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VORTEXRAG: 7-Layer RAG โ Causal Drift Filtering + Context Poison Guard [paper + code + demo]
Relevant for anyone using this model as a retrieval backbone.
One limitation of all embedding-based retrieval is that cosine similarity can't separate causal relevance from topical association. VORTEXRAG addresses this by adding causal filtering on top of embedding retrieval.
Architecture: your embedding model handles ANN search โ VORTEXRAG's SDC/CPG layers filter by causal drift โ FV layer verifies faithfulness post-generation.
Results: EM 74.8, Faithfulness 0.94 (+0.23 over standard embedding retrieval baseline). 11 domain presets (medical ฯ=0.35, legal ฯ=0.40, scientific ฯ=0.30) for plug-and-play deployment.
Paper: https://doi.org/10.5281/zenodo.20579702
Code (MIT, 229 tests): https://github.com/vignesh2027/VORTEXRAG
Demo: https://huggingface.co/spaces/vigneshwar234/VORTEXRAG