ECHO: Prune To Act, Trace To Learn With Selective Turn Memory In Agentic RL
arXiv:2606.31650v2 Announce Type: replace Abstract: Long-horizon language agents must repeatedly interact with tools, accumulate evidence, and make decisions under bounded context windows. Context-management methods make such rollouts feasible by simplifying past interactions through deletion, folding, or memory editing. However, when useful history is collapsed into compressed states, the reconstructed context may no longer reveal which earlier observations support a successful final answer. Th



