AsymPuzl：多智能体合作中的非对称谜题
AsymPuzl: An Asymmetric Puzzle for multi-agent cooperation
AI总结：
AsymPuzl通过非对称谜题环境研究多智能体合作中的通信策略与反馈机制。
摘要：
大型语言模型（LLM）代理在多轮、多智能体场景中受到越来越多研究，但大多数现有设置强调开放性角色扮演而非受控评估。我们介绍了AsymPuzl，一个最小但表达力强的双智能体谜题环境，旨在隔离信息不对称下的通信。每个智能体观察到互补但不完整的符号谜题视角，必须通过交换信息来合作解决。使用一系列当前一代和开源的LLM，我们表明（i）强大模型如GPT-5和Claude-4.0在两轮内通过共享完整信息可靠地解决各种谜题；（ii）较弱模型常忽略同伴信息或过度修正假设；（iii）反馈设计非 trivial：简单自我反馈可提高成功率，而详细联合反馈可能损害表现。这些发现表明，即使在简单的合作任务中，LLM通信策略也存在差异，且依赖于反馈信号的粒度。AsymPuzl因此为探索多轮合作的极限提供了测试平台，并为研究协调机制开辟了新途径。
英文摘要：
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) strong models such as GPT-5 and Claude-4.0 reliably converge across puzzle sizes on the solution by sharing complete information in two turns, (ii) weaker models often 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.
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