the phrase 'at most' should be used only if it is actually the maximum across all reported conditions.","section":"Abstract and Table 1"},{"comment":"All quantitative conclusions rest on averages over 50 episodes with no error bars, confidence intervals, or significance tests. For example, the adaptive strategy experiment (Table 2) reports a success-rate increase from 88% to 92% and an episode-time reduction from 331.37 to 319.31 seconds; these differences are within the range of plausible random variation for 50 episodes. Similarly, the ablation in Table 4 changes success rate from 90% to 84% to 78% without any uncertainty quantification. The text uses phrases such as 'significantly reduces' without statistical support. The authors should provide per-episode distributions, confidence intervals, and appropriate statistical tests, or soften the causal claims accordingly.","section":"Tables 1-4 and Section 4"},{"comment":"The related work identifies MHRC [20] as the closest prior method for decentralized heterogeneous-robot collaboration with LLMs, but the experimental section does not compare against MHRC or any other multi-robot LLM baseline. Without a comparison to the closest existing approach, the paper cannot substantiate its claim of being the first adaptive collaboration framework or its superiority over prior methods. At minimum, the authors should either include an MHRC comparison (or a reasonable re-implementation) or explicitly justify why such a comparison is infeasible and discuss how the results would relate to MHRC's reported performance.","section":"Section 2.2 and Section 4"}],"minor_comments":[{"comment":"The phrase 'in the real word' should be 'in the real world.'","section":"Section 1, first paragraph"},{"comment":"There are typographical errors in the references: [5] spells the author name as 'KAsaei' (likely 'Kasaei') and [14] spells 'Intenational' instead of 'International.'","section":"References [5] and [14]"},{"comment":"The limitation section refers to 'the supplementary material' for details on algorithms and hallucination handling, but no supplementary material is included in the submission. Either provide the supplement or remove the reference.","section":"Section 6"},{"comment":"The prompts in Figure 3 are described as important for reproducing the method, but the figure text appears too small to read and the chain-of-thought details are omitted 'due to page limitation.' Please include the full prompt templates in an appendix or as a textual listing.","section":"Figure 3"},{"comment":"The table formatting has minor issues: the 'A vg' column header should read 'Avg Ep Time (s)', and in Table 4 the 'LLM' column and the row for 'SayCoNav' with no ablation label are easy to misread. Please make the table headers and row labels consistent and self-explanatory.","section":"Table 1 and Table 4"}],"recommendation":"major_revision","confidential_remarks":"The paper's central idea is plausible and the architectural description is clear, but the empirical support is currently too weak for publication. The unvalidated multi-agent simulation wrapper is the most serious issue because it underlies every reported number; without a description, validation, or code release, the results cannot be independently assessed. The abstract/table inconsistency and the absence of statistical testing further weaken the claims. The omission of the MHRC baseline is also noticeable given that the paper identifies it as the closest related work. With additional validation, a corrected headline claim, and more rigorous experimental reporting, the paper could be suitable for a robotics venue after major revision."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"The thing to know about this paper is that the core idea is genuinely new: it uses an LLM to automatically generate and adapt a collaboration strategy for a heterogeneous multi-robot team, going beyond MHRC's manual role assignment and fixed communication topology. That is a real step forward in LLM-based multi-robot planning, and the three-level decentralized architecture is cleanly described. The paper also does a few things well: it runs ablations showing that feedback and summary in the prompt matter, it checks LLM-generated strategies against a human-refined version, and it honestly lists failure cases where the LLM cannot handle close coordination. I see no circularity: the baseline is an external SayNav-based independent planner, and the free parameters are just skill definitions, not tuned to the result.\n\nBut the evaluation is not solid enough to support the headline claims. First, the abstract says the method improves search efficiency by 'at most 44.28%,' but Table 1 shows a 53.1% improvement in the two-heterogeneous-agent case (A1(H), A2(F,M)). That's a direct contradiction. Second, there are no error bars or significance tests across the 50 episodes; given that, the adaptive-strategy improvement in Table 2 (88% to 92% SR, 331s to 319s) is easily within noise. Third, MHRC—the closest prior work—is never compared, and it is the one baseline that would actually isolate the contribution of automatic strategy generation. Fourth, and most important, the stress-test is right: the entire experiment rests on an unvalidated multi-agent wrapper around the single-agent AI2-THOR/ProcTHOR simulator. Section 4 says only that they 'implemented a mechanism on top of it,' with no design details, correctness checks, or validation. If that wrapper serializes actions, shares global state, or fails to propagate state changes correctly, the reported speedups could be artifacts rather than evidence of good coordination. The Limitations section does not touch this.\n\nNone of these problems kills the idea. The central hypothesis—LLM-generated adaptive strategies can help heterogeneous teams—is plausible, and the architecture is a reasonable way to test it. But the evidence as presented is not enough to verify it.\n\nWho gets value from this paper: researchers working on LLM-based multi-robot planning will want to know it exists because it stakes out a new subproblem. A serious editor should send it to review, but I'd expect heavy revision before acceptance: release code and data, add error bars and significance tests, compare against MHRC, document and validate the multi-agent simulation wrapper, and fix the abstract/table inconsistency. As it stands, I would not cite the numbers, but I would cite the idea once it's backed by sturdier experiments.","headline":"Genuinely new idea—LLM-generated adaptive collaboration strategies for heterogeneous robot teams—but the empirical support is thinner than the claims suggest, with an unvalidated multi-agent simulator wrapper and an abstract number that doesn't match Table 1.","tokens_in":10158,"tokens_out":1729,"would_cite":false,"duration_ms":17132,"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":"SayCoNav claims that an LLM can generate and adaptively update a collaboration strategy for a decentralized team of heterogeneous robots, reducing multi-object search time in simulated unknown houses by up to 44.28% compared with…","keywords":["Multi-Robot Navigation","Robot Collaboration","Large Language Models","Decentralized Planning","Heterogeneous Robots","Adaptive Strategy","Multi-Object Navigation","Scene Graphs"],"falsifier":"Re-run the same 50 MultiON episodes in a simulator that natively supports multiple agents with verified simultaneous state updates, or run the episodes with robots acting strictly one at a time; if the 44.28% time reduction and the adaptive-strategy success-rate gain of 88% to 92% do not reappear, the central empirical claim is an artifact of the unvalidated simulation layer.","tokens_in":9202,"feed_emoji":"🤖","tokens_out":6296,"duration_ms":53125,"temperature":0.7,"pith_summary":"SayCoNav sets out to show that a team of heterogeneous robots navigating an unknown environment can be coordinated by an LLM-generated collaboration strategy, produced before the mission from each robot's skill and condition description, and regenerated whenever a robot's condition changes. The paper argues this replaces hand-coded role assignment and centralized control with adaptive, decentralized planning. If the claim holds, multi-robot teams could be reconfigured for new tasks and team compositions without retraining or manual coordination design. The reported evidence is a 50-episode simulated Multi-Object Navigation benchmark in procedurally generated houses, where the largest time reduction from collaboration is 44.28% for a three-robot team with distinct skills.","feed_headline":"Language-model team plans cut robot search time 44%","feed_subtitle":"A decentralized robot team re-assigns roles when a teammate slows, finding objects in unknown homes faster.","key_machinery":"The load-bearing mechanism is the three-level decentralized planning architecture. A top-level global planner has one randomly chosen robot feed the LLM a prompt containing the task, each robot's skills and conditions, and a strategy request; the LLM returns roles and communication rules that are shared with the team. A middle-level local planner then builds each robot's step-by-step plan from a dynamic prompt that includes execution feedback, a task-progress summary, a local 3D scene graph, and messages from other robots. A bottom-level action planner executes each step using oracle navigation and manipulation actions. When a robot's condition changes, the global planner regenerates the strategy with the updated condition, and this regeneration is the adaptive-collaboration mechanism at the center of the paper.","core_discovery":"The central discovery is that a single LLM, prompted with the team task and each robot's skill set, can generate a workable collaboration strategy—roles, communication pattern, and task distribution—that lets a decentralized team search for multiple objects faster than the same robots acting independently. The paper also reports that when the fast robot's battery runs low mid-episode, re-prompting the global planner with the updated condition produces a new strategy that shifts scouting duties to another robot, raising success rate from 88% to 92% and lowering average episode time from 331.37 to 319.31 seconds. The largest reported gain, 44.28% average episode-time reduction, occurs in the three-heterogeneous-agent setting compared with the independent single-robot baseline.","pith_inferences":["A natural extension is to trigger strategy regeneration not only on battery loss but on any detectable capability change, such as sensor failure or a discovered blocked door; the paper's mechanism does not require a fixed set of trigger events.","The results are measured with oracle low-level actions, so the 44.28% reduction is a ceiling for high-level coordination gains; porting to real robots with learned controllers may shrink but not erase the benefit.","The failure cases involving a small object inside a closed container suggest a boundary condition: LLM-generated strategies help when skills are separable across robots, but struggle when a single task requires two robots to act jointly at the same location.","If the unvalidated multi-agent simulation layer does not faithfully model simultaneous perception and state changes, the numerical gains could change under a verified simulator; this is testable by re-running the benchmark in a multi-agent-native environment."],"forward_implications":["Robot teams with complementary sensors and actuators can be assigned roles automatically by an LLM, removing the need for a human to predefine who scouts, who opens, and who inspects.","A mid-mission failure or slowdown of one robot no longer forces mission failure; the team can redistribute responsibilities and continue toward the goal.","Because the strategy is generated from prompt text rather than learned weights, the same system can handle different team sizes and skill combinations without retraining.","The similar performance of human-refined and LLM-generated strategies suggests the LLM's role assignment is close to what a human coordinator would specify.","Removing feedback or summary from the local planner degrades success rate, indicating that the memory components in the dynamic prompt carry much of the coordination benefit."],"supporting_citations":[{"why":"Provides the single-robot LLM navigation system that SayCoNav extends and that serves as the independent-planning baseline.","marker":"[3]"},{"why":"Supplies the procedural house-generation framework used to build the 50-episode benchmark.","marker":"[7]"},{"why":"Defines the Multi-Object Navigation task that the experiments instantiate.","marker":"[8]"},{"why":"Is the closest prior heterogeneous-robot LLM collaboration system, contrasted with SayCoNav's adaptive strategy generation.","marker":"[20]"},{"why":"Introduces chain-of-thought prompting used in the global and local planner prompts.","marker":"[22]"},{"why":"Provides the interactive 3D simulator that the multi-agent mechanism is layered onto.","marker":"[23]"},{"why":"Is the small LLM used for the main experiments and ablations.","marker":"[24]"}],"fun_headline_variants":["LLM-driven robot teams cut search time 44%","Language model coordinates robot teams for 44% faster search","Adaptive LLM teamwork cuts multi-robot search time by 44%","LLM re-plans roles mid-mission, boosting robot search 44%"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The load-bearing premise is that the custom multi-agent simulation layer correctly models what each robot perceives and how robot actions change the shared environment for the others; the paper does not validate this layer.","fun_headline_variants_meta":{"raw":{"variants":["LLM-driven robot teams cut search time 44%","Language model coordinates robot teams for 44% faster search","Adaptive LLM teamwork cuts multi-robot search time by 44%","LLM re-plans roles mid-mission, boosting robot search 44%"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000773,"raw_usage":{"total_tokens":3391,"prompt_tokens":886,"completion_tokens":2505,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":502,"completion_tokens_details":{"reasoning_tokens":2429}},"tokens_in":502,"tokens_out":2505,"duration_ms":17179,"temperature":1.0,"reasoning_tokens":2429,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-15T20:10:32.825311+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Re-run the same 50 MultiON episodes in a simulator that natively supports multiple agents with verified simultaneous state updates, or run the episodes with robots acting strictly one at a time; if the 44.28% time reduction and the adaptive-strategy success-rate gain of 88% to 92% do not reappear, the central empirical claim is an artifact of the unvalidated simulation layer.","supporting_citations":[{"cited_title":"Gireesh, A","cited_arxiv_id":null,"evidence_quote":"Defines the Multi-Object Navigation task that the experiments instantiate."},{"cited_title":"Rajvanshi, K","cited_arxiv_id":null,"evidence_quote":"Provides the single-robot LLM navigation system that SayCoNav extends and that serves as the independent-planning baseline."},{"cited_title":"Deitke, E","cited_arxiv_id":null,"evidence_quote":"Supplies the procedural house-generation framework used to build the 50-episode benchmark."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Introduces chain-of-thought prompting used in the global and local planner prompts."},{"cited_title":"Gpt-4o mini","cited_arxiv_id":null,"evidence_quote":"Is the small LLM used for the main experiments and ablations."}],"review_version":1}