SALT: Salience-Aware Lexical Trie for Long-Context Compression
As large language models (LLMs) process increasingly longer prompts, computation and KV-cache memory costs have emerged as major bottlenecks in inference systems. Existing input-level prompt compression methods address this, but rank each sentence by a scalar relevance score, treating the document as an unstructured pool of words and sentences. Under tight budgets, this causes theme collapse, where the dominant theme(s) of a document consumes the budget, discarding less-frequent yet task-relevant themes. Preserving thematic coverage instead requires allocating the budget across recurring themes rather than scoring sentences in isolation. To this end, we propose SALT, a model-agnostic extr...
| Authors | Oteo Mamo, Hyunjin Yi, Joydhriti Choudhury et al. |
|---|---|
| Primary category | cs.PF |
| Published | 20 July 2026 |
| Last updated at source | 31 August 2026 |
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