11 papers
A Unified LLM-Adaptable Framework for Cold-Start Cognitive Diagnosis
Zihan Yao, Chentao Song, Yu He +4
Cognitive Diagnosis has become a critical task in AI-empowered education, supporting personalized learning by accurately assessing students' cognitive states. However, traditional…
Risk Awareness Injection: Calibrating Vision-Language Models for Safety without Compromising Utility
Mengxuan Wang, Yuxin Chen, Gang Xu +3
Vision language models (VLMs) extend the reasoning capabilities of large language models (LLMs) to cross-modal settings, yet remain highly vulnerable to multimodal jailbreak attack…
PhyCritic: Multimodal Critic Models for Physical AI
Tianyi Xiong, Shihao Wang, Guilin Liu +5
With the rapid development of large multimodal models, reliable judge and critic models have become essential for open-ended evaluation and preference alignment, providing pairwise…
KV-CoRE: Benchmarking Data-Dependent Low-Rank Compressibility of KV-Caches in LLMs
Jian Chen, Zhuoran Wang, Jiayu Qin +6
Large language models rely on kv-caches to avoid redundant computation during autoregressive decoding, but as context length grows, reading and writing the cache can quickly satura…
PRISM: Parametrically Refactoring Inference for Speculative Sampling Draft Models
Xuliang Wang, Yuetao Chen, Maochan Zhen +5
Large Language Models (LLMs), constrained by their auto-regressive nature, suffer from slow decoding. Speculative decoding methods have emerged as a promising solution to accelerat…
Learning to Decode Against Compositional Hallucination in Video Multimodal Large Language Models
Wenbin Xing, Quanxing Zha, Lizheng Zu +3
Current research on video hallucination mitigation primarily focuses on isolated error types, leaving compositional hallucinations, arising from incorrect reasoning over multiple i…