Parallel Context-of-Experts Decoding for Retrieval Augmented Generation

Giulio Corallo, Paolo Papotti

Parallel Context-of-Experts Decoding for Retrieval Augmented Generation: 20 upvotes on Hugging Face Daily Papers, #12 of 24 papers on 2026-01-14. Day-by-day upvote history.

Retrieval Augmented Generation faces a trade-off: concatenating documents in a long prompt enables multi-document reasoning but creates prefill bottlenecks, while encoding document KV caches separately offers speed but breaks cross-document interaction. We propose Parallel Context-of-Experts Decoding (Pced), a training-free framework that shifts evidence aggregation from the attention mechanism to the decoding. Pced treats retrieved documents as isolated "experts", synchronizing their predictions via a novel retrieval-aware contrastive decoding rule that weighs expert logits against the model prior. This approach recovers cross-document reasoning capabilities without constructing a shared attention across documents.

Paper page on Hugging Face · arXiv

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