arXiv AI June 8, 2026 By Haojie Hao, Longkun Hao, Yihang Lou, Yan Bai, Zhenyang Li, Zhichao Yang, Dongshuo Huang, Hongyu Lin, Lanqing Hong, Jiakai Wang, Xianglong Liu

StainFlow: Entity-Stain Tracking and Evidence Linking for Process Rewards in GUI Agents

Read the original on arXiv AI →

arXiv:2606. 07027v1 Announce Type: new Abstract: Reinforcement Learning (RL) has become a promising approach for improving GUI Agents in long-horizon, stochastic digital environments, but trajectory-level success feedback is too sparse to provide reliable credit assignment for intermediate exploration steps.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 11

ProcessThinker: Enhancing Multi-modal Large Language Models Reasoning via Rollout-based Process Reward

arXiv:2606. 11209v1 Announce Type: cross Abstract: Visual question answering increasingly requires multi-step reasoning.

By Jingpei Wu, Xiao Han, Weixiang Shen, Boer Zhang, Zifeng Ding, Volker Tresp

llms nlp reinforcement-learning fine-tuning multimodal benchmarks

arXiv AI

Jun 2

AgentProcessBench: Diagnosing Step-Level Process Quality in Tool-Using Agents

arXiv:2603. 14465v2 Announce Type: replace Abstract: While Large Language Models (LLMs) have evolved into tool-using agents, they remain brittle in long-horizon interactions.

By Shengda Fan, Xuyan Ye, Yupeng Huo, Zhi-Yuan Chen, Yiju Guo, Shenzhi Yang, Wenkai Yang, Shuqi Ye, Jingwen Chen, Haotian Chen, Xin Cong, Yankai Lin

llms agents benchmarks

arXiv AI

Jul 28

SeekJudge: A Practical Reward Framework for Reinforcement Learning in Computer-Use Agents

arXiv:2607. 23263v1 Announce Type: new Abstract: Deciding whether a trajectory actually fulfills its instruction governs how we measure computer-use agents on long-horizon graphical-user-interface tasks and how we train them with reinforcement learning.

By Yang Wan, Zhenhao Zhang, Jierui Wang, Linchao Zhu

agents reinforcement-learning efficiency

arXiv AI

1d ago

TRCA: Transition-wise Rubric Credit Assignment for Long-horizon LLM Agents

arXiv:2608. 16156v1 Announce Type: new Abstract: Long-horizon large language model (LLM) agents are typically optimized with sparse terminal outcomes, making fine-grained credit assignment across multi-step interactions difficult.

By Huan Zhang, Mingju Chen, Dongxu Zhou, Can Lv, Heng Chang, Sen Cui, Faguo Wu, Shiji Zhou

llms agents reinforcement-learning benchmarks

arXiv AI

Aug 11

SCOUT: Self-Checking and Recovery-Aware Tool-Thought Agents for Ultra-Long Egocentric Video Reasoning

arXiv:2608. 07959v1 Announce Type: new Abstract: Ultra-long egocentric video understanding requires reasoning over temporally sparse evidence distributed across hours or days, challenging current multimodal models with limited context and the grounding of key video segments.

By Keyang Zhong, Kuo Wang, Peng Liu, Quanlong Zheng, Junlin Xie, Zhijia Liang, Yanhao Zhang, Guanbin Li

agents reinforcement-learning multimodal benchmarks safety

arXiv Machine Learning

Jun 9

The Hidden Bias of Process Reward Models:PRISM for Rewarding the Right Reasoning

arXiv:2606. 09078v1 Announce Type: new Abstract: Process Reward Models (PRMs) improve credit assignment for reasoning by providing step-level feedback.

By Aakriti Agrawal, Souradip Chakraborty, Armin Saghafian, Nihal Sharma, Rizal Fathony, Nam H Nguyen, C. Bayan Bruss, Amrit Singh Bedi, Furong Huang

safety

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ProcessThinker: Enhancing Multi-modal Large Language Models Reasoning via Rollout-based Process Reward

AgentProcessBench: Diagnosing Step-Level Process Quality in Tool-Using Agents

SeekJudge: A Practical Reward Framework for Reinforcement Learning in Computer-Use Agents

TRCA: Transition-wise Rubric Credit Assignment for Long-horizon LLM Agents

SCOUT: Self-Checking and Recovery-Aware Tool-Thought Agents for Ultra-Long Egocentric Video Reasoning

The Hidden Bias of Process Reward Models:PRISM for Rewarding the Right Reasoning