3 papers
cs.CR2026
Learn from Your Mistakes: Tree-like Self-Play for Secure Code LLMs
Wenqi Chen, Ziyan Zhang, Bin Wang +3
While Large Language Models (LLMs) excel in code generation, they remain prone to replicating subtle yet critical vulnerabilities endemic to their training data. Current alignment…
cs.CL2026
Confidence Before Answering: A Paradigm Shift for Efficient LLM Uncertainty Estimation
Changcheng Li, Jiancan Wu, Hengheng Zhang +5
Reliable deployment of large language models (LLMs) requires accurate uncertainty estimation. Existing methods are predominantly answer-first, producing confidence only after gener…
cs.CL2026
Punctuation-aware Hybrid Trainable Sparse Attention for Large Language Models
Junxiang Qiu, Shuo Wang, Zhengsu Chen +4
Attention serves as the fundamental mechanism for long-context modeling in large language models (LLMs), yet dense attention becomes structurally prohibitive for long sequences due…