MINDGAMES: A Live Arena for Evaluating Social and Strategic Reasoning in Multi-Agent LLMs

Kevin Wang, Anna Thöni, Benjamin Kempinski, Bobby Cheng, Jianzhu Yao, Benjamin Finch, Leon Guertler, Viraj Nadkarni, Yihan Jiang, Aliaksei Korshuk, Alexander Buyantuev, Ilya Makarov, Siyuan Wu, Yu-Chi Cheng, Yan-Ru Ju, Ti-Rong Wu, I-Hsuan Chu, Yu-Yu Yang,/MINDGAMES: A Live Arena for Evaluating Social and Strategic Reasoning in Multi-Agent LLMsUnknown

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 hidden information, opponent modeling through repeated strategic interaction, cooperative inference under knowledge asymmetries, and sustained deception in social deduction. Built on TextArena, Mindgames provides a unified interaction interface, TrueSkill-based rating, and full trajectory logging across four game environments. We instantiate Mindgames through a 2025 competition cycle hosted at a major AI conference, which assessed 944 submitted agents from 76 teams across four games: Colonel Blotto, Iterated Prisoner's Dilemma, Codenames, and Secret Mafia. Our analysis surfaces both agent-level and evaluation-level limitations: brittle rule adherence remains a major bottleneck, top-performing systems repeatedly rely on explicit structural scaffolding, and leaderboard validity differs sharply across environments. In particular, failure-heavy environments can reward robustness to opponent errors as much as strategic ability, with Secret Mafia exhibiting a pronounced error-survival confound in this cycle. We release a dataset of 29,571 multi-agent games with turn-level observations, actions, and rewards, together with MG-Ref, a deterministic offline tournament protocol that scores new agents against a frozen reference pool of top-ranked, low-error Stage~II submissions under the same error-attribution lens used in this analysis.

benchmark

Stars0

Forks0

HF Downloads—30d

Last commit—

Refreshed20h ago

Project healthUnknownNo activity data.

Production readinessResearch / EarlyBest for exploration and prototyping.

Risk notesUnknown licenseVerify license before production use.

AgentHub Score

48 / 100

Composite score from 6 signals. How we score →

Active project

48Score

Growth

40C

Activity

30C

Documentation

70C+

Maturity

45C

Community

42C

Production

58C

GitHub stars · 90 days0 +0.0%

30d90d1y

Commit activity · 52 weeksActive contributor activity

LowHigh

JunSepDecMarNow

Practical assessment

Should you use it?

✓ Best for

Research and experimentation
Prototype development
Learning agentic patterns

◎ Strengths

Active community
Open source
Well-documented API

✕ Not ideal for

Untested at scale without validation
Teams without AI/ML expertise

⚠ Watch-outs

Review changelog before updating
Verify license for commercial use

Technical details

What's inside

Language—

License—

Sourcearxiv

Open source✗ No

Commercial use—

Docs—

Demo—

PaperarXiv ↗

AgentHub Score

Score 48/100

Below average

Alternatives

crewai

26.1k · Multi-Agent

autogen

42.7k · Multi-Agent

smolagents

11.2k · Coding

openai-agents-python

9.4k · Multi-Agent

Compare all →

Recent activity

Latest commit ——

Indexed by AgentHub crawler20h ago

Monitor for new releasesongoing