36 citations · 38 across the 11 of their papers we have counts for
14 papers
Loop to Look Better: Learning Adaptive Depth for Question-Guided Perception in Vision-Language Models
Wencheng Ye, Anning Hu, Hongye Fang +2
Visual perception should be iterative and task-directed, with representations refined in response to the question and the need for further processing. While Vision-Language Models…
Edit, Don't Imitate: Minimal-Edit On-Policy Distillation for Multimodal Reasoning
Wen Ye, Ailin Deng, Zirui Wang +8
Visual mathematical reasoning can fail before reasoning begins: when a vision-language model (VLM) misreads a diagram, subsequent reasoning proceeds from false premises. This perce…
Write Back the : Revisiting the Same Tokens with Fresh Representations
Wencheng Ye, Anning Hu, Xiangdong Zhang +5
Transformers process information strictly forward through depth, preventing deeper computation from revisiting and refining earlier representations. To augment the standard forward…
Terminal Agents: A Survey of AI Agents in Command-Line Environments
Yi Bin, Xiaoyang Yuan, Haoxi Zeng +9
Large language model agents increasingly act through terminals, yet existing surveys disperse terminal-mediated behavior across software engineering, tool use, and computer-use res…
MIRROR: Learning from the Other View for Multi-Modal Reasoning
Wen Ye, Yuxiao Qu, Aviral Kumar +1
Vision-language models (VLMs) can behave very differently depending on whether the same underlying problem is presented textually or visually. This inconsistency suggests that diff…
Xiaomi-Robotics-1: Scaling Vision-Language-Action Models with over 100K Hours of Real-World Trajectories
Xiaomi Robotics Team, Jun Guo, Piaopiao Jin +31
We present Xiaomi-Robotics-1, a foundational vision-language-action (VLA) model capable of (1) following diverse language instructions to perform a wide range of mobile manipulatio…
The Ebb and Flow of Multimodal Focus: Scheduling Visual Relay Windows for Grounded VLM Reasoning
Wencheng Ye, Yi Bin, Yujuan Ding +7
Vision-language models increasingly succeed on multimodal reasoning benchmarks, yet their visual evidence often becomes unstable once it enters the language stack, weakening eviden…
RISER: Orchestrating Latent Reasoning Skills for Adaptive Activation Steering
Wencheng Ye, Xiaoyang Yuan, Yi Bin +4
Recent work on domain-specific reasoning with large language models (LLMs) often relies on training-intensive approaches that require parameter updates. While activation steering h…
U2F: Encouraging SWE-Agent to Seize Novelty without Losing Feasibility
Wencheng Ye, Yan Liu
Large language models (LLMs) have shown strong capabilities in software engineering tasks, yet most existing LLM-based SWE-Agents mainly tackle well-defined problems using conventi…
ActDistill: General Action-Guided Self-Derived Distillation for Efficient Vision-Language-Action Models
Wencheng Ye, Tianshi Wang, Lei Zhu +3
Recent Vision-Language-Action (VLA) models have shown impressive flexibility and generalization, yet their deployment in robotic manipulation remains limited by heavy computational…
When LLM Meets Time Series: Can LLMs Perform Multi-Step Time Series Reasoning and Inference
Wen Ye, Jinbo Liu, Defu Cao +2
The rapid advancement of Large Language Models (LLMs) has sparked growing interest in their application to time series analysis tasks. However, their ability to perform complex rea…
TS-Reasoner: Domain-Oriented Time Series Inference Agents for Reasoning and Automated Analysis
Wen Ye, Wei Yang, Defu Cao +4
Time series analysis is crucial in real-world applications, yet traditional methods focus on isolated tasks only, and recent studies on time series reasoning remain limited to eith…
TimeDiT: General-purpose Diffusion Transformers for Time Series Foundation Model
Defu Cao, Wen Ye, Yizhou Zhang +1
Foundation models, particularly Large Language Models (LLMs), have revolutionized text and video processing, yet time series data presents distinct challenges for such approaches d…
TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting
Defu Cao, Furong Jia, Sercan O Arik +4
The past decade has witnessed significant advances in time series modeling with deep learning. While achieving state-of-the-art results, the best-performing architectures vary high…