most citedResT: Reshaping Token-Level Policy Gradients for Tool-Use Large Language Models

1 citations · 1 across the 12 of their papers we have counts for

collaborators

16 papers

cs.HC2026

Task-State Governance: A Virtual Machine for Verifiable and Recoverable GUI Agent Execution

Wenkui Yang, Yiming Hao, Kai Zhou +6

Graphical user interface (GUI) agents now execute workflows across real applications, but when execution drifts, users must determine what changed, what should have remained untouc…

cs.CV2026

GaugeVLM: Structuring Spatial Supervision with Measured Geometric Interventions

Hongbo Wang, Zihan Lin, Wenkui Yang +5

Vision-language models (VLMs) can contradict themselves across views of the same spatial relation and fail to respond when that relation changes. Addressing these failures requires…

cs.AI2026

VLM-in-Sandbox: Visual Workspaces for Agentic Visual Reasoning

Hexiong Yang, Mingrui Chen, Jie Cao +1

Sandboxed computer environments support multi-step reasoning with tools, executable programs, and persistent files, yet their extension from language models to vision-language mode…

cs.LG2026

One Step, One Lead: Mitigating Higher-Order Interference in Multi-Domain Reinforcement Learning via Cross-Step Control

Zihan Lin, Xiaohan Wang, Jie Cao +4

Reinforcement learning (RL) across multiple domains can broaden the reasoning capabilities of large language models (LLMs), yet joint training often degrades individual-domain perf…

cs.CL2026

When Not to Imitate: Boundary-Aware Skill Memory for Reliable Tool-Use LLM Agents

Zihan Lin, Zhenyu Chen, Jiawen Wei +6

Extracting skills from past successes is critical for the efficient evolution of Large Language Model (LLM) agents. Prevailing agent self-evolution paradigms typically rely on a co…

cs.AI2026

VlogReward: Learning Multi-Dimensional Evaluation for Vlog Editing

Yexiang Liu, Wen Zhong, Sijie Zhu +6

The rapid rise of vlogs as a personalized storytelling medium has created a demand for automated systems to evaluate and refine vlog editing plans. However, vlog assessment is high…

cs.CV2026

Regulating Anatomy-Aware Rewards via Trajectory-Integral Feedback for Volumetric Computed Tomography Analysis

Tianwei Lin, Zhongwei Qiu, Jie Cao +7

Medical vision-language models (VLMs) have rapidly advanced as general-purpose multimodal assistants, yet their deployment in 3D Computed Tomography (CT) analysis remains constrain…

cs.LG2026

HydroAgent: Closing the Gap Between Frontier LLMs and Human Experts in Hydrologic Model Calibration via Simulator-Grounded RL

Zhi Li, Songkun Yan, Jie Cao +4

Calibrating distributed hydrologic models is a critical bottleneck across operational water resources management - streamflow prediction, reservoir operation, drought monitoring, i…

cs.CR2026

From AI-Generated Content to Agentic Action: Security and Safety Threats in Generative AI

Zelin Zhang, Qi Li, Jie Cao +2

Generative AI systems are increasingly used not only to produce content but also to retrieve data, invoke tools, and execute actions. This work examines the security and safety imp…

cs.LG2026

ResRL: Boosting LLM Reasoning via Negative Sample Projection Residual Reinforcement Learning

Zihan Lin, Xiaohan Wang, Jie Cao +6

Reinforcement Learning with Verifiable Rewards (RLVR) enhances reasoning of Large Language Models (LLMs) but usually exhibits limited generation diversity due to the over-incentivi…

cs.AI2026

HalluClear: Diagnosing, Evaluating and Mitigating Hallucinations in GUI Agents

Chao Jin, Wenkui Yang, Hao Sun +6

While progress in GUI agents has been largely driven by industrial-scale training, ungrounded hallucinations often trigger cascading failures in real-world deployments.Unlike gener…

cs.CR2026

Are GUI Agents Focused Enough? Automated Distraction via Semantic-level UI Element Injection

Wenkui Yang, Chao Jin, Haisu Zhu +7

Existing red-teaming studies on GUI agents face two fundamental limitations: adversarial perturbations require white-box access unavailable in commercial deployments, while prompt…

cs.CL2026

AWPO: Enhancing Tool-Use of Large Language Models through Adaptive Integration of Reasoning Rewards

Zihan Lin, Xiaohan Wang, Hexiong Yang +5

While Reinforcement Learning (RL) shows promise in training tool-use Large Language Models (LLMs) using verifiable outcome rewards, existing methods largely overlook the potential…

cs.CV2025

Towards Robust Defense against Customization via Protective Perturbation Resistant to Diffusion-based Purification

Wenkui Yang, Jie Cao, Junxian Duan +1

Diffusion models like Stable Diffusion have become prominent in visual synthesis tasks due to their powerful customization capabilities, which also introduce significant security r…

cs.CL2025★ 1 cited

ResT: Reshaping Token-Level Policy Gradients for Tool-Use Large Language Models

Zihan Lin, Xiaohan Wang, Jie Cao +4

Large language models (LLMs) transcend passive generation and act as goal-directed agents by invoking external tools. Reinforcement learning (RL) offers a principled framework for…

cs.CV2025

Marmot: Object-Level Self-Correction via Multi-Agent Reasoning

Jiayang Sun, Hongbo Wang, Jie Cao +2

While diffusion models excel at generating high-quality images, they often struggle with accurate counting, attributes, and spatial relationships in complex multi-object scenes. On…