2 citations · 4 across the 12 of their papers we have counts for
13 papers
VIEWCRAFT: Evolving What Multimodal Agents See and How They Read It
Binxiao Xu, Ruichuan An, Bocheng Zou +7
Reusable skills improve multimodal agents without updating model weights, but their effectiveness depends on both the evidence presented and the procedure used to read it. We intro…
REFLECTOR: Internalizing Step-wise Reflection against Indirect Jailbreak
Jiachen Ma, Jiawen Zhang, Xiangtian Li +3
While Large Language Models (LLMs) demonstrate remarkable capabilities, they remain susceptible to sophisticated, multi-step jailbreak attacks that circumvent conventional surface-…
Agent Skills Should Go Beyond Text: The Case for Visual Skills
Binxiao Xu, Ruichuan An, Bocheng Zou +1
Reusable skills are a key mechanism for extending agent capabilities, allowing agents to accumulate experience and solve increasingly complex tasks. Yet most existing skill-learnin…
MementoGUI: Learning Agentic Multimodal Memory Control for Long-Horizon GUI Agents
Ziyun Zeng, Hang Hua, Bocheng Zou +3
Recent GUI agents have made substantial progress in visual grounding and action prediction, yet they remain brittle in long-horizon tasks that require maintaining task state across…
Chrono-Gymnasium: An Open-Source, Gymnasium-Compatible Distributed Simulation Framework
Bocheng Zou, Harry Zhang, Khailanii Slaton +5
High-fidelity physics simulation is essential for closing the sim-to-real gap in robotics and complex mechanical systems. However, the computational overhead of high-fidelity engin…
ChronoAgentic: A Code-based Multi-Agent World Simulator for Physically Grounded Simulation Construction
Hongyu Wang, Jingquan Wang, Ashvin Anilkumar +3
Video-based world models generate visually plausible rollouts, but since they infer dynamics in latent states, they enforce no explicit physical constraints: contacts drift, shapes…
MuRF: Unlocking the Multi-Scale Potential of Vision Foundation Models
Bocheng Zou, Mu Cai, Mark Stanley +2
Vision Foundation Models (VFMs) have become the cornerstone of modern computer vision, offering robust representations across a wide array of tasks. While recent advances allow the…
UniCTokens: Boosting Personalized Understanding and Generation via Unified Concept Tokens
Ruichuan An, Sihan Yang, Renrui Zhang +10
Personalized models have demonstrated remarkable success in understanding and generating concepts provided by users. However, existing methods use separate concept tokens for under…
TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Mu Cai, Reuben Tan, Jianrui Zhang +12
Understanding fine-grained temporal dynamics is crucial for multimodal video comprehension and generation. Due to the lack of fine-grained temporal annotations, existing video benc…
VGBench: Evaluating Large Language Models on Vector Graphics Understanding and Generation
Bocheng Zou, Mu Cai, Jianrui Zhang +1
In the realm of vision models, the primary mode of representation is using pixels to rasterize the visual world. Yet this is not always the best or unique way to represent visual c…
LLM as Dataset Analyst: Subpopulation Structure Discovery with Large Language Model
Yulin Luo, Ruichuan An, Bocheng Zou +3
The distribution of subpopulations is an important property hidden within a dataset. Uncovering and analyzing the subpopulation distribution within datasets provides a comprehensiv…
Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want
Weifeng Lin, Xinyu Wei, Ruichuan An +6
In this paper, we present the Draw-and-Understand framework, exploring how to integrate visual prompting understanding capabilities into Multimodal Large Language Models (MLLMs). V…
MoSA: Mixture of Sparse Adapters for Visual Efficient Tuning
Qizhe Zhang, Bocheng Zou, Ruichuan An +2
With the rapid growth in the scale of pre-trained foundation models, parameter-efficient fine-tuning techniques have gained significant attention, among which Adapter Tuning is the…