README.md

---
license: mit
task_categories:
  - question-answering
  - text-retrieval
language:
  - en
tags:
  - retrieval-augmented-generation
  - rag
  - causal-reasoning
  - multi-hop-qa
  - semantic-drift
  - context-window-poisoning
  - benchmark
  - evaluation
pretty_name: VORTEXRAG Benchmark Evaluation Data
size_categories:
  - 10K<n<100K
---

# VORTEXRAG Benchmark Evaluation Dataset

Benchmark evaluation data, results, and per-layer ablation scores for the VORTEXRAG 7-layer causal RAG framework.

## Description

This dataset contains:
1. **Main benchmark results** comparing VORTEXRAG against 7 baseline systems across NQ, HotpotQA, MuSiQue, and 2WikiMultiHopQA
2. **Full ablation study** showing per-layer contribution (configs A through H)
3. **Per-dataset breakdown** showing performance on 6 evaluation datasets
4. **Domain preset evaluation** for all 11 domain configurations
5. **Hyperparameter sensitivity** sweep data for τ and θ_CPG
6. **Latency breakdown** per layer on A100 GPU

## Datasets Used for Evaluation

| Dataset | Type | Questions | Hops | Source |
|---------|------|-----------|------|--------|
| NaturalQuestions | Open-domain QA | 7,842 | 1–2 | Wikipedia Dec 2018 |
| HotpotQA | Multi-hop QA | 7,405 | 2 | Wikipedia 10K docs |
| MuSiQue | Multi-hop QA | 2,417 | 2–4 | Wikipedia filtered |
| 2WikiMultiHopQA | Multi-hop QA | 12,576 | 2 | Wikipedia + Wikidata |
| LegalBench | Legal QA | 1,200 | 1–3 | US federal case law |
| MedQA | Medical QA | 1,273 | 1–2 | PubMed abstracts |

## Main Results

| System | EM | F1 | Faithfulness | SDR | CPR | Latency |
|--------|----|----|--------------|-----|-----|---------|
| Naive RAG | 61.2 | 68.4 | 0.71 | — | — | 120ms |
| BM25+Rerank | 59.8 | 66.1 | 0.69 | — | — | 95ms |
| HyDE | 64.1 | 71.8 | 0.74 | 12% | 8% | 340ms |
| CRAG | 66.9 | 74.3 | 0.78 | 31% | 19% | 290ms |
| Self-RAG | 68.4 | 75.9 | 0.81 | 35% | 24% | 410ms |
| FiD | 63.5 | 70.2 | 0.73 | 8% | 5% | 280ms |
| FLARE | 65.7 | 72.9 | 0.75 | 14% | 10% | 320ms |
| **VORTEXRAG** | **74.8** | **82.6** | **0.94** | **61%** | **71%** | **185ms** |

## Links
- 📄 Paper: https://doi.org/10.5281/zenodo.20285144
- 💻 GitHub: https://github.com/vignesh2027/VORTEXRAG
- 🤗 Space: https://huggingface.co/spaces/vigneshwar234/VORTEXRAG

**Author:** Vignesh L | **License:** MIT | **Version:** v2.0