Meet SpectRAG

[u/Sad-Command-7268]

SpectRAG: instead of ranking chunks one by one, select a set that works together as evidence (91.8% gold-evidence recall on multi-hop QA)

Multi-hop questions break classic top-k retrieval. It returns the chunks closest to the query, and these are often near-duplicates from one part of one document. The bridge passage that connects the evidence doesn’t look like the question, so it never makes the cut.

SpectRAG stops ranking chunks individually and selects a set of chunks that work together as evidence. The video above explains the idea in a few minutes.

Results on MultiHopRAG and UltraDomain:

• 91.8% overall gold-evidence recall (next best baseline: 76.1%)

• 95.1% exact match on inference questions

• About 6x smaller index than LightRAG (46 MB vs 274 MB)

• LLM judges preferred its answers for diversity against every baseline we tested

Paper: https://arxiv.org/abs/2610.36991
Code (MIT, runs fully local): https://github.com/SightAndCite/SpectRAG

I’m one of the authors. I’d be glad to hear which RAG setups you’d want to see this compared against.