mlsanityData to 14 Oct 2026

Testing Interchangeability in LLM Agent Teams

arXiv 2609.05279 ↗First posted 4 Sep 2026cs.AI cs.MAcited by 1

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

Production multi-agent systems replace agents constantly, on the assumption that an agent filling a role is interchangeable with any other agent that can do the job. We test that assumption. Eight teams per setting are formed independently from one base model on the same tasks, each agent keeping a private notebook across ten formation episodes; we then trade role-matched agents between teams and measure what changes on held-out tasks. Against a placebo that reproduces the disruption of a roster change without changing who occupies the seat, a swap costs little in task score but raises the communication a team spends per unit of progress by 16 to 63 percent, and in Hanabi a swapped agent is more expensive than an inexperienced one, consistent with interference from conventions learned with its former partner. In Collab-Overcooked, when the agent that sets the agenda is replaced, most of the extra communication comes from the agent that stayed. Three ablations, over base models, decoding temperature and formation length, move the swap penalty alongside one other quantity: how far independently formed teams drift apart. Greedy decoding lowers both; doubling a team's history raises both. In these settings, agents are more fungible in task outcome than in coordination efficiency, with larger swap effects after longer formation histories.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 24 of its 37 references that are in the map. First posted on arXiv 4 Sep 2026.

Builds on

24 of 37 references.

Closest ancestors · 5

Same direction, newest first

  1. 2026Toward an Organizational Science of Multi-Agent LLM Systems: Decoupling Who, How, and Which Algorithm28 Jul
  2. 2025MultiAgentBench: Evaluating the Collaboration and Competition of LLM agents3 Mar
  3. 2025Collab-Overcooked: Benchmarking and Evaluating Large Language Models as Collaborative Agents27 Feb
  4. 2023LLM-Coordination: Evaluating and Analyzing Multi-agent Coordination Abilities in Large Language Models5 Oct
  5. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework1 Aug

Other references · 19

  1. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
  2. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,988
  3. 2025A-MEM: Agentic Memory for LLM Agentscited by 1,063
  4. 2023ChatDev: Communicative Agents for Software Developmentcited by 919
  5. 2025Why Do Multi-Agent LLM Systems Fail?cited by 604
  6. 2019On the Utility of Learning about Humans for Human-AI Coordinationcited by 349
  7. 2020"Other-Play" for Zero-Shot Coordinationcited by 198
  8. 2019The Hanabi Challenge: A New Frontier for AI Researchcited by 197
  9. 2021Collaborating with Humans without Human Datacited by 142
  10. 2024Emergent social conventions and collective bias in LLM populationscited by 123
  11. 2021Off-Belief Learningcited by 75
  12. 2022A Survey of Ad Hoc Teamwork Researchcited by 43
7 more
  1. 2021A New Formalism, Method and Open Issues for Zero-Shot Coordinationcited by 33
  2. 2021On the Critical Role of Conventions in Adaptive Human-AI Collaborationcited by 27
  3. 2025Emergent Coordination in Multi-Agent Language Modelscited by 23
  4. 2026Thought Virus: Viral Misalignment via Subliminal Prompting in Multi-Agent Systemscited by 9
  5. 2026Attractor States Emerge in Multi-Turn LLM Conversationscited by 6
  6. 2026Sparks of Cooperative Reasoning: LLMs as Strategic Hanabi Agentscited by 3
  7. 2026Bayesian Partner Modelling enables Adaptive Replanning for LLM Coordinationcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

Similar, not cited

Close in wording, no citation either way

  1. 2026When Agents Disagree: The Selection Bottleneck in Multi-Agent LLM Pipelines
  2. 2026Multi-agent Scaling Across Disjunctive and Compensatory Tasks
  3. 2026At Equal Inference Cost, Multi-Agent Structure Does Not Beat a Single Frozen Agent
  4. 2026When Agents Coordinate: Measuring Coordination in Multi-Agent AI Coding
  5. 2026Multi-Agent Teams Hold Experts Back
  6. 2026Specialize Roles, Mix Deployments: Pushing the Cost-Accuracy Frontier of LLM Agent Teams
  7. 2026The Interaction Tax: When Communication Erases Diversity in Multi-Agent Teams
  8. 2026Improving the Efficiency of Language Agent Teams with Adaptive Task Graphs