AI Research
The Temporal Tug-of-War: Visualizing and Detecting RAG Conflicts in Diffusion Models via Trajectory Variance
Uncovering the hidden structural friction between retrieved context and parametric knowledge in diffusion AI.
Retrieval-Augmented Generation (RAG) introduces a specific failure mode in discrete diffusion language models: when retrieved context contradicts parametric knowledge, the iterative denoising process becomes a visible battleground between competing knowledge sources. We identify ...
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ArXiv CS.CL
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ArXiv CS.CL
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This research reveals how retrieval-augmented generation creates measurable instability during the denoising process when external data clashes with internal parameters. By tracking trajectory variance, developers can finally diagnose and mitigate factual hallucinations in diffusion-based language models.
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