1 citations · 1 across the 11 of their papers we have counts for
13 papers
VISTA: Verifier-Informed Student-to-Teacher Adaptation for On-Policy Self-Distillation
Zewen Ding, Zezhong Wu, Zhou Tao +5
On-policy self-distillation (OPSD) improves reasoning by training a problem-only student on its own rollouts using dense token-level supervision from a privileged teacher that also…
LOCUS: Local Visual Cue Search for Enhancing Fine-Grained Perception in Multimodal Large Language Models
Zhou Tao, Fang Zhang, Zewen Ding +5
Multimodal Large Language Models (MLLMs) remain unreliable on fine-grained visual perception, even when high-resolution inputs preserve the necessary local details. We identify thi…
Risk-Aware LLM Agents for Geospatial Data Retrieval: Design and Preliminary Adversarial Evaluation
Kyle Gao, Joel Cumming, Jonathan Li +2
We present an LLM-driven framework for retrieving remote sensing data from cloud-based geospatial catalogues using natural language queries. The system converts user intent into st…
Dynamic Token Compression for Efficient Video Understanding through Reinforcement Learning
Shida Wang, YongXiang Hua, Zhou Tao +2
Multimodal Large Language Models have demonstrated remarkable capabilities in video understanding, yet face prohibitive computational costs and performance degradation from ''conte…
When Thinking Hurts: Mitigating Visual Forgetting in Video Reasoning via Frame Repetition
Xiaokun Sun, Yubo Wang, Haoyu Cao +1
Recently, Multimodal Large Language Models (MLLMs) have demonstrated significant potential in complex visual tasks through the integration of Chain-of-Thought (CoT) reasoning. Howe…
DiG: Differential Grounding for Enhancing Fine-Grained Perception in Multimodal Large Language Model
Zhou Tao, Shida Wang, Yongxiang Hua +2
Multimodal Large Language Models have achieved impressive performance on a variety of vision-language tasks, yet their fine-grained visual perception and precise spatial reasoning…
Input Domain Aware MoE: Decoupling Routing Decisions from Task Optimization in Mixture of Experts
Yongxiang Hua, Haoyu Cao, Zhou Tao +4
Sparse Mixture of Experts (sMoE) has become a pivotal approach for scaling large vision-language models, offering substantial capacity while maintaining computational efficiency th…
Large Reasoning Embedding Models: Towards Next-Generation Dense Retrieval Paradigm
Jianting Tang, Dongshuai Li, Tao Wen +3
In modern e-commerce search systems, dense retrieval has become an indispensable component. By computing similarities between query and item (product) embeddings, it efficiently se…
CROP: Integrating Topological and Spatial Structures via Cross-View Prefixes for Molecular LLMs
Jianting Tang, Yubo Wang, Haoyu Cao +1
Recent advances in molecular science have been propelled significantly by large language models (LLMs). However, their effectiveness is limited when relying solely on molecular seq…
BASIC: Boosting Visual Alignment with Intrinsic Refined Embeddings in Multimodal Large Language Models
Jianting Tang, Yubo Wang, Haoyu Cao +1
Mainstream Multimodal Large Language Models (MLLMs) achieve visual understanding by using a vision projector to bridge well-pretrained vision encoders and large language models (LL…
FPEdit: Robust LLM Fingerprinting through Localized Parameter Editing
Shida Wang, Chaohu Liu, Yubo Wang +1
Large language models represent significant investments in computation, data, and engineering expertise, making them extraordinarily valuable intellectual assets. Nevertheless, the…
AdPO: Enhancing the Adversarial Robustness of Large Vision-Language Models with Preference Optimization
Chaohu Liu, Tianyi Gui, Yu Liu +1
Large Vision-Language Models (LVLMs), such as GPT-4o and LLaVA, have recently witnessed remarkable advancements and are increasingly being deployed in real-world applications. Howe…
Tracking the Copyright of Large Vision-Language Models through Parameter Learning Adversarial Images
Yubo Wang, Jianting Tang, Chaohu Liu +1
Large vision-language models (LVLMs) have demonstrated remarkable image understanding and dialogue capabilities, allowing them to handle a variety of visual question answering task…