REVA Mines LLM Attention into Reusable Evidence Views, Cutting RAG Compression Overhead up to 15.6x

_reachsumit · x · 2026-09-11

REVA reframes RAG compression as data mining: instead of per-query compression, it aggregates historical query-document-model interactions into reusable evidence views. It mines the generator's past attention traces into a document-keyed, budget-agnostic score store, then renders budget-specific plain-text views that preserve document order.

Across four benchmarks and modern LLMs, REVA improves generation quality by 1.0-5.8 points over existing compressors while reducing compression overhead by 5.3-15.6x, adding under 40 ms of latency.

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REVA Mines LLM Attention into Reusable Evidence Views, Cutting RAG Compression Overhead up to 15.6x

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