agent/papers/items/2026-2606-28182-llawco-learning-laws-of-cooperation-for-modeling-embodied-multi-agent-behavior.md

Paper: LLawCo: Learning Laws of Cooperation for Modeling Embodied Multi-Agent Behavior

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type: paper title: "LLawCo: Learning Laws of Cooperation for Modeling Embodied Multi-Agent Behavior" authors: Qinhong Zhou, Chuang Gan, Anoop Cherian year: 2026 venue: arXiv url: https://arxiv.org/abs/2606.28182 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-06-26 updated_at: 2026-06-26 status: queued relevance: high topics:

• agent-evaluation
• embodied-agent
• multi-agent
• planning
• rag
• reasoning methods:

benchmarks:

models:

datasets:

• cs.LG
• cs.AI
• cs.CV
• cs.RO related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 17 collection_queries: multi-agent-llm

One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

Why Collected

• matched queries: multi-agent-llm
• inferred topics: agent-evaluation, embodied-agent, multi-agent, planning, rag, reasoning
• arXiv categories: cs.LG, cs.AI, cs.CV, cs.RO
• collection score: 17

Review Checklist

• Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
• Does it include a benchmark, dataset, code, or reproducible experimental setup?
• Should it be promoted from queued to skimmed or summarized?

Links

• arXiv: https://arxiv.org/abs/2606.28182