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TRACER is proposed, which formulates compression as a sequential per-tool decision problem, and demonstrates the value of consequence-aware, per-tool context retention for improving the efficiency of long-horizon language agents.
This work presents DREAM (Developing Recommender Engine with Agentic Methods), an autonomous optimization control architecture that adds a perception-aware, orchestrable, and auditable policy layer atop existing pipelines without replacing them, supporting agentic meta-control as a viable paradigm for industrial recommendation.
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