---
license: mit
language:
  - en
pretty_name: SpectRAG Evaluation Results
size_categories:
  - n<1K
task_categories:
  - question-answering
tags:
  - rag
  - retrieval-augmented-generation
  - multi-hop-qa
  - graph-rag
  - evaluation
  - benchmark-results
configs:
  - config_name: retrieval_ranking
    data_files: data/retrieval_ranking.csv
    default: true
  - config_name: answer_accuracy
    data_files: data/answer_accuracy.csv
  - config_name: accuracy_by_question_type
    data_files: data/accuracy_by_question_type.csv
  - config_name: index_cost
    data_files: data/index_cost.csv
  - config_name: pairwise_win_rates
    data_files: data/pairwise_win_rates.csv
---

# SpectRAG Evaluation Results

Aggregate evaluation results from the paper
**[SpectRAG: Weighted Chunk Graphs for Token-Budgeted Context Selection in Multi-Hop Retrieval-Augmented Generation](https://arxiv.org/abs/2610.36991)**.

SpectRAG is compared with **NaiveRAG**, **Hybrid** (BM25 + dense, RRF), **HyDE** and **LightRAG** on **MultiHopRAG** and four **UltraDomain** corpora (Agriculture, CS, Legal, Mix). Every system uses the same generator (`gpt-4o-mini`, temperature 0), so differences come from retrieval.

- 📄 Paper: [arXiv:2610.36991](https://arxiv.org/abs/2610.36991) · 🤗 [Paper page](https://huggingface.co/papers/2610.36991)
- 💻 Code: [github.com/SightAndCite/SpectRAG](https://github.com/SightAndCite/SpectRAG)
- 🎬 Visual walkthrough: [sightandcite/SpectRAG Space](https://huggingface.co/spaces/sightandcite/SpectRAG)

## Subsets

| Config | Source | Rows | What it contains |
|---|---|---|---|
| `retrieval_ranking` | Table 6 | 5 | MultiHopRAG gold-evidence recall (overall, @2, @5, @10) and MRR per system |
| `answer_accuracy` | Table 7 | 5 | Exact match, token F1 and answer containment on 2,556 MultiHopRAG questions |
| `accuracy_by_question_type` | Table 8 | 20 | Exact match per question type (comparison, inference, temporal, null) and system |
| `index_cost` | Table 4 | 10 | Graph nodes, edges, edges/node and graph file size per corpus, SpectRAG vs LightRAG |
| `pairwise_win_rates` | Tables 9 & 10 | 160 | gpt-4o pairwise judge win rates, per edge-weight config, baseline, domain and dimension |

All values are percentages except `nodes`, `edges`, `edges_per_node`, `graph_file_mb` and `node_reduction_vs_lightrag`.

## Usage

```python
from datasets import load_dataset

ranking = load_dataset("sightandcite/SpectRAG-eval-results", "retrieval_ranking", split="train")
wins = load_dataset("sightandcite/SpectRAG-eval-results", "pairwise_win_rates", split="train").to_pandas()

# SpectRAG's average diversity win rate against each baseline
print(wins.query("domain == 'Average' and dimension == 'diversity'")
          [["signal_config", "baseline", "spectrag_win_rate"]])
```

## Column notes

- **`pairwise_win_rates`**: in each row `baseline_win_rate + spectrag_win_rate = 100`. A `spectrag_win_rate` above 50 means the judge preferred SpectRAG. Answer order was swapped to control for position bias. `domain = "Average"` rows are the averages reported in the paper. `edge_weights` lists the six edge-signal weights used for that configuration.
- **`accuracy_by_question_type`**: `null` questions have no answerable gold evidence, so a higher score means correct abstention.
- **`retrieval_ranking`**: the paper notes that NaiveRAG's chunk-level retrieval does not map cleanly onto the graph-node evidence-attribution scheme, so treat its retrieval figures with care. Its generation-level results in `answer_accuracy` are directly comparable.

## Limitations

These are **aggregate** results transcribed from the paper's tables (Tables 9 and 10 were parsed programmatically from the PDF and every row was checked to sum to 100). They don't include per-question predictions or judge rationales. Pairwise judgments come from an LLM judge (gpt-4o) on a sample of questions per UltraDomain domain, so read small differences with caution.

## 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},
}
```