Temporal vision-language models (TVLMs) offer a reusable, prompt-based interface for surgical video understanding, yet, their robustness under clinically realistic acquisition artifacts in endoscopy remains insufficiently characterized. In practice, degradations such as defocus, haze, motion blur, noise, cautery smoke,...
Darakshan Rashid, Raza Imam, Ufaq Khan et al.· 0 citations
Looped language models (LoopLMs) increase computational depth through parameter sharing, offering a path to scale inference computation without adding parameters. However, it remains unclear when additional recurrence is beneficial and how architectural choices affect its effectiveness. Through controlled experiments,...
Xin-Lin Zhuang, Si-Yuan Wang, Imran Razzak et al.· 0 citations
Operator-level Adaptive Muon Orthogonalization (AMO), an observe-then-commit method that measures weight geometry by operator type early in training and then uses these signals to allocate the NS budget for the remainder of training, delivers consistent improvements over uniform-schedule Muon.
Xin-Lin Zhuang, Panyi Ouyang, Yicheng Li et al.· 0 citations
Representative studies identify blood pressure as a convergence node linking adiposity, hepatic, and lipid phenotypes to vascular outcomes, and show that an adiposity-inflammation-blood-pressure pathway is largely attenuated by joint adjustment for body mass index (BMI) and smoking.
Yu-Long Li, Rong Xia, Yuxuan Zhang et al.· 0 citations
Medical benchmarks are dominated by single-turn, multiple-choice clinical cases that poorly reflect real consultations. Practically, clinicians elicit evidence interactively and patient communication varies widely. We introduce MedRoundsQA, a multi-turn diagnostic benchmark derived from 1,387 board-exam cases across 17...
Youssef Mohamed, Ahmed Heakl, Qin-Rong Cui et al.· 0 citations
This work proposes AsyTO, an Asymmetric Temporal Operator, an Asymmetric Temporal Operator that factorizes the tensor of per-variable operators into shared but distinct history-reading and future-writing temporal modes with per-variable mode-wise gains, complemented by a low-rank periodic prototype and a cycle-separabl...
This work investigates two representative model families, LLaVA-1.5 and Qwen2.5, and provides a token-, layer-, and head-level account of how VLMs transform object grounding into spatial relations, showing that knowing where objects are is not equivalent to knowing how they relate.
Xi-Wei Liu, Yu-Long Li, Xin-Lin Zhuang et al.· 1 citation
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