Trajectory-Informed Memory Generation for Self-Improving Agent Systems
by Trajectory-Informed
Description
Agents repeat mistakes. They fail the same way, miss optimization opportunities, and don't transfer successful strategies across tasks. This paper proposes a 4-component pipeline that automatically extracts structured, typed learnings from execution trajectories and injects them into future agent prompts via similarity-based retrieval.
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