mlsanityData to 7 Oct 2026

TeamBench: Evaluating Agent Coordination under Enforced Role Separation

arXiv 2605.07073 ↗First posted 8 May 2026cs.AIcited by 8

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Abstract (arXiv)

Agent systems often decompose a task across multiple roles, but these roles are typically specified by prompts rather than enforced by access controls. Without enforcement, a team pass rate can mask whether agents actually coordinated or whether one role effectively did another role's work. We present TeamBench, a benchmark with 851 task templates and 931 seeded instances for evaluating agent coordination under operating system-enforced role separation. TeamBench separates specification access, workspace editing, and final certification across Planner, Executor, and Verifier roles, so that no role can read the full requirements, modify the workspace, and certify the final answer. Prompt-only and sandbox-enforced teams reach statistically indistinguishable pass rates, but prompt-only runs produce 3.6 times more cases where the verifier attempts to edit the executor's code. Verifiers approve 49% of submissions that fail the deterministic grader, and removing the verifier improves mean partial score in the ablation. Team value is also conditional. Teams benefit when single agents struggle, but hurt when single agents already perform well. A 40-session human study under the same role separation shows that our benchmark exposes interaction patterns that pass rate misses. Solo participants work through the task directly, human participants paired with agents often collapse into quick approval, and human teams spend more effort coordinating missing information across roles.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 20 of its 34 references that are in the map. First posted on arXiv 8 May 2026.

Builds on

20 of 34 references.

References · 20

  1. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
  2. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,906
  3. 2024LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Codecited by 1,906
  4. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,761
  5. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,739
  6. 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineeringcited by 1,628
  7. 2018Datasheets for Datasetscited by 1,371
  8. 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Societycited by 1,348
  9. 2023AgentBench: Evaluating LLMs as Agentscited by 1,211
  10. 2023GAIA: a benchmark for General AI Assistantscited by 1,084
  11. 2024OpenHands: An Open Platform for AI Software Developers as Generalist Agentscited by 956
  12. 2023ChatDev: Communicative Agents for Software Developmentcited by 893
8 more
  1. 2025BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agentscited by 616
  2. 2025Why Do Multi-Agent LLM Systems Fail?cited by 559
  3. 2026Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfacescited by 515
  4. 2024MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineeringcited by 429
  5. 2025MultiAgentBench: Evaluating the Collaboration and Competition of LLM agentscited by 169
  6. 2025Towards a Science of Scaling Agent Systemscited by 125
  7. 2025Establishing Best Practices for Building Rigorous Agentic Benchmarkscited by 94
  8. 2024Language Agents as Optimizable Graphscited by 81

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Citing papers per year: 2026: 8.

8 papers.