arXiv:2605.29512v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as interactive agents, yet their capacity for social and strategic reasoning over extended interaction remains poorly understood. Existing evaluations rely on static vignettes or single-game benchmarks that cannot capture the sustained, multi-faceted reasoning that real-world multi-agent settings demand. We introduce Mindgames, a multi-game arena and evaluation platform for LLM agents that operationalizes complementary reasoning demands relevant to ``theory of mind'': belief attribution under
The rapid advancement and deployment of LLMs as interactive agents necessitates new evaluation methods to understand their capabilities beyond static benchmarks, as reflected in this new research platform.
A strategic reader should care because improving the social and strategic reasoning of multi-agent LLMs is a critical step towards more robust and autonomous AI systems, impacting enterprise and defense applications.
The introduction of 'Mindgames' provides a more dynamic and comprehensive evaluation framework for LLMs' social and strategic reasoning, moving beyond simple static tests toward sustained, interactive assessment.
- · AI researchers
- · LLM developers
- · Platforms for multi-agent systems
- · Developers relying on static LLM benchmarks
- · Systems with poor social reasoning
Mindgames will lead to a clearer understanding of current LLM limitations in complex multi-agent interactions.
Improved evaluation will drive focus on developing LLMs with more sophisticated theory of mind and strategic capabilities.
More capable multi-agent LLMs could accelerate the development of autonomous enterprise agents and sophisticated defense applications.
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Read at arXiv cs.AI