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A metacognitive controller is introduced that guides diffusion generation according to the needs of the current response and the agent's interaction with the environment, reducing overall computation within responses.
Large language model (LLM) agents now autonomously complete long-horizon tasks, such as repository-level coding, through hundreds of think-act steps, yet the experience each run produces is rarely reused on the next. Prior memory systems concentrate on retrieval, finding the most relevant past trajectory. Retrieval can...
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