AGORA: Adapter-Grounded Observation-Action Retention for Inference-Free Prompt Compression in LLM Agents
Quick Take
AGORA presents a novel method for efficient prompt compression in LLM agents, outperforming traditional token-level compressors.
Key Points
- Traditional token-level compressors underperform in LLM contexts.
- AGORA achieves 1.0-11.5x adaptive compression.
- Structural floor identified as key quality factor.
Article Excerpt
From source RSS / original summaryarXiv: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 component ablation isolates the structural floor as the dominant quality lever and the learned scorer as the source of 1. 0-11.
5x adaptive end-to-end compression from a single fixed keep ratio.
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