The collaboration between SAP Labs, France and EURECOM, France published a paper on January 13, 2026 titord "Parallel Context-of-Experts Decoding for Retrieval Augmented Generation". The paper introduces Parallel Context-of-Experts Decoding (PCED), a training-free framework designed to optimize Retrieval Augmented Generation (RAG) by overcoming the latency and reasoning limitations of long-context prompts. Rather than concatenating numerous documents into one massive input, PCED encodes each retrieved text independently as an isolated "expert" and synchronizes their predictions during the decoding stage. This method employs a retrieval-aware contrastive decoding rule that weights expert suggestions against a model prior using actual relevance scores. By shifting evidence aggregation from the attention mechanism to the decoding process, the system recovers cross-document reasoning capabilities without the computational burden of a shared attention context. Consequently, PCED achieves a significant speedup in time-to-first-token while maintaining or exceeding the accuracy of traditional long-context models. This approach proves especially robust against irrelevant distractors, as it isolates evidence and suppresses noise through dynamic expert selection at every generated token. Source: January 13, 2026 https://arxiv.org/pdf/2601.08670