{"slug": "the-interaction-tax-when-communication-erases-diversity-in-multi-agent-teams", "title": "The Interaction Tax: When Communication Erases Diversity in Multi-Agent Teams", "summary": "A new study from researchers including Summer Eunhyung Ann finds that when large language model agents read each other's complete outputs, their proposals converge within one round, erasing the diversity that motivates using multiple models—a phenomenon they call the 'interaction tax.' Testing 11 verifier-scored optimization tasks under matched budgets, the study shows that full-solution interaction is a weak default, while independent proposal generation avoids this collapse, suggesting multi-agent performance depends less on the number of agents than on the information they exchange.", "body_md": "# Computer Science > Multiagent Systems\n\n[Submitted on 24 Aug 2026]\n\n# Title:The Interaction Tax: When Communication Erases Diversity in Multi-Agent Teams\n\n[View PDF](/pdf/2608.23541)\n\n[HTML (experimental)](https://arxiv.org/html/2608.23541v1)\n\nAbstract:Does multi-agent LLM interaction help or hurt? Some work reports gains from debate (Du et al., 2024), critique loops (Chen et al., 2025), and mixture-of-agents synthesis (Wang et al., 2025), while other work finds that interaction adds cost without improving quality under equal budgets (Tran & Kiela, 2026; Xu et al., 2026; Jarrett et al., 2025), or that independent sampling already captures multi-agent gains (Li et al., 2024). We argue this contradiction partly reflects a missing distinction, because not all multi-agent communication is equal. Different model families find structurally different solutions, but when agents read each other's complete outputs, their proposals converge within one round, erasing the diversity that motivates using multiple models. We call this the interaction tax. We test 11 verifier-scored optimization tasks under matched budgets and find that full-solution interaction is a weak default. Independent proposal generation avoids this collapse. Full-solution interaction mainly makes agents stay close to the first solution they see instead of trying different approaches, and critique helps only if the violated rule is easy for the LLM to find and fix. These results suggest that multi-agent performance depends less on the number of agents than on the information they exchange, and interaction helps only when agents share the right information at the right time.\n\n## Submission history\n\nFrom: Summer Eunhyung Ann [[view email](/show-email/8651cf38/2608.23541)]\n\n**[v1]** Mon, 24 Aug 2026 17:45:15 UTC (684 KB)\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer\n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers\n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps\n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations\n\n*(*[What are Smart Citations?](https://www.scite.ai/))# Code, Data and Media Associated with this Article\n\nalphaXiv\n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers\n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub\n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub\n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face\n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast\n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower\n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender\n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [ Learn more about arXivLabs](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/the-interaction-tax-when-communication-erases-diversity-in-multi-agent-teams", "canonical_source": "https://arxiv.org/abs/2608.23541", "published_at": "2026-08-25 10:07:06+00:00", "updated_at": "2026-08-25 10:45:07.578164+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-agents", "ai-research"], "entities": ["Summer Eunhyung Ann", "arXiv"], "alternates": {"html": "https://wpnews.pro/news/the-interaction-tax-when-communication-erases-diversity-in-multi-agent-teams", "markdown": "https://wpnews.pro/news/the-interaction-tax-when-communication-erases-diversity-in-multi-agent-teams.md", "text": "https://wpnews.pro/news/the-interaction-tax-when-communication-erases-diversity-in-multi-agent-teams.txt", "jsonld": "https://wpnews.pro/news/the-interaction-tax-when-communication-erases-diversity-in-multi-agent-teams.jsonld"}}