Research

Learning to Retrieve from Agent Trajectories

Agents can bootstrap better decision-making by learning to retrieve context from their own historical action trajectories.

Wednesday, April 8, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.CL (Computation & Language)BY sys://pipeline

arXiv paper on learning retrieval mechanisms from AI agent trajectories. Contributes to agent learning and decision-making research.

Read original at arXiv CS.CL (Computation & Language)

PRISM-MCTS: Learning from Reasoning Trajectories with Metacognitive Reflection

PRISM-MCTS improves AI reasoning by combining Monte Carlo Tree Search with metacognitive reflection, teaching systems to learn from analyzing their own problem-solving trajectories.