38 citations · 64 across the 30 of their papers we have counts for
37 papers
EVA-Cap: Optimizing Audiovisual Video Captioning via Event-Centric Alignment
Jiapeng Shi, Qiuxia Hou, Jiaqi Leng +5
Recent advances in Omni Language Models (OLMs) provide a unified solution for audiovisual video captioning. Nevertheless, existing approaches predominantly treat captioning as an u…
OpenViTac: Learning and Benchmarking Visuo-Tactile Policies in a Unified Sim-and-Real Framework
Yifan Wu, Qin Li, Nan Min +14
Tactile feedback provides embodied agents with physical information beyond visual observations, enabling more reliable interaction with the real world. However, despite the rapid p…
EgoExo-Next:Benchmarking Vision-Language Models on Visual-Option Next-State and Cross-View Reasoning
Yutong Li, Molin Wang, Xiaotong Li +3
Vision-language models (VLMs) are increasingly evaluated for egocentric and cross-view video reasoning, yet existing benchmarks largely focus on semantic event understanding, tempo…
LIBERO-Agent: Evaluating General-Purpose Agents for Direct Embodied Manipulation
Zijie Diao, Yitong Chen, Sicheng Xie +7
General-purpose agents can plan, use tools, and revise their behavior from feedback, but it remains unclear whether these capabilities transfer from digital environments to embodie…
Chinese-Jev: Bringing System One Model to Chinese-Language Tasks
Zexiao Wang, Zihao Zhang, Xudong Wang +5
System One models such as Jev offer an efficient alternative to generative language models for tasks that require decisions rather than open-ended responses. However, existing Jev…
Understanding Curriculum Learning in Large Language Models via Cross-Difficulty Optimization Dynamics
Zhikai Ding, Ziyi Ye
Curriculum learning has been widely adopted in the post-training of large language models by organizing training data from easy to hard. However, its effectiveness varies substanti…
Explicit Language Memory for Long-Horizon Planning in Vision-Language-Action Models
Houze Xu, Jizhong Li, Ziyi Ye
Vision-language-action (VLA) models provide a unified paradigm for connecting visual perception, language understanding, and robotic control. However, existing VLA models still fac…
Seeing Touch from Motion: A Unified Modality-Aware Visuo-Tactile Policy with Tactile Motion Correlation
Shengqi Xu, Guojin Zhong, Yang Liu +7
Visuo-Tactile policies leveraging optical tactile sensors have shown great promise in contact-rich manipulation. These sensors achieve high spatial resolution and multi-dimensional…
ThinkingVLA: Interleaved Vision and Language Reasoning for Robotic Manipulation
Tianyi Lu, Hui Zhang, Zijie Diao +8
Most Vision-Language-Action (VLA) models map observations directly to actions without explicit reasoning, limiting their capacity for reasoning-intensive long-horizon tasks. To add…
Teach Multimodal Recommendation Model to See via Personalized Visual Extraction and Adaptive Learning
Yutong Li, Xinyi Zhang, Ziyi Ye +2
Multimodal sequential recommendation (MSR) incorporates textual and visual information to improve recommendation quality. However, recent studies and our empirical analysis show th…
ActiveMimic: Egocentric Video Pretraining with Active Perception
Xingyao Lin, Guojin Zhong, Tianyi Lu +4
Egocentric human video offers a scalable alternative to robot data for pretraining, yet models pretrained on such video consistently underperform those pretrained on robot data. We…
VLA-Pro: Cross-Task Procedural Memory Transfer for Vision-Language-Action Models
Shengyu Si, Yuanzhuo Lu, Ruimeng Yang +3
Vision-Language-Action (VLA) models have shown strong potential for general-purpose robotic manipulation, yet they still struggle to generalize to unseen tasks that necessitate tra…
Adversarial Orthogonal Disentanglement for LVLM Hallucination Mitigation
Ruoxi Cheng, Haoxuan Ma, Zhengfei Hai +6
Large Vision-Language Models (LVLMs) have advanced multimodal understanding, yet their reliability is limited by hallucination, where generated content conflicts with visual facts.…
How do Humans Process AI-generated Hallucination Contents: a Neuroimaging Study
Shuqi Zhu, Yi Zhong, Ziyi Ye +4
While AI-generated hallucinations pose considerable risks, the underlying cognitive mechanisms by which humans can successfully recognize or be misled by these hallucinations remai…
Atoms of Thought: Universal EEG Representation Learning with Microstates
Xinyang Tian, Ruitao Liu, Ziyi Ye +3
Learning universal representations from electroencephalogram (EEG) signals is a cutting-edge approach in the field of neuroinformatics and brain-computer interfaces (BCIs). Convent…
Steering the Verifiability of Multimodal AI Hallucinations
Jianhong Pang, Ruoxi Cheng, Ziyi Ye +4
AI applications driven by multimodal large language models (MLLMs) are prone to hallucinations and pose considerable risks to human users. Crucially, such hallucinations are not eq…
Robotic Grasping and Placement Controlled by EEG-Based Hybrid Visual and Motor Imagery
Yichang Liu, Tianyu Wang, Ziyi Ye +4
We present a framework that integrates EEG-based visual and motor imagery (VI/MI) with robotic control to enable real-time, intention-driven grasping and placement. Motivated by th…
sleep2vec: Unified Cross-Modal Alignment for Heterogeneous Nocturnal Biosignals
Weixuan Yuan, Zengrui Jin, Yichen Wang +4
Tasks ranging from sleep staging to clinical diagnosis traditionally rely on standard polysomnography (PSG) devices, bedside monitors and wearable devices, which capture diverse no…
Towards Unification of Hallucination Detection and Fact Verification for Large Language Models
Weihang Su, Jianming Long, Changyue Wang +5
Large Language Models (LLMs) frequently exhibit hallucinations, generating content that appears fluent and coherent but is factually incorrect. Such errors undermine trust and hind…
TwinVoice: A Multi-dimensional Benchmark Towards Digital Twins via LLM Persona Simulation
Bangde Du, Minghao Guo, Songming He +8
Large Language Models (LLMs) are exhibiting emergent human-like abilities and are increasingly envisioned as the foundation for simulating an individual's communication style, beha…
SurGE: A Benchmark and Evaluation Framework for Scientific Survey Generation
Weihang Su, Anzhe Xie, Qingyao Ai +5
The rapid growth of academic literature makes the manual creation of scientific surveys increasingly infeasible. While large language models show promise for automating this proces…
Node-as-Agent: Graph Agentic Network
Minghao Guo, Xi Zhu, Qingyue Jiao +7
Graph Neural Networks (GNNs) have achieved remarkable success in graph-based learning by propagating information among neighbor nodes via predefined aggregation mechanisms. However…
Are Recommenders Self-Aware? Label-Free Recommendation Performance Estimation via Model Uncertainty
Jiayu Li, Ziyi Ye, Guohao Jian +4
Can a recommendation model be self-aware? This paper investigates the recommender's self-awareness by quantifying its uncertainty, which provides a label-free estimation of its per…
ValueSim: Generating Backstories to Model Individual Value Systems
Bangde Du, Ziyi Ye, Zhijing Wu +5
As Large Language Models (LLMs) continue to exhibit increasingly human-like capabilities, aligning them with human values has become critically important. Contemporary advanced tec…