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LLM agents can dynamically evolve their skill libraries, preventing obsolescence and improving long-horizon task performance by up to 7.8% through a fitness-driven lifecycle.
Historical forecast errors can be more valuable than target values, as RATL shows significant performance gains in multivariate time-series forecasting by retrieving and utilizing these residuals.
MemeMind recovers from failed rollouts by leveraging offline reference answers, leading to a remarkable 22% boost in performance on complex meme interpretation tasks.
Cross-frame feedback boosts Transformer tracking performance by leveraging historical information, outperforming traditional same-frame methods by up to 3.2 AO points.
