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The article describes a novel approach to attachment extraction in AI tools where the tool builds a cognitive map of the user's thinking patterns and past interactions to automatically extract relevant information, overriding default generic extraction when explicit instructions are given.
The paper proposes the Map-then-Act Paradigm (MAP), a plug-and-play framework that shifts environmental understanding before execution in interactive LLM agents, achieving consistent gains across benchmarks and enabling frontier models to surpass near-zero baseline performance in 22 of 25 game environments.