{"slug": "mental-models-for-multi-agent-systems", "title": "Mental-Models for Multi-Agent Systems", "summary": "Researchers Mohammad Hanan Gani and colleagues posted a paper to arXiv on 8 October 2026 introducing \"mental-model-enabled agents,\" a framework that learns an amortized recursive Theory-of-Mind representation with first- and second-order mental-state structure jointly with a belief-conditioned reward model. The authors report that explicit mental-state modeling consistently improved interaction quality and Theory-of-Mind performance over base agentic systems on both language-only and multimodal benchmarks. Code for the framework is publicly available on GitHub.", "body_md": "# Computer Science > Multiagent Systems\n\n  [Submitted on 8 Oct 2026]\n\n# Title:Mental-Models for Multi-Agent Systems\n\n[View PDF](http://arxiv.org/pdf/2610.12453v1)\n\n[HTML (experimental)](https://arxiv.org/html/2610.12453v1)\n\nAbstract:Large foundation models have accelerated progress toward general-purpose agents that interact with humans and other agents through language and multimodal signals. However, robust multi-agent decision-making requires reasoning about what other agents know, intend, and are likely to do under partial observability. Current agentic systems often operate through prompt design, memory, or end-to-end behavioral shaping, but typically do not learn an explicit partner-state representation that can be reused as a decision variable across tasks. We introduce \\emph{mental-model-enabled agents}, a framework that equips an agent with a latent mental model of its counterpart, allowing it to infer hidden beliefs, intentions, and likely reactions from the observed history and use these inferences to guide action selection. Our method learns an amortized recursive Theory-of-Mind representation, with first- and second-order mental-state structure, jointly with a belief-conditioned reward model that evaluates candidate actions relative to the inferred partner state. A policy is then learned under this belief-aware signal, yielding an agent that can act independently at inference time while retaining the benefits of explicit partner modeling. We evaluate the same framework on both language-only and multimodal benchmarks. Across these settings, explicit mental-state modeling consistently improves interaction quality and Theory-of-Mind performance over base agentic systems, showing that structured partner modeling is a useful inductive bias for general multi-agent systems. Our code is publicly available at [this https URL](https://github.com/hananshafi/Mental-Models)\n\n## Submission history\n\nFrom: Mohammad Hanan Gani [\n[view email](http://arxiv.org/show-email/06b95fc9/2610.12453)]\n\n**[v1]** Thu, 8 Oct 2026 17:59:01 UTC (4,047 KB)\n\n### Additional Features\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/))\n# 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))\n# 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))\n# 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/mental-models-for-multi-agent-systems", "canonical_source": "http://arxiv.org/abs/2610.12453v1", "published_at": "2026-10-09 14:49:54+00:00", "updated_at": "2026-10-09 15:25:51.601317+00:00", "lang": "en", "topics": ["ai-agents", "ai-research", "artificial-intelligence", "large-language-models", "machine-learning"], "entities": ["arXiv", "Mohammad Hanan Gani", "Mental-Models for Multi-Agent Systems", "GitHub"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/mental-models-for-multi-agent-systems", "markdown": "https://wpnews.pro/news/mental-models-for-multi-agent-systems.md", "text": "https://wpnews.pro/news/mental-models-for-multi-agent-systems.txt", "jsonld": "https://wpnews.pro/news/mental-models-for-multi-agent-systems.jsonld"}}