1 citations · 2 across the 17 of their papers we have counts for
20 papers
LoopVL: Recurrent Visual Intelligence
Zhe Qian, Ziyang Gong, Zhongxing Xu +13
We introduce LoopVL to study whether Loop Transformers can be effectively extended to vision- language models. LoopVL combines Module-Loop and Model-Loop computation to iteratively…
Routing by Reasoning Need: Trajectory-Aware Decoding Control for Diffusion Vision-Language Models
Yixiang Liu, Zhongxing Xu, Zhonghua Wang +1
Diffusion vision-language models generate answers through iterative refinement, exposing intermediate answer trajectories that can be inspected and controlled at inference time. Ho…
SDARE-Bench: Evaluating Large Language Models on Conversational Stigma Detection and Response in Dyadic and Group Dialogue
Stephanie Fong, Yiwen Jiang, Zimu Wang +12
Large Language Models (LLMs) are increasingly used in advice seeking and decision making that may affect social judgements. Despite stigma's profound effects on people and communit…
VIBE-Bench: Evaluating Personalized Large Language Models When Profiles Don't Mean Preferences
Yiwen Jiang, Yang Deng, Stephanie Fong +9
Personalized Large Language Models (PLLMs) aim to tailor responses to individual users, where a central challenge is preference reasoning: inferring query-relevant preferences from…
AnchorSIPS: A Synthetic Dataset and Evaluation Resource for Evidence-Supported Psychosis-Risk Symptom Measurement
Guilherme C. Oliveira, Stephanie Fong, Zimu Wang +10
Progress on AI for psychosis-risk assessment is limited by a data-access bottleneck. Real clinical interviews are difficult to share because of privacy, governance, and consent con…
SVSR: A Self-Verification and Self-Rectification Paradigm for Multimodal Reasoning
Zhe Qian, Nianbing Su, Zhonghua Wang +6
Current multimodal models often suffer from shallow reasoning, leading to errors caused by incomplete or inconsistent thought processes. To address this limitation, we propose Self…
Cognitive Pivot Points and Visual Anchoring: Unveiling and Rectifying Hallucinations in Multimodal Reasoning Models
Zhe Qian, Yanbiao Ma, Zhuohan Ouyang +7
Multimodal Large Reasoning Models (MLRMs) have achieved remarkable strides in visual reasoning through test time compute scaling, yet long chain reasoning remains prone to hallucin…
Do No Harm: Exposing Hidden Vulnerabilities of LLMs via Persona-based Client Simulation Attack in Psychological Counseling
Qingyang Xu, Yaling Shen, Stephanie Fong +7
The increasing use of large language models (LLMs) in mental healthcare raises safety concerns in high-stakes therapeutic interactions. A key challenge is distinguishing therapeuti…
Thinking in Uncertainty: Mitigating Hallucinations in MLRMs with Latent Entropy-Aware Decoding
Zhongxing Xu, Zhonghua Wang, Zhe Qian +10
Recent advancements in multimodal large reasoning models (MLRMs) have significantly improved performance in visual question answering. However, we observe that transition words (e.…
PsychEthicsBench: Evaluating Large Language Models Against Australian Mental Health Ethics
Yaling Shen, Stephanie Fong, Yiwen Jiang +9
The increasing integration of large language models (LLMs) into mental health applications necessitates robust frameworks for evaluating professional safety alignment. Current eval…
A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers
Ming Hu, Chenglong Ma, Wei Li +117
Scientific Large Language Models (Sci-LLMs) are transforming how knowledge is represented, integrated, and applied in scientific research, yet their progress is shaped by the compl…
Seeing Far and Clearly: Mitigating Hallucinations in MLLMs with Attention Causal Decoding
Feilong Tang, Chengzhi Liu, Zhongxing Xu +9
Recent advancements in multimodal large language models (MLLMs) have significantly improved performance in visual question answering. However, they often suffer from hallucinations…
Confidence-Aware Self-Distillation for Multimodal Sentiment Analysis with Incomplete Modalities
Yanxi Luo, Shijin Wang, Zhongxing Xu +3
Multimodal sentiment analysis (MSA) aims to understand human sentiment through multimodal data. In real-world scenarios, practical factors often lead to uncertain modality missingn…
MMRC: A Large-Scale Benchmark for Understanding Multimodal Large Language Model in Real-World Conversation
Haochen Xue, Feilong Tang, Ming Hu +13
Recent multimodal large language models (MLLMs) have demonstrated significant potential in open-ended conversation, generating more accurate and personalized responses. However, th…
Incomplete Modality Disentangled Representation for Ophthalmic Disease Grading and Diagnosis
Chengzhi Liu, Zile Huang, Zhe Chen +6
Ophthalmologists typically require multimodal data sources to improve diagnostic accuracy in clinical decisions. However, due to medical device shortages, low-quality data and data…
Neighbor Does Matter: Density-Aware Contrastive Learning for Medical Semi-supervised Segmentation
Feilong Tang, Zhongxing Xu, Ming Hu +6
In medical image analysis, multi-organ semi-supervised segmentation faces challenges such as insufficient labels and low contrast in soft tissues. To address these issues, existing…
Toward Modality Gap: Vision Prototype Learning for Weakly-supervised Semantic Segmentation with CLIP
Zhongxing Xu, Feilong Tang, Zhe Chen +5
The application of Contrastive Language-Image Pre-training (CLIP) in Weakly Supervised Semantic Segmentation (WSSS) research powerful cross-modal semantic understanding capabilitie…
OphCLIP: Hierarchical Retrieval-Augmented Learning for Ophthalmic Surgical Video-Language Pretraining
Ming Hu, Kun Yuan, Yaling Shen +17
Surgical practice involves complex visual interpretation, procedural skills, and advanced medical knowledge, making surgical vision-language pretraining (VLP) particularly challeng…
OphNet: A Large-Scale Video Benchmark for Ophthalmic Surgical Workflow Understanding
Ming Hu, Peng Xia, Lin Wang +11
Surgical scene perception via videos is critical for advancing robotic surgery, telesurgery, and AI-assisted surgery, particularly in ophthalmology. However, the scarcity of divers…
Hunting Attributes: Context Prototype-Aware Learning for Weakly Supervised Semantic Segmentation
Feilong Tang, Zhongxing Xu, Zhaojun Qu +3
Recent weakly supervised semantic segmentation (WSSS) methods strive to incorporate contextual knowledge to improve the completeness of class activation maps (CAM). In this work, w…