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REVIEW 4 major objections 5 minor 1 cited by

SayCoNav: Utilizing Large Language Models for Adaptive Collaboration in Decentralized Multi-Robot Navigation

T0 review · 4 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read 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…

desk verdict 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. read the letter →

arxiv 2505.13729 v1 pith:BIABXCMA submitted 2025-05-19 cs.RO cs.AI

Abhinav Rajvanshi , Pritish Sahu , Tixiao Shan , Karan Sikka , Han-Pang Chiu This is my paper · ORCID
classification cs.ROcs.AI
keywords Multi-RobotNavigationRobotCollaborationLargeLanguageModelsDecentralizedPlanningHeterogeneousRobotsAdaptiveStrategyMulti-ObjectSceneGraphs
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

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.

What carries the argument

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.

What would settle it

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.

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Extended reading notes

Core claim

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.

Load-bearing premise

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.

Editorial extensions

If this is right

  • 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.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • 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.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

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.

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 (4)
  1. [Section 4, 'implemented a mechanism on top of it to enable multi-agent simulation'] 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.
  2. [Abstract and Table 1] 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.
  3. [Tables 1-4 and Section 4] 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.
  4. [Section 2.2 and Section 4] 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.
minor comments (5)
  1. [Section 1, first paragraph] The phrase 'in the real word' should be 'in the real world.'
  2. [References [5] and [14]] There are typographical errors in the references: [5] spells the author name as 'KAsaei' (likely 'Kasaei') and [14] spells 'Intenational' instead of 'International.'
  3. [Section 6] 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.
  4. [Figure 3] 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.
  5. [Table 1 and Table 4] 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.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the 44.28% improvement is a direct empirical comparison against a SayNav-based baseline; self-citation is component reuse, not a load-bearing assumption.

full rationale

The paper's central claim is an empirical one: SayCoNav's LLM-generated collaboration strategy reduces multi-object navigation time relative to a SayNav-based baseline, with the reported 44.28% improvement in the three-heterogeneous-agent case. This number is computed directly from Table 1 (537.43 vs. 299.45 seconds), so the prediction is not equivalent to any input by construction. The method contains no fitted parameters, no learned weights, and no hidden quantity that is later renamed as a prediction. The strongest potential circularity concern is self-citation of SayNav [3], which shares authors with the present paper. However, SayNav is used in two legitimate roles: as a component base (graph generator and bottom-level action planner, Section 3.3) and as the independent baseline method (Section 4). Neither role smuggles in the collaboration benefit being claimed: the baseline executes three independent SayNav agents without coordination, and the comparison is a standard external benchmark. The paper's task design deliberately includes objects that require complementary skills (objects inside closed entities and small objects), but this is an experimental design choice that makes collaboration relevant, not a circular derivation of the result. The unvalidated multi-agent simulation mechanism layered on AI2-THOR/ProcTHOR is a legitimate validity risk—all reported speed-ups depend on it—but it is an empirical correctness concern, not a circularity of the kind where an output is equivalent to an input by definition or where a fitted parameter is renamed as a prediction. The Limitations section (Section 6) further concedes that some complex collaboration cases were not handled well by the LLM, which is an honest empirical limitation rather than evidence of circular reasoning. Overall, the derivation chain is self-contained with respect to the claimed improvement: input is environment observations and skill descriptions, and the output is measured navigation time. No step reduces to its own premises.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

No free parameters are fitted to data in the statistical sense; the main hand-chosen quantities are the qualitative skill settings and the simulator mechanism, which are not quantified. The central claims rest on the simulation fidelity and the LLM's reliability, both of which are unverified assumptions rather than independently evidenced facts.

free parameters (1)
  • Skill capability definitions (speed advantage, camera resolution, manipulation success) = not specified
    The simulator defines heterogeneous skills such as Fast Movement, High-Resolution Cameras, and Manipulation, but the actual quantitative differences are not reported. The magnitudes of the speed-ups depend on these hand-chosen settings, so the results are uncalibrated to any physical robot.
assumptions (4)
  • domain assumption The custom multi-agent simulation layer on AI2-THOR correctly models concurrent robots and shared environment state.
    Invoked in Section 4 for all experiments; no validation is provided for this layer, and all reported improvements depend on it.
  • domain assumption Oracle A* navigation and oracle manipulation planners are a valid proxy for real low-level control in evaluating high-level collaboration.
    Stated in Section 3.3; the choice isolates high-level planning but limits transferability to real robots.
  • domain assumption The LLM (gpt-4o-mini) can parse scene-graph text and output consistent, executable plans for multiple robots.
    Assumed throughout Sections 3 and 4; the paper acknowledges hallucination failures in Section 6.
  • domain assumption The 50 ProcTHOR episodes are representative of large-scale unknown environments.
    Used in Section 4 without an argument for representativeness or sample-size justification; the paper does report limitations but not this sampling concern.

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Cite this review

Pith. "Pith review of SayCoNav: Utilizing Large Language Models for Adaptive Collaboration in Decentralized Multi-Robot Navigation." pith.science (2026). https://pith.science/paper/BIABXCMA

@misc{pith2026250513729,
  author       = {Pith},
  title        = {Pith review of: SayCoNav: Utilizing Large Language Models for Adaptive Collaboration in Decentralized Multi-Robot Navigation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BIABXCMA}},
  note         = {Machine review of arXiv:2505.13729}
}
read the original abstract

Adaptive collaboration is critical to a team of autonomous robots to perform complicated navigation tasks in large-scale unknown environments. An effective collaboration strategy should be determined and adapted according to each robot's skills and current status to successfully achieve the shared goal. We present SayCoNav, a new approach that leverages large language models (LLMs) for automatically generating this collaboration strategy among a team of robots. Building on the collaboration strategy, each robot uses the LLM to generate its plans and actions in a decentralized way. By sharing information to each other during navigation, each robot also continuously updates its step-by-step plans accordingly. We evaluate SayCoNav on Multi-Object Navigation (MultiON) tasks, that require the team of the robots to utilize their complementary strengths to efficiently search multiple different objects in unknown environments. By validating SayCoNav with varied team compositions and conditions against baseline methods, our experimental results show that SayCoNav can improve search efficiency by at most 44.28% through effective collaboration among heterogeneous robots. It can also dynamically adapt to the changing conditions during task execution.

Figures

Figures reproduced from arXiv: 2505.13729 by the authors.

Figure 1
Figure 1. A SayCoNav example: Three heterogeneous robots use LLMs to define and to execute the [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. The SayCoNav framework for a single robot. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Prompts for top-level global planner (left) and middle-level local planner (right). The [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Example of failure cases where a small pencil is present inside a box (left) and a small egg [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Is Inter-Seed Cross-Play Enough? Evaluating the Robustness of Zero-Shot Coordination Algorithms to Implementation Details

    cs.AI 2026-08 conditional novelty 5.0 of 10

    For Other-Play in Yokai, agents trained with different implementation details coordinate across implementations about as well as across seeds, supporting inter-seed cross-play as a proxy for cross-implementation evaluation.

Reference graph

Works this paper leans on

25 extracted references · 20 canonical work pages · cited by 1 Pith paper

  1. [20]

    W. Yu, J. Peng, Y . Ying, S. Li, J. Ji, and Y . Zhang. Mhrc: Closed-loop decentralized multi- heterogeneous robot collaboration with large language models. InarXiv:2409.16030, 2024

    arXiv 2024

  2. [1]

    Y . Rizk, M. Awad, and E. W. Tunstel. Cooperative heterogeneous multi-robot systems: A survey.ACM Computing Surveys (CSUR), 52(2):1–31, 2019

    work page 2019

  3. [2]

    Shorinwa, T

    O. Shorinwa, T. Halsted, J. Yu, and M. Schwager. Distributed optimization methods for multi- robot systems: Part 2 - a survey.IEEE Robotics & Automation Magazine, 2024

    work page 2024

  4. [3]

    Rajvanshi, K

    A. Rajvanshi, K. Sikka, X. Lin, B. Lee, H. Chiu, and A. Velasquez. Saynav: Grounding large language models for dynamic planning to navigation in new environments. InInternational Conference on Automated Planning and Scheduling, 2024

    work page 2024

  5. [4]

    Rana1, J

    K. Rana1, J. Haviland, S. Garg, J. Abou-Chakra, I. Reid, and N. Suenderhauf. Sayplan: Grounding large language models using 3d scene graphs for scalable robot task planning. In Conference on Robot Learning (CoRL), 2023

    work page 2023

  6. [5]

    B. Yu, H. KAsaei, and M. Cao. Co-navgpt: Multi-robot cooperative visual semantic navigation using large language models. InarXiv:2310.07937v2, 2023

    arXiv 2023

  7. [6]

    Z. Shen, H. Luo, K. Chen, F. Lv, and T. Li. Enhancing multi-robot semantic navigation through multimodal chain-of-thought score collaboration. InProceedings of the AAAI Conference on Artificial Intelligence, 2025

    work page 2025

  8. [7]

    Deitke, E

    M. Deitke, E. VanderBilt, A. Herrasti, L. Weihs, J. Salvador, K. Ehsani, W. Han, E. Kolve, A. Farhadi, A. Kembhavi, and R. Mottaghi. Procthor: Large-scale embodied ai using procedu- ral generation.Advances in neural information processing systems, 35:5982–5994, 2022

    work page 2022

Show all 25 references
  1. [8]

    Gireesh, A

    N. Gireesh, A. Agrawal, A. Datta, S. Banerjee, M. Sridharan, B. Bhowmick, and M. Krishna. Sequence-agnostic multi-object navigation. InIEEE International Conference on Robotics and Automation (ICRA), 2023

    2023

  2. [9]

    Wijmans, A

    E. Wijmans, A. Kadian, A. Morcos, S. Lee, I. Essa, D. Parikh, M. Savva, and D. Batra. Dd- ppo: Learning near-perfect pointgoal navigators from 2.5 billion frames. InInternational Conference on Learning Representations (ICLR), 2019

    2019

  3. [10]

    B. Yu, H. Kasaei, and M. Cao. L3mvn: Leveraging large language models for visual target navigation. InIEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023

    2023

  4. [11]

    V . N. Devarakonda, R. G. Goswami, A. U. Kaypak, N. Patel, R. Khorrambakht, P. Krishna- murthy, and F. Khorrami. Orionnav: Online planning for robot autonomy with context-aware llm and open-vocabulary semantic scene graphs. InarXiv:2410.06239v2, 2024

    arXiv

  5. [12]

    H. Yin, X. Xu, L. Zhao, Z. Wang, J. Zhou, and J. Lu. Unigoal: Towards universal zero-shot goal-oriented navigation. InarXiv:2503.10630v3, 2025

    2025 arXiv

  6. [13]

    H. Yin, X. Xu1, Z. Wu, J. Zhou, and J. Lu. Sg-nav: Online 3d scene graph prompting for llm-based zero-shot object navigation.Advances in neural information processing systems, 37:5285–5307, 2024

    2024

  7. [14]

    T. Shan, A. Rajvanshi, N. Mithun, and H. Chiu. Graph2nav: 3d object-relation graph gen- eration to robot navigation. InIEEE Intenational Conference on Robotics and Automation (ICRA), 2025

    2025

  8. [15]

    Brown, B

    T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, and P. Dhariwal. Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901, 2020

    1901

  9. [16]

    Ouyang, J

    L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, and C. Zhang. Training language models to follow instructions with human feedback.Advances in neural information processing systems, 35:27730–27744, 2022. 10

    2022

  10. [17]

    C. Yu, X. Yang, J. Gao, H. Yang, Y . Wang, and Y . Wu. Learning efficient multi-agent cooper- ative visual exploration. InEuropean Conference on Computer Vision (ECCV), 2022

    2022

  11. [18]

    C. Yu, X. Yang, J. Gao, J. Chen, Y . Li, J. Liu, and Y . Xiang. Asynchronous multi-agent rein- forcement learning for efficient real-time multi-robot cooperative exploration. InInternational Conference on Autonomous Agents and Multiagent Systems, 2023

    2023

  12. [19]

    Zobel, R

    J. Zobel, R. Kundel, and R. Steinmetz. Camon: Aerial-ground cooperation system for disaster network detection. InISCRAM, 2022

    2022

  13. [21]

    D. S. Chaplot, D. Gandhi, A. Gupta, and R. Salakhutdinov. Object goal navigation using goaloriented semantic exploration.Advances in neural information processing systems, 33: 4247–4258, 2020

    2020

  14. [22]

    J. Wei, X. Wang, D. Schuurmans, M. Bosma, B. Ichter, F. Xia, E. Chi, Q. Le, and D. Zhou. Chain-of-thought prompting elicits reasoning in large language models.Advances in neural information processing systems, 35:24824–24837, 2022

    2022

  15. [23]

    Kolve, R

    E. Kolve, R. Mottaghi, W. Han, E. VanderBilt, L. Weihs, A. Herrasti, and M. Deitke. Ai2-thor: An interactive 3d environment for visual ai. InarXiv:1712.05474, 2017

    2017 arXiv

  16. [24]

    Gpt-4o mini

    OpenAI. Gpt-4o mini. Inhttps://platform.openai.com/docs/models/gpt-4o-mini, 2024

    2024

  17. [25]

    OpenAI. o4-mini. Inhttps://platform.openai.com/docs/models/o4-mini, 2025. 11

    2025

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Reviewed August 15, 2026 · model on record in the stance chip above.