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cs.CL2026
Archer: Adaptive Reuse of Cached Hidden States for Efficient Rollback in Diffusion Language Models
Xuning He, Zinan Sheng, Yongding Tao +4
Diffusion language models (DLMs) iteratively refine a sequence, allowing earlier predictions to be revised as context evolves. This rollback capability distinguishes them from irre…
cs.CL2026
Detecting Data Contamination from Reinforcement Learning Post-training for Large Language Models
Yongding Tao, Tian Wang, Yihong Dong +4
Data contamination poses a significant threat to the reliable evaluation of Large Language Models (LLMs). This issue arises when benchmark samples may inadvertently appear in train…
cs.CL2025
Rethinking Repetition Problems of LLMs in Code Generation
Yihong Dong, Yuchen Liu, Xue Jiang +2
With the advent of neural language models, the performance of code generation has been significantly boosted. However, the problem of repetitions during the generation process cont…