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This work proposes tfour, a twin-critic method that calibrates token-level advantages from a single generated trajectory, and describes optimal mixing under the signal-retention constraint and establishes an upper bound on residual mean-induced drift.
This paper proposes Lever, a locality-aware collaborative retrieval framework that exploits query locality to accelerate graph-based RAG retrieval, and identifies and empirically validate a previously underexplored property of RAG workloads: strong per-user query locality.
The Federated Multi-Armed Bandit (FMAB) framework is proposed to facilitate collaborative model training in cloud-edge environments. Most existing FMAB-based systems assume that participants have personal datasets. This assumption becomes biased in scenarios with limited local data or when the discrepancy between histo...
Retrieval-Augmented Generation (RAG) grounds large language models in external knowledge and has become a key technique for knowledge-intensive tasks. As knowledge bases continue to scale, however, the retrieval stage increasingly dominates end-to-end latency, limiting the responsiveness of RAG systems. In this paper,...
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