✨Parallel Context-of-Experts Decoding for Retrieval Augmented Generation

📝 Summary:
Parallel Context-of-Experts Decoding Pced is a training-free framework for multi-document RAG that avoids prefill bottlenecks. It treats documents as isolated experts, using a retrieval-aware contrastive decoding rule to synchronize predictions and recover cross-document reasoning.

🔹 Publication Date: Published on Jan 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.08670
• PDF: https://arxiv.org/pdf/2601.08670

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✓ https://t.iss.one/DataScienceT

#RAG #LLM #NLP #AI #Decoding

✨Stop the Flip-Flop: Context-Preserving Verification for Fast Revocable Diffusion Decoding

📝 Summary:
COVER stops flip-flop oscillations in parallel diffusion decoding with cache override verification. It performs leave-one-out verification and stable drafting in one pass, preserving context via KV cache override. This greatly reduces revisions for faster, quality-preserving decoding.

🔹 Publication Date: Published on Feb 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06161
• PDF: https://arxiv.org/pdf/2602.06161

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For more data science resources:
✓ https://t.iss.one/DataScienceT

#DiffusionModels #GenerativeAI #DeepLearning #Decoding #ContextPreservation