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ContextEvo, a framework that learns a context policy from long-horizon trajectories and reconstructs the model-visible context at key decision points, identifies context-related failures, and applies targeted policy updates, is introduced.
EvoRS is introduced, a self-evolving RL framework that evolves the reward system from on-policy experience, representing it as an executable Reward-DAG, and is updated from on-policy rollouts and reward traces to maintain train-time reliability.
HarnessLens is introduced, a budget-aware framework for automated harness evolution that jointly explores the task space and user-configurable components, derives candidate modifications from execution trajectories, and selectively verifies each candidate on behavior-relevant tasks using an attributable-evidence gate.
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