21 citations · 23 across the 8 of their papers we have counts for
9 papers
TacMamba: A Tactile History Compression Adapter Bridging Fast Reflexes and Slow VLA Reasoning
Zhenan Wang, Yanzhe Wang, Meixuan Ren +8
In visually ambiguous manipulation such as detecting button click tactile feedback is often the sole source of ground truth. However, fusing tactile data poses a significant challe…
CoSpace: Benchmarking Continuous Space Perception Ability for Vision-Language Models
Yiqi Zhu, Ziyue Wang, Can Zhang +2
Vision-Language Models (VLMs) have recently witnessed significant progress in visual comprehension. As the permitting length of image context grows, VLMs can now comprehend a broad…
LVAgent: Long Video Understanding by Multi-Round Dynamical Collaboration of MLLM Agents
Boyu Chen, Zhengrong Yue, Siran Chen +4
Existing MLLMs encounter significant challenges in modeling the temporal context within long videos. Currently, mainstream Agent-based methods use external tools to assist a single…
DEEM: Dynamic Experienced Expert Modeling for Stance Detection
Xiaolong Wang, Yile Wang, Sijie Cheng +2
Recent work has made a preliminary attempt to use large language models (LLMs) to solve the stance detection task, showing promising results. However, considering that stance detec…
Speak It Out: Solving Symbol-Related Problems with Symbol-to-Language Conversion for Language Models
Yile Wang, Sijie Cheng, Zixin Sun +2
Symbols (or more broadly, non-natural language textual representations) such as numerical sequences, molecular formulas, and table delimiters widely exist, playing important roles…
Filling the Image Information Gap for VQA: Prompting Large Language Models to Proactively Ask Questions
Ziyue Wang, Chi Chen, Peng Li +1
Large Language Models (LLMs) demonstrate impressive reasoning ability and the maintenance of world knowledge not only in natural language tasks, but also in some vision-language ta…
EgoThink: Evaluating First-Person Perspective Thinking Capability of Vision-Language Models
Sijie Cheng, Zhicheng Guo, Jingwen Wu +4
Vision-language models (VLMs) have recently shown promising results in traditional downstream tasks. Evaluation studies have emerged to assess their abilities, with the majority fo…
Failures Pave the Way: Enhancing Large Language Models through Tuning-free Rule Accumulation
Zeyuan Yang, Peng Li, Yang Liu
Large Language Models (LLMs) have showcased impressive performance. However, due to their inability to capture relationships among samples, these frozen LLMs inevitably keep repeat…
A Dynamic LLM-Powered Agent Network for Task-Oriented Agent Collaboration
Zijun Liu, Yanzhe Zhang, Peng Li +2
Recent studies show that collaborating multiple large language model (LLM) powered agents is a promising way for task solving. However, current approaches are constrained by using…