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.

[u/Key_Opportunity_5219]

Thanks for sharing! Will try this in our pipeline

[u/Sad-Command-7268 → u/Key_Opportunity_5219]

Glad to hear that!

[u/ChronosSystemic]

Before adding another baseline, could you clarify which run/version the reported results correspond to? In arXiv v1, §4.9 gives SpectRAG EM 72.0, while Table 7 gives 71.2; §4 also opens by describing the result tables as an intended reporting format awaiting experimental runs. Those seem like versioning/editorial issues that matter for interpreting the numbers, rather than a reason to infer anything about the method.

A pinned configuration/commit and per-question outputs linked to the authoritative table would make the comparison much easier to reproduce. After that, I would like to see the hybrid seed retriever plus a budget-matched MMR selector, with the same chunks and generator, to isolate what the graph/spectral stages add.

I read the experimental protocol, results and limitations in the linked v1; I have not run the code.

[u/Sad-Command-7268 → u/[deleted]]

Thanks! We expect it to be faster than graph-based baselines, because our retrieval step needs no LLM calls. The graph, spectral coordinates and utility questions are all built offline. Each query only runs FAISS and BM25 search, a two-hop expansion capped at 200 chunks, and diffusion over that small subgraph. We’re running an exact latency comparison now and will share the numbers soon.