AI RESEARCH
Agents Explore but Agents Ignore: LLMs Lack Environmental Curiosity
arXiv CS.LG
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ArXi:2604.17609v1 Announce Type: cross LLM-based agents are assumed to integrate environmental observations into their reasoning: discovering highly relevant but unexpected information should naturally lead to a model exploiting its own discoveries. We show that this assumption is false for current LLM-based agents, which struggle to reflect or react to unexpected information. Across three benchmarks (Terminal-Bench, SWE-Bench, AppWorld), we inject complete task solutions into the agent environments to deliberately expose a task's solution to a model.