6 accepted papers
Semantic-level watermarking (SWM) for large language models (LLMs) enhances watermarking robustness against text modifications and paraphrasing attacks by treating the sentence as the fundamental unit. However, existing methods still lack strong theoretical guarantees of robustness, and reject-sampl…
The rapid adoption of LLMs has overshadowed the potential advantages of traditional BERT-like models in text classification. This study challenges the prevailing “LLM-centric” trend by systematically comparing three category methods, *i.e.,* BERT-like models fine-tuning, LLM internal state utilizati
Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in diverse reasoning tasks, yet their application to complex physics reasoning remains underexplored. Physics reasoning presents unique challenges, requiring grounding in physical conditions and the interpretation of
Vision-language models demand watermarking solutions that protect intellectual property without compromising multimodal coherence. Existing text watermarking methods disrupt visual-textual alignment through biased token selection and static strategies, leaving semantic-critical concepts vulnerable.
Measuring the motility and morphology parameters of motile cells is important for revealing their functional characteristics. This paper presents automation techniques that, for the first time, enable automated, non-invasive measurement of motility and morphology parameters of individual sperms. Com…
Manipulation of motile cells such as bacteria and sperm is required in both cell biology and clinical applications. For immobilizing a motile sperm, the sperm head and tail positions must be accurately tracked, interference of proximal sperms on the target sperm must be tackled, and the orientation…