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cs.AI2026
REA: From Correct LLM Judgments to Safe Decision Delivery
Jingxiang Meng, Aofan Liu, Yaoshuai Ma +2
Generative safety guards must classify requests correctly and return outputs that applications can parse. Reasoning guards can improve classification accuracy while producing outpu…
cs.AI2026
CURE: Local Uncertainty Repair for Block-Parallel Speculative Decoding
Aofan Liu, Jingxiang Meng, Fangxin Liu +1
Speculative decoding mitigates the latency of sequential generation in autoregressive Large Language Models (LLMs) by interleaving draft generation with target verification. Howeve…
cs.AI2026
TaPR: Test-Aware Policy Refinement for Feedback-Conditioned Code Generation
Aofan Liu, Jingxiang Meng, Fangxin Liu +1
Multi-turn code agents rely on execution feedback to repair incorrect programs, yet standard reinforcement learning paradigms optimize and evaluate policy performance primarily usi…