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This work proposes an LLM-based framework ProcessLight to decompose signal decisions into verifiable semantic steps and develops Step-wise Traffic Process Policy Optimization (STeP-PO), a novel reinforcement learning framework that optimizes structured reasoning processes through step-level credit assignment.
This work asks what a CAE simulation agent still needs beyond a generic harness, and finds that with information access and repair budget held fixed, a single-agent harness matches or beats multi-agent specialized systems.
PreGress is proposed, the first ranking-native pre-training and prompting framework for supporting a wide range of node ranking tasks, and designs lightweight, task-specific prompt modules that adapt a frozen ranking backbone to downstream tasks without full retraining.
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