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ModularRSI is proposed, a benchmark-disjoint, contrastive, and modular framework for generalizable harness evolution that contrasts successful and failed trajectories for the same task and aggregates evidence across tasks to identify recurring behavioral deficiencies.
TACO reduces token overhead by 10% while boosting terminal agent performance by up to 4%, revolutionizing how we approach long-horizon reasoning tasks.
