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| WEBVTT | |
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| Meet SpectRAG. | |
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| Stop retrieving chunks, and start retrieving context. | |
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| Ask a multi-hop question, | |
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| and classic top-k retrieval grabs six near-duplicate chunks from one cluster. | |
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| It finds only one of the three pieces of evidence. | |
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| SpectRAG seeds, expands over the graph, | |
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| diffuses relevance, and selects a diverse, connected set. | |
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| Three out of three. | |
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| Offline, every document is split into chunks. | |
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| Then the chunks are connected by six signals: | |
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| adjacency, sections, semantic similarity, | |
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| shared entities, utility questions, and citations. | |
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| Together, they form one weighted chunk graph. | |
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| Now every chunk has two geometries. | |
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| Its embedding captures what the chunk says. | |
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| Its spectral coordinates, | |
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| from the graph Laplacian, capture where it lives. | |
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| Communities pull together, whatever their wording. | |
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| At query time, relevance diffuses along the graph. | |
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| Bridge chunks that never matched the query still light up. | |
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| That is how SpectRAG finds the missing link. | |
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| Instead of a fixed top-k, | |
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| SpectRAG fills a twelve-thousand-token budget. | |
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| A protected core comes first, then coverage-aware picks with no redundancy, | |
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| and finally a backfill, so no space is wasted. | |
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| The result: | |
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| 91.8 percent gold-evidence recall, 95 percent accuracy on inference questions, | |
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| and an index six times smaller than LightRAG. | |
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| SpectRAG. | |
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| Retrieve a context set, not a pile of similar chunks. | |
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| Read the paper, and try the code. | |