trajectory-learning

This paper shows that language agents fail to reliably connect past actions with their outcomes when using interaction history, and proposes a simple outcome-labeling annotation plus a learned calibrator that selectively records experience, improving task success and reducing action repetition.

The paper introduces Trace, a credit-guided framework that compiles noisy interaction histories into executable walkthroughs to enhance long-horizon AI agent performance, showing significant improvements in experiments.

This article discusses the importance of trajectory learning in AI agent self-evolution and introduces the Apodex 1.1 system and open-source tool FrontierAgent for executing and evaluating long-duration research tasks.