1 citations · 1 across the 9 of their papers we have counts for
10 papers
Automated Quality Assessment of Blind Sweep Obstetric Ultrasound for Improved Diagnosis
Prasiddha Bhandari, Kanchan Poudel, Nishant Luitel +6
Blind Sweep Obstetric Ultrasound (BSOU) enables scalable fetal imaging in low-resource settings by allowing minimally trained operators to acquire standardized sweep videos for aut…
Learning Modal-Mixed Chain-of-Thought Reasoning with Latent Embeddings
Yifei Shao, Kun Zhou, Ziming Xu +5
We study how to extend chain-of-thought (CoT) beyond language to better handle multimodal reasoning. While CoT helps LLMs and VLMs articulate intermediate steps, its text-only form…
PAN: A World Model for General, Actionable, and Long-Horizon World Simulation
PAN Team, Zihan Liu, Yi Gu +12
A world model is a cognitive simulator of the real-world environment allowing biological agents to reason about how the world evolves, whether spontaneously or in response to their…
Auto-scaling Continuous Memory for GUI Agent
Wenyi Wu, Kun Zhou, Ruoxin Yuan +4
We study how to endow GUI agents with scalable memory that help generalize across unfamiliar interfaces and long-horizon tasks. Prior GUI agents compress past trajectories into tex…
Vision-G1: Towards General Vision Language Reasoning with Multi-Domain Data Curation
Yuheng Zha, Kun Zhou, Yujia Wu +7
Despite their success, current training pipelines for reasoning VLMs focus on a limited range of tasks, such as mathematical and logical reasoning. As a result, these models face d…
Revisiting Reinforcement Learning for LLM Reasoning from A Cross-Domain Perspective
Zhoujun Cheng, Shibo Hao, Tianyang Liu +21
Reinforcement learning (RL) has emerged as a promising approach to improve large language model (LLM) reasoning, yet most open efforts focus narrowly on math and code, limiting our…
Activation Control for Efficiently Eliciting Long Chain-of-thought Ability of Language Models
Zekai Zhao, Qi Liu, Kun Zhou +4
Despite the remarkable reasoning performance, eliciting the long chain-of-thought (CoT) ability in large language models (LLMs) typically requires costly reinforcement learning or…
Decentralized Arena: Towards Democratic and Scalable Automatic Evaluation of Language Models
Yanbin Yin, Kun Zhou, Zhen Wang +11
The recent explosion of large language models (LLMs), each with its own general or specialized strengths, makes scalable, reliable benchmarking more urgent than ever. Standard prac…
Towards General Continuous Memory for Vision-Language Models
Wenyi Wu, Zixuan Song, Kun Zhou +3
Language models (LMs) and their extension, vision-language models (VLMs), have achieved remarkable performance across various tasks. However, they still struggle with complex reaso…
Causal-Copilot: An Autonomous Causal Analysis Agent
Xinyue Wang, Kun Zhou, Wenyi Wu +10
Causal analysis plays a foundational role in scientific discovery and reliable decision-making, yet it remains largely inaccessible to domain experts due to its conceptual and algo…