6 papers
A Controlled Audit of Pretraining Contamination in Public Medical Vision-Language Benchmarks
Bruce Changlong Xu, Lan Wu, Alexander Ryu
Medical vision-language models (VLMs) are evaluated on public benchmarks whose images and question-answer pairs have been freely downloadable for years, yet reported accuracy assum…
Alignment Collapse Under KV Cache Quantization: Diagnosis and Mitigation
Bruce Changlong Xu, Adarsh Kumarappan, Mu Zhou
Key-value (KV) cache quantization is widely used to reduce Large Language Model (LLM) inference memory, yet existing evaluations solely focus on measuring perplexity and accuracy w…
Typhoon: Towards an Effective Task-Specific Masking Strategy for Pre-trained Language Models
Muhammed Shahir Abdurrahman, Hashem Elezabi, Bruce Changlong Xu
The choice of \emph{which} tokens to mask is a central, under-examined design decision in masked language modeling (MLM). Standard pretraining masks tokens uniformly at random, but…
Training-Inference Kernel Contracts: Bounding Divergence in Post-Training and Deployment
Bruce Changlong Xu, Lan Wu
A modern post-training pipeline often writes one symbol for its policy, pi_theta, while evaluating it through two different programs: a training kernel optimized for autograd and a…
From Theory to Decision Rule: Calibrating the Noisy-Label Crossover for Vision-Language Model Weak Supervision Across Three Medical-Imaging Benchmarks
Bruce Changlong Xu, Jose James, Alexander Ryu
Classical noisy-label theory predicts that downstream performance under weak supervision is bounded above by the labeler's accuracy, implying a sharp crossover: once a gold-trained…
Activation Sensitivity as a Unifying Principle for Post-Training Quantization
Bruce Changlong Xu
Post-training quantization (PTQ) methods for large language models rely on heuristics that implicitly estimate which weight channels most strongly influence model behavior. Two dom…