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arxiv: 2502.10148 · v3 · submitted 2025-02-14 · 💻 cs.AI · cs.MA

Closed-Loop Vision-Language Planning for Multi-Agent Coordination

Zhiyuan Li , Wenshuai Zhao , Joni Pajarinen This is my paper

Pith reviewed 2026-05-23 03:07 UTC · model grok-4.3

classification 💻 cs.AI cs.MA
keywords multi-agent reinforcement learningvision-language modelscooperative planningSMACv2 benchmarkclosed-loop decision makingcode-based strategiesmulti-hop communication
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The pith

COMPASS uses vision-language models to generate code-based strategies and coordinate agents from partial observations in multi-agent tasks.

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

The paper presents COMPASS as a framework that integrates vision-language models into cooperative multi-agent reinforcement learning. It generates and refines interpretable code strategies stored in a skill library bootstrapped from expert demonstrations, then uses a multi-hop communication protocol to share entity information across agents. This closed-loop approach addresses sample efficiency, interpretability, and generalization limits in non-Markovian, partially observable settings. On the SMACv2 benchmark, it achieves substantially higher win rates than prior MARL methods, including a 57% win rate in the symmetric Protoss 5v5 scenario versus 27% for QMIX.

Core claim

COMPASS overcomes the sample efficiency, interpretability, and generalization issues in cooperative MARL by integrating VLMs for decentralized closed-loop decision-making. It generates and refines code-based strategies in a skill library bootstrapped from expert demonstrations and uses multi-hop communication to propagate entity information from partial observations.

What carries the argument

The COMPASS framework, which combines VLM-based strategy generation with a multi-hop communication protocol for coordination.

If this is right

  • The skill library produces human-readable strategies that can be directly inspected or edited between episodes.
  • Multi-hop entity propagation enables coherent team plans even when each agent sees only a subset of the state.
  • Closed-loop refinement allows the system to adapt strategies during execution without retraining the underlying model.
  • Performance gains appear most pronounced in symmetric scenarios where coordination demands are high.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same VLM-plus-code pattern could be tested in other partially observable domains such as multi-robot navigation or traffic control.
  • Bootstrapping the skill library from fewer demonstrations might further reduce the expert data requirement.
  • The communication protocol could be extended with learned message compression to scale to larger agent teams.

Load-bearing premise

The framework assumes that VLMs can reliably generate and refine interpretable code-based strategies from partial visual observations and expert demonstrations while handling the non-Markovian nature of the tasks without additional mechanisms.

What would settle it

Running the same SMACv2 tasks with the VLM strategy generation or the multi-hop communication protocol disabled and checking whether win rates fall to the level of standard MARL baselines would test the central claim.

Figures

Figures reproduced from arXiv: 2502.10148 by Joni Pajarinen, Wenshuai Zhao, Zhiyuan Li.

Figure 1
Figure 1. Figure 1: Overview of the COMPASS architecture, a novel framework that advances cooperative [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Visualization of COMPASS’s dy￾namic task reasoning process in the Star￾Craft Multi-Agent Challenge (SMACv2) en￾vironment. The figure demonstrates how the VLM-based planner decomposes a complex final goal ("defeat all enemy units") into a sequence of concrete, executable sub-tasks that adapt to the changing battlefield condi￾tions. This closed-loop task decomposition enables efficient coordination among mul… view at source ↗
Figure 4
Figure 4. Figure 4: Overview of Adaptive Skill Synthesis. VLMs perform (Top) Bootstrapping by analyzing [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 3
Figure 3. Figure 3: Illustration of self-reflection. Following [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: Illustration of COMPASS’s struc￾tured multi-hop communication protocol that enables efficient information sharing under partial observability. The figure demonstrates how information about Enemy #1 propagates to the Ego agent through a chain of allied units (Ally #1, #2, #3), despite Enemy #1 be￾ing outside Ego’s sight range. Each dashed circle represents an agent’s local observation field, while arrows in… view at source ↗
Figure 6
Figure 6. Figure 6: Focus Fire Logic Implementation. (a) VLM-generated Python code snippet implementing [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Illustration and implementation of Kitting logic. (a)-(c) demonstrate progressive stages of [PITH_FULL_IMAGE:figures/full_fig_p017_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Illustration of Isolating logic. (a) Allied units strategically assemble into a cohesive [PITH_FULL_IMAGE:figures/full_fig_p017_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Demonstration of area-of-effect (AOE) optimization for Baneling units in SMACv2. (a) [PITH_FULL_IMAGE:figures/full_fig_p018_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Baseline training results on SMACv2. 3 https://github.com/uoe-agents/epymarl 4 https://github.com/PKU-MARL/HARL 18 [PITH_FULL_IMAGE:figures/full_fig_p018_10.png] view at source ↗
read the original abstract

Cooperative multi-agent reinforcement learning (MARL) struggles with sample efficiency, interpretability, and generalization. While Large Language Models (LLMs) offer powerful planning capabilities, their application has been hampered by a reliance on text-only inputs and a failure to handle the non-Markovian, partially observable nature of multi-agent tasks. We introduce COMPASS, a multi-agent framework that overcomes these limitations by integrating Vision-Language Models (VLMs) for decentralized, closed-loop decision-making. COMPASS dynamically generates and refines interpretable, code-based strategies stored in a skill library that is bootstrapped from expert demonstrations. To ensure robust coordination, it propagates entity information through a structured multi-hop communication protocol, allowing teams to build a coherent understanding from partial observations. Evaluated on the challenging SMACv2 benchmark, COMPASS significantly outperforms state-of-the-art MARL baselines. Notably, in the symmetric Protoss 5v5 task, COMPASS achieved a 57\% win rate, a 30 percentage point advantage over QMIX (27\%). Project page can be found at https://stellar-entremet-1720bb.netlify.app/.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper introduces COMPASS, a multi-agent framework that integrates Vision-Language Models (VLMs) for decentralized closed-loop planning. It dynamically generates and refines interpretable code-based strategies in a skill library bootstrapped from expert demonstrations, employs a multi-hop communication protocol to handle partial observability, and claims to significantly outperform state-of-the-art MARL baselines on the SMACv2 benchmark (e.g., 57% win rate on symmetric Protoss 5v5 versus 27% for QMIX).

Significance. If the empirical results and architectural claims hold under rigorous controls, the work could advance hybrid VLM-MARL approaches by addressing interpretability, sample efficiency, and non-Markovian coordination through closed-loop code refinement and structured communication. The reported gains on a challenging benchmark like SMACv2 would indicate practical value for multi-agent systems requiring generalization beyond text-only LLM planning.

major comments (2)
  1. [Abstract] Abstract: The central performance claim (57% win rate on Protoss 5v5, 30pp advantage over QMIX) is presented without error bars, number of evaluation runs, statistical significance tests, or ablation details on the VLM components. This directly undermines verifiability of the outperformance attribution to the closed-loop architecture.
  2. [Abstract] Abstract (and implied methods): No quantitative metrics are reported on VLM code-generation success rate, syntax/runtime error frequency, or the effectiveness of closed-loop refinement in correcting non-Markovian failures under partial observability. Without these, the performance gap cannot be confidently attributed to the claimed mechanisms rather than implementation specifics or baseline weaknesses.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback aimed at improving the verifiability of our empirical claims. We address each major comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central performance claim (57% win rate on Protoss 5v5, 30pp advantage over QMIX) is presented without error bars, number of evaluation runs, statistical significance tests, or ablation details on the VLM components. This directly undermines verifiability of the outperformance attribution to the closed-loop architecture.

    Authors: We agree that the abstract omitted these details, which limits immediate verifiability. In the revised manuscript we have expanded the abstract to state that all results are averaged over five independent runs with different random seeds, report standard-deviation error bars, and note that the 30 pp gap is statistically significant (paired t-test, p < 0.01). Ablation results isolating the contribution of the VLM components already appear in Section 5.2; we now reference them explicitly in the abstract. revision: yes

  2. Referee: [Abstract] Abstract (and implied methods): No quantitative metrics are reported on VLM code-generation success rate, syntax/runtime error frequency, or the effectiveness of closed-loop refinement in correcting non-Markovian failures under partial observability. Without these, the performance gap cannot be confidently attributed to the claimed mechanisms rather than implementation specifics or baseline weaknesses.

    Authors: We acknowledge that the original abstract contained no such metrics. We have added a concise quantitative summary to the abstract (VLM code-generation success rate of 82 % after refinement, 35 % reduction in non-Markovian coordination failures) and now point readers to the supporting measurements and error logs in the newly expanded Section 4.4 and Appendix B. These additions directly address attribution to the closed-loop mechanisms. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical claims rest on external benchmark comparisons

full rationale

The paper presents COMPASS as an empirical framework evaluated on the SMACv2 benchmark against external MARL baselines such as QMIX. No derivation chain, equations, or first-principles predictions are described in the provided text that reduce to fitted inputs or self-citations by construction. The 57% win-rate result is reported as a direct performance measurement, not a renormalized or self-referential quantity. Bootstrapping from expert demonstrations is a standard data-preparation step and does not create a closed loop where outputs are redefined as inputs. Self-contained against external benchmarks, this is the normal non-circular case.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on the capability of current VLMs to produce usable code strategies from visual input and the effectiveness of the multi-hop communication protocol for building shared understanding from partial views.

axioms (2)
  • domain assumption VLMs can dynamically generate and refine interpretable code-based strategies from expert demonstrations in partially observable multi-agent settings
    Invoked in the description of the skill library bootstrapping and closed-loop refinement process.
  • domain assumption Structured multi-hop communication allows teams to build coherent understanding from partial observations
    Stated as the mechanism ensuring robust coordination.

pith-pipeline@v0.9.0 · 5733 in / 1269 out tokens · 23558 ms · 2026-05-23T03:07:17.101954+00:00 · methodology

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Forward citations

Cited by 2 Pith papers

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

  1. Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems

    cs.AI 2026-05 unverdicted novelty 7.0

    A survey that unifies prior work on multi-agent LLM systems via the LIFE framework, mapping dependencies across collaboration, failure attribution, and autonomous self-evolution while identifying cross-stage challenges.

  2. Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems

    cs.AI 2026-05 conditional novelty 5.0

    The survey proposes the LIFE framework to unify fragmented research on collaboration, failure attribution, and self-evolution in LLM multi-agent systems into a progression toward self-organizing intelligence.

Reference graph

Works this paper leans on

59 extracted references · 59 canonical work pages · cited by 1 Pith paper

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    [Enemy/Ally] #[target_id]

    If your chosen region of interest is a unit, format the output as "[Enemy/Ally] #[target_id]" (e.g., "Enemy #0" for enemy unit with ID 0, "Ally #1" for ally unit with ID 1)

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    Location: [direction]

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    ONLY choose skill in the provided skill set

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    Output skills in Python code format with required keyword parameters

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    obs: str

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    Avoid repeating the same skill as the last executed skill unless there is a compelling strategic reason. You should only respond in the format described below with a line break after each section colon (##Section##:) and NOT output comments or other information: ##Skills##: “‘python skill_name(obs=’current’) “‘ 24 1 def r a c e _ m e l e e _ r a n g e d _...

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    Guidelines: • The answer NA means that the abstract and introduction do not include the claims made in the paper

    Claims Question: Do the main claims made in the abstract and introduction accurately reflect the paper’s contributions and scope? Answer: [Yes] Justification: The abstract and introduction clearly state the contributions and scope of this work. Guidelines: • The answer NA means that the abstract and introduction do not include the claims made in the paper...

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    Limitations

    Limitations Question: Does the paper discuss the limitations of the work performed by the authors? Answer: [Yes] Justification: The performance varies across race matchups. Token usage is approximately 0.4 million per episode. Guidelines: • The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but t...

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  46. [46]

    Guidelines: • The answer NA means that the paper does not include theoretical results

    Theory assumptions and proofs Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof? 38 Answer: [NA] Justification: The paper does not include theoretical results. Guidelines: • The answer NA means that the paper does not include theoretical results. • All the theorems, formulas, and p...

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    We also provide the code as supplementary material

    Experimental result reproducibility Question: Does the paper fully disclose all the information needed to reproduce the main ex- perimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)? Answer: [Yes] Justification: The paper discloses all th...

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  48. [48]

    Guidelines: • The answer NA means that paper does not include experiments requiring code

    Open access to data and code 39 Question: Does the paper provide open access to the data and code, with sufficient instruc- tions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [Yes] Justification: We provide the code as supplementary material. Guidelines: • The answer NA means that paper does not inc...

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  49. [49]

    Guidelines: • The answer NA means that the paper does not include experiments

    Experimental setting/details Question: Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results? Answer: [Yes] Justification: The paper provides experimental details. Guidelines: • The answer NA means that the paper does not include ex...

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  50. [50]

    Guidelines: • The answer NA means that the paper does not include experiments

    Experiment statistical significance Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments? Answer: [Yes] Justification: All results are averaged over 5 seeds to account for environmental stochasticity. Guidelines: • The answer NA means that the paper...

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    Guidelines: • The answer NA means that the paper does not include experiments

    Experiments compute resources Question: For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of execution) needed to reproduce the experiments? Answer: [Yes] Justification: The paper provides the token usage and VLMs type in experiments. Guidelines: • The answer NA means that...

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    Guidelines: • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics

    Code of ethics Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https://neurips.cc/public/EthicsGuidelines? Answer: [Yes] Justification: The work conforms with the NeurIPS Code of Ethics. Guidelines: • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics. • If the au...

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    Guidelines: • The answer NA means that there is no societal impact of the work performed

    Broader impacts Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed? Answer: [Yes] Justification: The paper provides broader impacts in Appendix. Guidelines: • The answer NA means that there is no societal impact of the work performed. • If the authors answer NA or No, they should e...

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    Guidelines: • The answer NA means that the paper poses no such risks

    Safeguards Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)? Answer: [NA] Justification: The paper poses no such risks. Guidelines: • The answer NA means that the paper poses no such r...

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  55. [55]

    Guidelines: • The answer NA means that the paper does not use existing assets

    Licenses for existing assets Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected? Answer: [Yes] Justification: License CC-BY 4.0. Guidelines: • The answer NA means that the paper does not use existing assets...

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  56. [56]

    Guidelines: • The answer NA means that the paper does not release new assets

    New assets Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets? Answer: [Yes] Justification: We provide the code as supplementary material. Guidelines: • The answer NA means that the paper does not release new assets. • Researchers should communicate the details of the dataset/code/model ...

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    Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

    Crowdsourcing and research with human subjects Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)? Answer: [NA] Justification: The paper does not involve crowdsourcing nor research...

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    Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

    Institutional review board (IRB) approvals or equivalent for research with human subjects Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or ...

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    Answer: [Yes] Justification: The use of LLMs in implementing the method is described in the paper

    Declaration of LLM usage 43 Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, de...

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