Title: AsymPuzl: An asymmetric puzzle for multi-agent cooperation
Primary area: foundation or frontier models, including LLMs
Keywords: LLM, Mutli-Agent Cooperation

Abstract: Large Language Model (LLM) agents are increasingly studied in multi-turn, multi-agent scenarios, yet most existing setups emphasize open-ended role-play rather than controlled evaluation.
We introduce AsymPuzl, a minimal but expressive two-agent puzzle environment designed to isolate communication under information asymmetry.
Each agent observes complementary but incomplete views of a symbolic puzzle and must exchange messages to solve it cooperatively.
Using a diverse set of current-generation and open-source LLMs, we show that (i) models such as GPT-5 and Claude-4.0 reliably solve puzzles of different sizes by sharing complete information in few turns, (ii) other models tend ignore partner messages or over-correct their hypotheses, and (iii) feedback design is non-trivial: simple self-feedback improves success rates, while detailed joint feedback can hurt performance.
These findings show that even in simple cooperative tasks, LLM communication strategies diverge and depend on the granularity of feedback signals.
AsymPuzl thus provides a testbed for probing the limits of multi-turn cooperation and opens avenues for studying coordination mechanisms.