Parallel Context-of-Experts Decoding for Retrieval Augmented Generation
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.
69679f39c5e371f6b235d2ca
Giulio Corallo
claimed_verified
2026-01-14T14:21:42.003Z
62c88e75a5ac2974c0a5c8ea
https://cdn-avatars.huggingface.co/v1/production/uploads/1666106981514-62c88e75a5ac2974c0a5c8ea.jpeg
Giulio Corallo
giulio98
user
giulio98
69679f39c5e371f6b235d2cb
Paolo Papotti
Parallel Context-of-Experts Decoding for Retrieval Augmented Generation
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.
Parallel Context-of-Experts Decoding enables multi-document reasoning in retrieval-augmented generation by treating retrieved documents as isolated experts and synchronizing predictions through a retrieval-aware contrastive decoding rule.
retrieval augmented generation attention mechanism decoding retrieval-aware contrastive decoding expert models cross-document reasoning prefill bottlenecks document KV caches