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A comprehensive evaluation framework for continual learning in language agents is introduced, emphasizing controlled task streams and memory design analysis to better assess reusable experience and learning stability.
Adaptive Auto-Harness is a framework for sustained self-improvement of agentic systems deployed on open-ended task streams, outperforming baselines via a stateful multi-agent evolver, harness tree, and human-steering hooks.