AGORA: Adapter-Grounded Observation-Action Retention for Inference-Free Prompt Compression in LLM Agents 事件

AGORA: Adapter-Grounded Observation-Action Retention for Inference-Free Prompt Compression in LLM Agents arXiv:2605.26596v1 Announce Type: new Abstract: The token-level extractive compressors widely used for general LM context are structurally inappropriate for LLM agents: across 17 (env, backbone, method) cells spanning two independent token-level method families, every cell collapses to mean reward = 75% uncompressed performance in 8 of 9 cells (with the lone exception at 73%); a four-way com