ECHO: Learning Epistemically Adaptive Language Agents with Turn-Level Credit

arXiv:2606.29745 · cs.MA · Submitted 2026-06-29 · Read on arXiv

cs.MA

Submitted: 2026-06-29

Updated: 2026-10-20

Terminology

• Benchmark — 1× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 9 episodes
• Epistemic Credit for History-Conditioned Optimization — 1× in this paper
• GRPO — 1× in this paper · explained in 6 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Information Gain — 1× in this paper · explained in 2 episodes

Related papers

• Highway Congestion Reduction through Reinforcement Learning Based Eulerian Headway Control
• Temporal Fair Division in Multi-Agent Systems: From Precise Alternation Metrics to Scalable Coordination Proxies
• Auditable and Deployable Multi-Agent Motion Planning via Posetal Games from Legal Guidelines
• ORACLE-SWE: Quantifying the Contribution of Critical Information Signals on SWE Agents
• You Only Align Once: Propagating Cooperative Behaviors in Multi-Agent Systems through Seed Agents
• Deny Without Disabling: Authorization-Paired Evaluation and Control for Multi-Agent Systems