Parallel Context-of-Experts Decoding for Retrieval Augmented Generation

摘要

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 retrieval-aware extension of context-aware decoding. This approach recovers cross-document reasoning capabilities without constructing a shared attention across documents.