4 citations · 18 across the 21 of their papers we have counts for
6 papers · 1 filter
CoLearn: An Agentic Tutor that Learns its Learner in a Human--AI Co-Learning Loop
Kailai He, Zhihao Wu, Linhai Zhang +3
Good tutoring adapts to the individual: it tracks what a learner knows, notices why they go wrong, and asks the next question that will help most. Most deployed tutoring tools inst…
Follow the Latent Roadmap: Navigating Revocable Decoding for Diffusion LLMs with Anchor Tokens
Yizhen Yao, Qinglin Zhu, Runcong Zhao +4
Diffusion Large Language Models (dLLMs) offer a promising avenue for parallel generation but face a trade-off between decoding speed and quality. While revocable decoding strategie…
EDIT: Evidence-Diagnosed Intervention Training for Rule-Faithful LLM Grading
Zhihao Wu, Linhai Zhang, Taiyi Wang +4
Reliable rubric grading requires more than accurate score prediction. Each judgement must be grounded in the mark scheme and evidence from the student answer. Existing credit-assig…
Linear Ensembles Wash Away Watermarks: On the Fragility of Distributional Perturbations in LLMs
Zhihao Wu, Gracia Gong, Qinglin Zhu +2
Watermarking embeds statistical signatures in AI-generated text for detection and attribution. We reveal a fundamental vulnerability: when users access multiple models (today's rea…
Detecting Contextual Hallucinations in LLMs with Frequency-Aware Attention
Siya Qi, Yudong Chen, Runcong Zhao +6
Hallucination detection is critical for ensuring the reliability of large language models (LLMs) in context-based generation. Prior work has explored intrinsic signals available du…
Pull Requests as a Training Signal for Repo-Level Code Editing
Qinglin Zhu, Tianyu Chen, Shuai Lu +8
Repository-level code editing requires models to understand complex dependencies and execute precise multi-file modifications across a large codebase. While recent gains on SWE-ben…