AI RESEARCH
A Self-Evolving Framework for Efficient Terminal Agents via Observational Context Compression
arXiv CS.CL
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ArXi:2604.19572v1 Announce Type: new As model capabilities advance, research has increasingly shifted toward long-horizon, multi-turn terminal-centric agentic tasks, where raw environment feedback is often preserved in the interaction history to future decisions. However, repeatedly retaining such feedback