TopoCompress: Long Context Compression via Graph-Wired Semantic Trajectories
Long-context compression is essential for reducing the cost and latency of large language model inference. However, existing methods can fragment important evidence, require additional training or alignment, and often depend on the target model for effective compression. We introduce TopoCompress, a training-free and model-agnostic framework that compresses long contexts by selecting coherent semantic spans. TopoCompress first scores each span using dense and lexical query relevance together with semantic acceleration. It then constructs a hybrid graph that connects spans based on semantic similarity and sequential adjacency, and propagates the query-guided relevance scores over the graph...
| Authors | Daniel Agyei Asante, Yang Li |
|---|---|
| Primary category | cs.CL |
| Published | 31 August 2026 |
| Last updated at source | 1 September 2026 |
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