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| <title>SpectRAG · Retrieve Context, Not Chunks</title> | |
| <meta name="description" content="SpectRAG: weighted chunk graphs and spectral diffusion for token-budgeted context selection in multi-hop RAG."> | |
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| <header> | |
| <div class="eyebrow">Multi-hop RAG · arXiv 2610.36991</div> | |
| <h1>Stop retrieving chunks.<br><span>Start retrieving context.</span></h1> | |
| <p class="lede">SpectRAG builds a weighted chunk graph from six evidence signals and uses its Laplacian spectrum to select a context set that is <b>relevant, diverse and connected</b>, all under a fixed token budget.</p> | |
| <p class="authors">Zafer Sahinoglu · Abdulazeez Mohammed · Buse Yaren Kazangirler · Cahit Berkay Kazangirler</p> | |
| <div class="btns"> | |
| <a class="btn primary" href="https://arxiv.org/abs/2610.36991" target="_blank" rel="noopener">📄 Paper</a> | |
| <a class="btn" href="https://github.com/SightAndCite/SpectRAG" target="_blank" rel="noopener">💻 Code</a> | |
| <a class="btn" href="https://huggingface.co/papers/2610.36991" target="_blank" rel="noopener">🤗 Paper page</a> | |
| <a class="btn" href="#video">🎬 Watch the explainer</a> | |
| </div> | |
| </header> | |
| <figure> | |
| <img src="assets/01_hero_topk_vs_spectrag.gif" alt="Animation: classic top-k retrieval picks six near-duplicate chunks and finds 1 of 3 gold passages; SpectRAG seeds, expands, diffuses and selects a diverse set that finds 3 of 3."> | |
| <figcaption>Same question, same 6-chunk budget. Top-k finds 1/3 of the gold evidence; SpectRAG finds 3/3. (Illustrative toy corpus.)</figcaption> | |
| </figure> | |
| <p class="quote">Top-k ranks chunks one at a time. Multi-hop questions need a <b>set</b> of chunks that works together as evidence.</p> | |
| <section id="how"> | |
| <h2>How it works</h2> | |
| <p class="sub">An offline pass builds the graph once; a five-stage online pass selects context for each query.</p> | |
| <div class="steps"> | |
| <div class="step"> | |
| <div class="txt"><div class="num">1</div><h3>A chunk graph built from six signals</h3> | |
| <p>Semantic similarity, document adjacency, section hierarchy, shared entities, LLM-generated utility questions and citations are fused into one weighted edge.</p></div> | |
| <img src="assets/02_six_signals_graph_build.gif" alt="Animation: chunks from five documents are connected signal by signal into a weighted chunk graph." loading="lazy"> | |
| </div> | |
| <div class="step"> | |
| <div class="txt"><div class="num">2</div><h3>Two geometries for every chunk</h3> | |
| <p>The dense embedding captures what a chunk says. The 32-d spectral coordinate from the normalized Laplacian captures where it lives in the corpus.</p></div> | |
| <img src="assets/03_spectral_coordinates.gif" alt="Animation: points morph from an overlapping embedding layout into well-separated spectral communities." loading="lazy"> | |
| </div> | |
| <div class="step"> | |
| <div class="txt"><div class="num">3</div><h3>Relevance diffuses along the graph</h3> | |
| <p>Hybrid seeds (dense + BM25 + utility questions) are expanded two hops and re-scored with spectral closeness and Personalized PageRank, so bridge evidence surfaces.</p></div> | |
| <img src="assets/04_spectral_diffusion.gif" alt="Animation: relevance heat spreads from three seed nodes across a graph." loading="lazy"> | |
| </div> | |
| <div class="step"> | |
| <div class="txt"><div class="num">4</div><h3>Token-budgeted selection</h3> | |
| <p>A protected core, a coverage-aware tail with a precision floor, and a backfill fill a ~12k-token window, adapting to 10–15 chunks per query.</p></div> | |
| <img src="assets/05_token_budget_selection.gif" alt="Animation: candidate chunks fill a 12,000-token context window; redundant and low-precision chunks are skipped." loading="lazy"> | |
| </div> | |
| </div> | |
| <figure style="margin-top:40px"> | |
| <img src="assets/online-offline-main-pipeline.png" alt="Full SpectRAG pipeline diagram: offline indexing and online query-time retrieval." loading="lazy"> | |
| <figcaption>The full pipeline: offline corpus indexing and online query-time retrieval.</figcaption> | |
| </figure> | |
| </section> | |
| <section id="video"> | |
| <h2>Explainer video</h2> | |
| <p class="sub">A 98-second narrated walkthrough. Captions are available in the player.</p> | |
| <video controls preload="metadata" poster="assets/00_cover.png"> | |
| <source src="assets/SpectRAG_explainer_voiceover.mp4" type="video/mp4"> | |
| <track kind="captions" src="assets/SpectRAG_explainer_voiceover.vtt" srclang="en" label="English" default> | |
| </video> | |
| </section> | |
| <section id="results"> | |
| <h2>Results</h2> | |
| <p class="sub">MultiHopRAG (2,556 gold-answer questions) and four UltraDomain corpora, against NaiveRAG, Hybrid, HyDE and LightRAG. Every system uses the same generator.</p> | |
| <div class="stats"> | |
| <div class="stat"><b>91.8%</b><span>overall gold-evidence recall (next best 76.1%)</span></div> | |
| <div class="stat"><b>95.1%</b><span>exact match on inference questions</span></div> | |
| <div class="stat"><b>~6×</b><span>smaller index than LightRAG (46 vs 274 MB)</span></div> | |
| <div class="stat"><b>>50%</b><span>diversity win rate vs every baseline</span></div> | |
| </div> | |
| <div class="charts"> | |
| <img src="assets/chart_02_recall_by_depth.png" alt="Line chart: recall at 2, 5, 10 and overall for five systems; SpectRAG reaches 91.8% overall." loading="lazy"> | |
| <img src="assets/chart_01_index_footprint.png" alt="Bar charts: index size 46 MB vs 274 MB and node counts per corpus, SpectRAG vs LightRAG." loading="lazy"> | |
| <img src="assets/chart_03_accuracy_by_question_type.png" alt="Dot plot: exact match by question type; SpectRAG leads on inference at 95.1%." loading="lazy"> | |
| <img src="assets/chart_04_diversity_win_rate.png" alt="Bar chart: SpectRAG diversity win rate above 50% against all four baselines in both configurations." loading="lazy"> | |
| </div> | |
| <div class="note" style="margin-top:20px"> | |
| <b>Honest trade-offs</b> | |
| <ul> | |
| <li>Hybrid retrieval leads at the very top of the ranking (Recall@2, Recall@5, MRR); SpectRAG wins on breadth.</li> | |
| <li>End-to-end accuracy gains are narrow (EM 71.2 vs 70.6 for the next best): better retrieval saturates with short multi-hop answers.</li> | |
| <li>On unanswerable (null) questions SpectRAG is behind the vector baselines, and signal weights can need per-corpus tuning.</li> | |
| </ul> | |
| </div> | |
| </section> | |
| <section id="cite"> | |
| <h2>Citation</h2> | |
| <pre id="bib">@misc{sahinoglu2026spectragweightedchunkgraphs, | |
| title={SpectRAG: Weighted Chunk Graphs for Token-Budgeted Context Selection in Multi-Hop Retrieval-Augmented Generation}, | |
| author={Zafer Sahinoglu and Abdulazeez Mohammed and Buse Yaren Kazangirler and Cahit Berkay Kazangirler}, | |
| year={2026}, | |
| eprint={2610.36991}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.AI}, | |
| url={https://arxiv.org/abs/2610.36991}, | |
| }</pre> | |
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| <footer>SpectRAG is open source under the MIT license · <a href="https://github.com/SightAndCite/SpectRAG">GitHub</a></footer> | |
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