activity
20162026
most citedUncertainty-Guided Mixup for Semi-Supervised Domain Adaptation without Source Data

22 citations · 83 across the 41 of their papers we have counts for

collaborators
Showing 2026Show all

15 papers · 1 filter

cs.CL2026

PhysioBench: A Unified Benchmark for Physiological Signal Question Answering

Mengxuan Li, Junfa Chen, Jinze Xia +5

Physiological signals support diverse clinical and monitoring tasks, yet existing physiological signal foundation models typically require task-specific adaptation for each task. N…

cs.LG2026

Pretraining Reusable Inference Across Views with Synthetic Task Priors

Jielong Lu, Zhihao Wu, Jiajun Yu +2

Modern pretrained encoders make representations from heterogeneous views increasingly reusable, but the procedure that determines view utility and combines evidence is still relear…

cs.CV2026

CoANeRV: Coordinate-Aware Token-Space Neural Video Representation

Jialong Guo, Ke Liu, Mengxuan Li +2

Neural representations for videos (NeRV) have shown strong reconstruction fidelity by storing video-specific information in network weights. However, existing formulations typicall…

cs.CV2026

CAER: Conflict-Aware Evidence Routing with Dual Prefix Experts for Multimodal Large Language Models

Zixuan Liu, Juntao Cai, Xiaoxu Cai +2

Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in multimodal understanding and generation. However, when textual inputs conflict with visual evi…

q-bio.GN2026

DeepBD: A Grounded Agentic Workflow for Variant Prioritization and Diagnosis of Genetic Birth Defects

Shiyu Li, Ziqi Yan, Zhihao Wu +7

Birth defects are a major cause of fetal loss, neonatal morbidity and long-term disability. In the subset with suspected genetic etiologies, exome and genome sequencing have moved…

cs.CV2026

Morphology-Aware Multimodal Representation Learning for Insect Phylogenetic Reconstruction

Zixuan Liu, Kaijie Yu, Chun He +5

Morphological traits provide important evidence for phylogenetic reconstruction and evolutionary relationship analysis. Recent image-based approaches have introduced deep learning,…

cs.LG2026

Sparsity Curse: Understanding RLVR Model Parameter Space from Model Merging

Chenrui Wu, Zexi Li, Jiajun Bu +2

Reinforcement Learning with Verifiable Reward (RLVR) has emerged as a powerful post-training paradigm that surpasses Supervised Fine-Tuning (SFT) in eliciting reasoning intelligenc…

cs.AI2026

Medical world models: representing medical states, modelling clinical dynamics and guiding intervention policies

Ke Liu, Mengxuan Li, Yanyi Bao +4

Medical diagnosis and treatment are dynamic processes in which patient states evolve over time and clinical interventions alter future outcomes. Although current medical AI can det…

cs.CV2026

Deep Reprogramming Distillation for Medical Foundation Models

Siyuan Du, Yuhang Zhou, Haolin Li +5

Medical foundation models pre-trained on large-scale datasets have shown powerful versatile performance. However, when adapting medical foundation models for specific medical scena…

cs.MA2026

Beyond the Individual: Virtualizing Multi-Disciplinary Reasoning for Clinical Intake via Collaborative Agents

Huangwei Chen, Wu Li, Junhao Jia +7

The initial outpatient consultation is critical for clinical decision-making, yet it is often conducted by a single physician under time pressure, making it prone to cognitive bias…

cs.MA2026

Sci-Mind: Cognitively-Inspired Adversarial Debate for Autonomous Mathematical Modeling

Junhao Jia, Huangwei Chen, Ruiying Sun +4

Real-world mathematical modeling is inherently an experiential and collaborative endeavor. Domain experts rarely solve complex problems from scratch; instead, they draw upon analog…

cs.IR2026

Learning Image-Text Matching with Optimal Partial Transport

Zhengxin Pan, Haishuai Wang, Fangyu Wu +3

Cross-modal matching, a fundamental task in bridging vision and language, has recently garnered substantial research interest. Despite the development of numerous methods aimed at…

cs.CL2026

LLM-Bootstrapped Targeted Finding Guidance for Factual MLLM-based Medical Report Generation

Cunyuan Yang, Dejuan Song, Xiaotao Pang +7

The automatic generation of medical reports utilizing Multimodal Large Language Models (MLLMs) frequently encounters challenges related to factual instability, which may manifest a…

eess.IV2026

Unsupervised Causal Prototypical Networks for De-biased Interpretable Dermoscopy Diagnosis

Junhao Jia, Yueyi Wu, Huangwei Chen +4

Despite the success of deep learning in dermoscopy image analysis, its inherent black-box nature hinders clinical trust, motivating the use of prototypical networks for case-based…

cs.CV2026

Directed Ordinal Diffusion Regularization for Progression-Aware Diabetic Retinopathy Grading

Huangwei Chen, Junhao Jia, Ruocheng Li +7

Diabetic Retinopathy (DR) progresses as a continuous and irreversible deterioration of the retina, following a well-defined clinical trajectory from mild to severe stages. However,…