From Similarity to Structure: Training-free LLM Context Compression with Hybrid Graph Priors
Long-context large language models remain computationally expensive to run and often fail to reliably process very long inputs, which makes context compression an important component of many systems. Existing compression approaches typically rely on trained compressors, dense retrieval-style selection, or heuristic trimming, and they often struggle to jointly preserve task relevance, topic coverage, and cross-sentence coherence under a strict token budget. To address this, we propose a training-free and model-agnostic compression framework that selects a compact set of sentences guided by structural graph priors. Our method constructs a sparse hybrid sentence graph that combines mutual k-...
| Authors | Yitian Zhou, Chaoning Zhang, Jiaquan Zhang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 25 April 2026 |
| Last updated at source | 25 April 2026 |
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