AGORA: Adapter-Grounded Observation-Action Retention for Inference-Free Prompt Compression in LLM Agents
Quick Answer
AGORA presents a novel approach for LLM agents, revealing that traditional token-level extractive compressors yield only 75% of uncompressed performance across various environments and methods.
Quick Take
A four-way component ablation study identifies structural limitations as the key factor, achieving up to 11.5x adaptive end-to-end compression with a fixed keep ratio.
Key Points
- Traditional token-level compressors underperform for agents, averaging 75% of uncompressed performance.
- A four-way ablation study highlights structural limitations as the main quality lever.
- AGORA achieves 1.0-11.5x adaptive end-to-end compression with a fixed keep ratio.
- The study spans 17 (env, backbone, method) cells across two token-level method families.
- Only one cell achieved 73% performance, indicating widespread inefficiency.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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. We name and characterize this failure mode as action-grammar destruction -- the tokens carrying action semantics (identifiers, brackets, action verbs) are exactly those self-information ranks lowest, so a general-purpose compressor reliably removes them and the environment rejects the residual. The diagnosis points to step-granularity compression. We introduce AGORA, an inference-free step-level compressor combining a structural prompt parser, an always-keep floor for format- and recency-critical content, and a 125M-parameter relevance scorer trained on counterfactual next-action-change labels (~2ms/step, zero per-step LLM toll). Across the compared inference-free and LLM-based methods, AGORA is the only one retaining >= 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.
| Comments: | 10 pages, 2 figures. Code and data: this https URL |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.26596 [cs.AI] |
| (or arXiv:2605.26596v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2605.26596 arXiv-issued DOI via DataCite |
Submission history
From: Haoran Zhang [view email]
[v1]
Tue, 26 May 2026 06:29:44 UTC (4,379 KB)
— Originally published at arxiv.org
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