Blame for README.md

---
title: SpectRAG
emoji: 🐙
colorFrom: blue
colorTo: indigo
sdk: static
pinned: true
license: mit
short_description: Weighted chunk graphs for multi-hop RAG context selection
tags:
  - rag
  - retrieval-augmented-generation
  - graph
  - spectral-graph-theory
  - multi-hop-qa
---

# SpectRAG: Weighted Chunk Graphs for Token-Budgeted Context Selection in Multi-Hop RAG

An interactive, visual walkthrough of **SpectRAG**. SpectRAG is a retrieval framework that replaces independent top-k ranking with **context-set selection** over a weighted chunk graph and its Laplacian spectrum.

- 📄 Paper: [arXiv:2610.36991](https://arxiv.org/abs/2610.36991)
- 🤗 Paper page: [hf.co/papers/2610.36991](https://huggingface.co/papers/2610.36991)
- 💻 Code (MIT): [github.com/SightAndCite/SpectRAG](https://github.com/SightAndCite/SpectRAG)

## Highlights (MultiHopRAG & UltraDomain)

| | |
|---|---|
| Overall gold-evidence recall | **91.8%** (next best 76.1%) |
| Exact match on inference questions | **95.1%** |
| Index size vs LightRAG | **46 MB vs 274 MB** (~6× smaller) |
| Diversity win rate (LLM judge) | **> 50% vs every baseline**, up to 72.5% |

## Citation

```bibtex
@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},
}
```