2 citations · 4 across the 26 of their papers we have counts for
29 papers
ChemArgus: Automated Scientific Diagnostics for Lab-Grade Multimodal Chemistry Reasoning: A Frontier Benchmark
Hanyao Guo, Mo Cui, Zihan Tan +21
Chemistry evaluation has moved past multiple-choice probes toward open-ended expert problems, yet the benchmarks that define the current frontier still score answer endpoints alone…
Decouple, Purify and Unite: Semantic-Structural Prototype Learning for Federated Medical Segmentation
Xingyue Zhao, Wenke Huang, Linghao Zhuang +11
Federated learning enables medical institutions to train a global model without sharing data, yet feature heterogeneity from diverse scanners or protocols remains challenging. Exis…
MemWM: Memory-Augmented Text-Based World Model
Yujun Wang, Tao Zhang, Jinhe Bi +9
World models are increasingly used to support planning in agents by predicting how environment states evolve in response to agent actions. Yet fluent next-state predictions can sti…
DriveCache: Action-Aware Caching for Driving World Model Inference
Jianchun Yang, Jian Liang, Xianda Guo +5
Driving video generation models support autonomous-driving development by predicting controllable future scenes for simulation, planning evaluation, and offline data generation. Di…
TrustVLA: Mechanism-Guided Inference-Time Defense Against Vision-Language-Action Backdoors
Pinhan Fu, Xianda Guo, Xuetao Li +5
Vision-Language-Action (VLA) models are deployed through pipelines that end users cannot audit, and a poisoned VLA can behave normally on clean observations while a small visual tr…
Behavioural Signatures of Risk-Sensitive Decision-Making in Large Language Models
Xuankun Rong, Wenke Huang, Bo Du +2
As large language models (LLMs) are increasingly used in decision support, it is important to understand whether their choices under uncertainty exhibit stable and interpretable be…
Switch-Reasoner: Learn When to Think in Multitask Mixtures via Reinforcement Learning
Yiyang Fang, Pei Fu, Jinjie Li +7
Multimodal Large Language Models (MLLMs) often follow a fixed Think-then-Answer paradigm, which is inefficient in heterogeneous multitask settings because simple inputs may not req…
Co-policy: Responsive Human-Robot Co-Creation for Musical Performances
Xuetao Li, Wenke Huang, Mang Ye +4
Art has long stood as a pivotal expression of human creativity. Embodied artificial intelligence offers a route for generative models to participate in that creativity through phys…
StereoFactory: A Unified Merging Framework for Robust Stereo Matching
Xianda Guo, Pinhan Fu, Ruilin Wang +3
Stereo matching has advanced through foundation models trained on large-scale datasets, yet this paradigm suffers from a scalability bottleneck: incorporating new data requires cos…
Shift-Dependent Asymmetry: Orthogonal Inverse Low-Rank Adaptation for Federated Medical Segmentation
Xingyue Zhao, Wenke Huang, Linghao Zhuang +7
Low-Rank Adaptation (LoRA) enables efficient federated fine-tuning of segmentation foundation models for medical imaging. However, most federated LoRA methods adopt a uniform aggre…
EMO-R3: Reflective Reinforcement Learning for Emotional Reasoning in Multimodal Large Language Models
Yiyang Fang, Wenke Huang, Pei Fu +5
Multimodal Large Language Models (MLLMs) have shown remarkable progress in visual reasoning and understanding tasks but still struggle to capture the complexity and subjectivity of…
Generalizable Geometric Prior and Recurrent Spiking Feature Learning for Humanoid Robot Manipulation
Xuetao Li, Wenke Huang, Mang Ye +4
Humanoid robot manipulation is a crucial research area for executing diverse human-level tasks, involving high-level semantic reasoning and low-level action generation. However, pr…
RGMP: Recurrent Geometric-prior Multimodal Policy for Generalizable Humanoid Robot Manipulation
Xuetao Li, Wenke Huang, Nengyuan Pan +7
Humanoid robots exhibit significant potential in executing diverse human-level skills. However, current research predominantly relies on data-driven approaches that necessitate ext…
SafeGRPO: Self-Rewarded Multimodal Safety Alignment via Rule-Governed Policy Optimization
Xuankun Rong, Wenke Huang, Tingfeng Wang +3
Multimodal large language models (MLLMs) have demonstrated impressive reasoning and instruction-following capabilities, yet their expanded modality space introduces new composition…
Divide, Conquer and Unite: Hierarchical Style-Recalibrated Prototype Alignment for Federated Medical Segmentation
Xingyue Zhao, Wenke Huang, Xingguang Wang +5
Federated learning enables multiple medical institutions to train a global model without sharing data, yet feature heterogeneity from diverse scanners or protocols remains a major…
MAPO: Mixed Advantage Policy Optimization
Wenke Huang, Quan Zhang, Yiyang Fang +11
Recent advances in reinforcement learning for foundation models, such as Group Relative Policy Optimization (GRPO), have significantly improved the performance of foundation models…
S2FGL: Spatial Spectral Federated Graph Learning
Zihan Tan, Suyuan Huang, Guancheng Wan +3
Federated Graph Learning (FGL) combines the privacy-preserving capabilities of federated learning (FL) with the strong graph modeling capability of Graph Neural Networks (GNNs). Cu…
Backdoor Cleaning without External Guidance in MLLM Fine-tuning
Xuankun Rong, Wenke Huang, Jian Liang +5
Multimodal Large Language Models (MLLMs) are increasingly deployed in fine-tuning-as-a-service (FTaaS) settings, where user-submitted datasets adapt general-purpose models to downs…
CoT-Kinetics: A Theoretical Modeling Assessing LRM Reasoning Process
Jinhe Bi, Danqi Yan, Yifan Wang +8
Recent Large Reasoning Models significantly improve the reasoning ability of Large Language Models by learning to reason, exhibiting the promising performance in solving complex ta…
An Empirical Study of Federated Prompt Learning for Vision Language Model
Zhihao Wang, Wenke Huang, Tian Chen +7
The Vision Language Model (VLM) excels in aligning vision and language representations, and prompt learning has emerged as a key technique for adapting such models to downstream ta…
ThanoRA: Task Heterogeneity-Aware Multi-Task Low-Rank Adaptation
Jian Liang, Wenke Huang, Xianda Guo +3
Low-Rank Adaptation (LoRA) is widely adopted for downstream fine-tuning of foundation models due to its efficiency and zero additional inference cost. Many real-world applications…
Adversarial Curriculum Graph-Free Knowledge Distillation for Graph Neural Networks
Yuang Jia, Xiaojuan Shan, Jun Xia +5
Data-free Knowledge Distillation (DFKD) is a method that constructs pseudo-samples using a generator without real data, and transfers knowledge from a teacher model to a student by…
LoRASculpt: Sculpting LoRA for Harmonizing General and Specialized Knowledge in Multimodal Large Language Models
Jian Liang, Wenke Huang, Guancheng Wan +2
While Multimodal Large Language Models (MLLMs) excel at generalizing across modalities and tasks, effectively adapting them to specific downstream tasks while simultaneously retain…
Privacy-Enhancing Paradigms within Federated Multi-Agent Systems
Zitong Shi, Guancheng Wan, Wenke Huang +4
LLM-based Multi-Agent Systems (MAS) have proven highly effective in solving complex problems by integrating multiple agents, each performing different roles. However, in sensitive…