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cs.CL2025
Systematic Failures in Collective Reasoning under Distributed Information in Multi-Agent LLMs
Yuxuan Li, Aoi Naito, Hirokazu Shirado
Multi-agent systems built on large language models (LLMs) are expected to enhance decision-making by pooling distributed information, yet systematically evaluating this capability…
cs.CL2025
Spontaneous Giving and Calculated Greed in Language Models
Yuxuan Li, Hirokazu Shirado
Large language models demonstrate strong problem-solving abilities through reasoning techniques such as chain-of-thought prompting and reflection. However, it remains unclear wheth…
cs.CL2025
Actions Speak Louder than Words: Agent Decisions Reveal Implicit Biases in Language Models
Yuxuan Li, Hirokazu Shirado, Sauvik Das
While advances in fairness and alignment have helped mitigate overt biases exhibited by large language models (LLMs) when explicitly prompted, we hypothesize that these models may…