6 papers
How Order-Sensitive Are LLMs? OrderProbe for Deterministic Structural Reconstruction
Zhaolu Kang, Yingjie He, Kehan Jiang +18
Large language models (LLMs) excel at semantic understanding, yet their ability to reconstruct internal structure from scrambled inputs remains underexplored. Sentence-level restor…
SpineBench: A Clinically Salient, Level-Aware Benchmark Powered by the SpineMed-450k Corpus
Ming Zhao, Wenhui Dong, Yang Zhang +23
Spine disorders affect 619 million people globally and are a leading cause of disability, yet AI-assisted diagnosis remains limited by the lack of level-aware, multimodal datasets.…
Temporal Consistency Constrained Transferable Adversarial Attacks with Background Mixup for Action Recognition
Ping Li, Jianan Ni, Bo Pang
Action recognition models using deep learning are vulnerable to adversarial examples, which are transferable across other models trained on the same data modality. Existing transfe…
MomentSeeker: A Task-Oriented Benchmark For Long-Video Moment Retrieval
Huaying Yuan, Jian Ni, Zheng Liu +7
Accurately locating key moments within long videos is crucial for solving long video understanding (LVU) tasks. However, existing benchmarks are either severely limited in terms of…
Practical token pruning for foundation models in few-shot conversational virtual assistant systems
Haode Qi, Cheng Qian, Jian Ni +6
In an enterprise Virtual Assistant (VA) system, intent classification is the crucial component that determines how a user input is handled based on what the user wants. The VA syst…
Distilling Event Sequence Knowledge From Large Language Models
Somin Wadhwa, Oktie Hassanzadeh, Debarun Bhattacharjya +2
Event sequence models have been found to be highly effective in the analysis and prediction of events. Building such models requires availability of abundant high-quality event seq…