Retrieval-augmented generation (RAG) dominates repository-level code completion: it retrieves cross-file context (R), then decodes one greedy completion (G). Existing work mainly focuses on retrieval and stops there. We argue both stages can be improved together, with generation in particular gaining from test-time sca...
Jia-Jie Wang, Yu-Tong Zhao, Ke-Bin Peng et al.· 0 citations
Large Vision-Language Models (LVLMs) have achieved remarkable performance across a wide range of tasks; however, they often inherit social biases from their training data, resulting in biased behavior when processing portraits from different social groups. Existing debiasing approaches typically compare token probabili...
Yisong Xiao, Aishan Liu, Yongxin Huang et al.· 0 citations
This work studies uncertainty-aware rollback decoding, an inference-time strategy that uses uncertainty signals to identify unreliable generation regions and roll back to earlier valid prefixes without retraining the model.
Xianzong Wu, Xiaohong Li, Yue-Jun Guo et al.· 0 citations
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