From Similarity to Structure: Training-free LLM Context Compression with Hybrid Graph Priors

ArXi:2604.23277v1 Announce Type: cross 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.