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Memory agents can boost decision-making performance by over 8% in long-horizon tasks by actively managing relevant information instead of passively retrieving it.
Stronger coding agents can achieve higher success rates while requiring fewer user interventions, reshaping our understanding of effective coding assistance.
On-policy RL for machine learning engineering agents is now practical, thanks to a synthetic sandbox that slashes execution time by 13x while boosting performance by up to 67%.
