AI RESEARCH
Environment Maps: Structured Environmental Representations for Long-Horizon Agents
arXiv CS.AI
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ArXi:2603.23610v2 Announce Type: new Although large language models (LLMs) have advanced rapidly, robust automation of complex software workflows remains an open problem. In long-horizon settings, agents frequently suffer from cascading errors and environmental stochasticity; a single misstep in a dynamic interface can lead to task failure, resulting in hallucinations or trial-and-error. This paper