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Large language models are increasingly deployed in applications that act on users'long-term behavior histories, from personal assistants to recommendation and advertising systems. These applications demand the user dimension of LLM intelligence: inferring a person's preferences, intent, and likely future actions from w...
COMAP is a novel framework that co-evolves textual world models and agent policies through closed-loop interaction and improves the world model's prediction accuracy over time and leads to more effective long-horizon decision-making.
PILOT is presented, a supervisor-worker harness for live self-improvement through two coupled mechanisms: (1) live steering lets a separate supervisor redirect or abort the active worker during execution; and (2) live self-evolution distils procedures and failure modes revealed during execution into reusable skills and...
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