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cs.AI2026
CriticGen: Generation-Aware Evaluation as Actionable Feedback
Huifang Du, Zecheng Zuo, Sen Wang +3
Current evaluation methods for large language models are coarse-grained and decoupled from generation, producing generic explanations that fail to provide actionable feedback for m…
cs.CL2026
AgentV-RL: Scaling Reward Modeling with Agentic Verifier
Jiazheng Zhang, Ziche Fu, Zhiheng Xi +13
Verifiers have been demonstrated to enhance LLM reasoning via test-time scaling (TTS). Yet, they face significant challenges in complex domains. Error propagation from incorrect in…
cs.CL2026
Stable-DiffCoder: Pushing the Frontier of Code Diffusion Large Language Model
Chenghao Fan, Wen Heng, Bo Li +6
Diffusion-based language models (DLLMs) offer non-sequential, block-wise generation and richer data reuse compared to autoregressive (AR) models, but existing code DLLMs still lag…