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SCAD effectively combines outcome-based credit assignment with teacher-guided distillation to improve planning and execution in long-horizon agents.
PowerAtlas is presented, an LLM-agent framework for electricity-computing co-scheduling that integrates historical instances, domain knowledge, and physical constraints to produce joint decisions satisfying both grid operational rules and the service-level agreements of computing tasks.
This work introduces EMemBench, a programmatic benchmark generator for evaluating long-term episodic memory of agents through interactive games, and evaluates memory agents with strong LMs/VLMs as backbones, using in-context prompting as baselines.
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