AGORA: Adapter-Grounded Observation-Action Retention for Inference-Free Prompt Compression in LLM Agents
The article discusses a new method called AGORA for improving prompt compression in large language model (LLM) agents. AGORA addresses the issue of action-grammar destruction, which occurs when essential tokens are removed during compression. The method demonstrates significant performance retention compared to existing techniques, making it a promising advancement in the field of artificial intelligence.
- ▪AGORA is an inference-free step-level compressor designed for LLM agents.
- ▪The method retains at least 75% of uncompressed performance in 8 out of 9 tested scenarios.
- ▪AGORA combines a structural prompt parser with a relevance scorer trained on counterfactual next-action-change labels.
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Computer Science > Artificial Intelligence arXiv:2605.26596 (cs) [Submitted on 26 May 2026] Title:AGORA: Adapter-Grounded Observation-Action Retention for Inference-Free Prompt Compression in LLM Agents Authors:Haoran Zhang, Zhaohua Sun View a PDF of the paper titled AGORA: Adapter-Grounded Observation-Action Retention for Inference-Free Prompt Compression in LLM Agents, by Haoran Zhang and 1 other authors View PDF HTML (experimental) 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 <= 0.05 despite 1.3-13.3x realized compression.
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