README.md

metadata

title: VORTEXRAG
emoji: 🌀
colorFrom: purple
colorTo: blue
sdk: gradio
sdk_version: 5.29.0
app_file: app.py
pinned: true
license: mit
short_description: '7-Layer RAG: +13.6 EM, 0.94 Faithfulness'
tags:
  - retrieval-augmented-generation
  - RAG
  - NLP
  - question-answering
  - causal-reasoning
  - hallucination-reduction
  - LLM
  - machine-learning

VORTEXRAG

Vector Orthogonal Resonance-Tuned EXtraction Retrieval-Augmented Generation

A 7-layer RAG framework that simultaneously eliminates Semantic Drift and Context Window Poisoning.

---

The Problem Standard RAG Cannot Solve

Ask "Why did Lehman Brothers collapse?"

Standard RAG retrieves both Dodd-Frank provisions (cosine 0.87, topically related but WRONG) and the CDS mispricing mechanism (cosine 0.91, causally correct). The LLM sees both and hallucinates a policy-response narrative. This is Semantic Drift.

Even with the right chunk retrieved, 7 surrounding irrelevant chunks dilute the LLM attention. This is Context Window Poisoning.

VORTEXRAG solves both with a principled 7-layer pipeline.

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The 7 Layers

Layer	Name	Formula
1	TVE - Tri-Vector Encoding	score = alpha * cos_sem + beta * cos_syn + gamma * cos_cau
2	VRC - Vortex Retrieval Cone	spiral = TVE * exp(-lambdar) * cos(ntheta)
3	SDC - Semantic Drift Corrector	SDS = 1 - tanh(norm(D)/tau) >= 0.72
4	CPG - Context Poison Guard	ESR = sum(S*w)/(P+eps) >= 3.5 (provably optimal)
5	RFG - Rank Fusion Gate	Phi = TVE^alpha * SDS^beta * ESR^gamma
6	CCB - Causal Context Builder	pos = rank(Phi) * causal_depth
7	FV - Faithfulness Verifier	Delta_R = 1 - ROUGE-L * NLI <= 0.15

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Benchmark Results (v3.0)

System	EM	F1	Faithfulness	Latency
VORTEXRAG	74.8	82.6	0.94	185ms
Self-RAG	68.4	77.1	0.81	410ms
CRAG	66.9	75.8	0.79	320ms
Naive RAG	61.2	69.4	0.71	95ms

+13.6 EM over Naive RAG - Semantic Drift -61% - Context Poisoning -71% - 2.2x faster than Self-RAG

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Ablation

Baseline:          61.2 EM / 0.68 Faithfulness
+TVE:              65.3 EM  (+4.1)
+VRC:              67.8 EM  (+2.5)
+SDC:              70.4 EM  (+2.6)
+CPG:              72.1 EM  (+1.7)
+RFG:              73.4 EM  (+1.3)
+CCB:              73.9 EM  (+0.5)
VORTEXRAG (full):  74.8 EM  (+0.9)

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11 Domain Presets

scientific (tau=0.30) - medical (tau=0.35) - legal (tau=0.40) - cybersecurity (tau=0.45) - financial (tau=0.50) - code (tau=0.60) - educational (tau=0.65) - general (tau=0.80) - historical (tau=0.90) - customer (tau=0.95) - creative (tau=1.20)

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Citation

@article{vignesh2026vortexrag,
  title   = {VORTEXRAG: Vector Orthogonal Resonance-Tuned EXtraction RAG},
  author  = {Vignesh, L},
  year    = {2026},
  doi     = {10.5281/zenodo.20579702},
  url     = {https://doi.org/10.5281/zenodo.20579702}
}

Paper: https://doi.org/10.5281/zenodo.20579702 Code: https://github.com/vignesh2027/VORTEXRAG ORCID: https://orcid.org/0009-0004-9777-7592