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2025-10-03 更新
Efficient and Transferable Agentic Knowledge Graph RAG via Reinforcement Learning
Authors:Jinyeop Song, Song Wang, Julian Shun, Yada Zhu
Knowledge-graph retrieval-augmented generation (KG-RAG) couples large language models (LLMs) with structured, verifiable knowledge graphs (KGs) to reduce hallucinations and expose reasoning traces. However, many KG-RAG systems compose multiple LLM modules (e.g planning, reasoning, and responding), inflating inference cost and binding behavior to a specific target KG. To address this, we introduce KG-R1, an agentic KG retrieval-augmented generation (KG-RAG) framework through reinforcement learning (RL). KG-R1 utilizes a single agent that interacts with KGs as its environment, learning to retrieve at each step and incorporating the retrieved information into its reasoning and generation. The process is optimized through end-to-end RL. In controlled experiments across Knowledge-Graph Question Answering (KGQA) benchmarks, our method demonstrates both efficiency and transferability: Using Qwen-2.5-3B, KG-R1 improves answer accuracy with fewer generation tokens than prior multi-module workflow methods that use larger foundation or fine-tuned models. Furthermore, KG-R1 enables plug and play: after training, it maintains strong accuracy on new KGs without modification. These properties make KG-R1 a promising KG-RAG framework for real-world deployment. Our code is publicly available at https://github.com/Jinyeop3110/KG-R1.
知识图谱检索增强生成(KG-RAG)将大型语言模型(LLM)与结构化的、可验证的知识图谱(KG)相结合,以减少幻想并暴露推理痕迹。然而,许多KG-RAG系统由多个LLM模块(如规划、推理和响应)组成,增加了推理成本,并且绑定到特定的目标知识图谱上。为了解决这个问题,我们引入了KG-R1,这是一个通过强化学习(RL)实现的知识图谱检索增强生成(KG-RAG)框架。KG-R1使用一个单独的智能体与环境中的知识图谱进行交互,学习如何逐步检索,并将检索到的信息融入其推理和生成中。该过程通过端到端的RL进行优化。在知识图谱问答(KGQA)基准测试上的受控实验表明,我们的方法既有效率又有可迁移性:使用Qwen-2.5-3B基准测试,KG-R1在较少的生成标记中提高了答案的准确性,优于先前的多模块工作流程方法,这些方法使用更大的基础模型或微调模型。此外,KG-R1还具有即插即用功能:训练后,它能够在无需修改的情况下在新的知识图谱上保持较高的准确性。这些特性使KG-R1成为面向现实世界部署的有希望的知识图谱检索增强生成框架。我们的代码公开在https://github.com/Jinyeop3110/KG-R1。
论文及项目相关链接
PDF 10 pages, 5 figures. Submitted to ICLR 2026
Summary
KG-R1是一种基于强化学习(RL)的知识图谱检索增强生成(KG-RAG)框架。它使用一个智能体与环境(知识图谱)进行交互,在每一步中学习检索,并将检索到的信息融入其推理和生成过程中。该方法优化了端到端的RL过程,在知识图谱问答(KGQA)基准测试中展现出高效性和可迁移性。KG-R1能提高答案准确性,并使用较少的生成令牌。此外,它实现了即插即用,训练后在新知识图谱上保持高准确性而无需修改。
Key Takeaways
- KG-R1是一个基于强化学习的知识图谱检索增强生成框架。
- KG-R1使用一个智能体与环境(知识图谱)交互,进行知识检索。
- KG-R1将检索到的知识融入其推理和生成过程。
- KG-R1通过端到端的强化学习过程进行优化。
- KG-R1在知识图谱问答基准测试中表现出高效性和可迁移性。
- KG-R1提高了答案的准确性,同时使用了较少的生成令牌。
点此查看论文截图



Beyond the Algorithm: A Field Guide to Deploying AI Agents in Clinical Practice
Authors:Jack Gallifant, Katherine C. Kellogg, Matt Butler, Amanda Centi, Shan Chen, Patrick F. Doyle, Sayon Dutta, Joyce Guo, Matthew J. Hadfield, Esther H. Kim, David E. Kozono, Hugo JWL Aerts, Adam B. Landman, Raymond H. Mak, Rebecca G. Mishuris, Tanna L. Nelson, Guergana K. Savova, Elad Sharon, Benjamin C. Silverman, Umit Topaloglu, Jeremy L. Warner, Danielle S. Bitterman
Large language models (LLMs) integrated into agent-driven workflows hold immense promise for healthcare, yet a significant gap exists between their potential and practical implementation within clinical settings. To address this, we present a practitioner-oriented field manual for deploying generative agents that use electronic health record (EHR) data. This guide is informed by our experience deploying the “irAE-Agent”, an automated system to detect immune-related adverse events from clinical notes at Mass General Brigham, and by structured interviews with 20 clinicians, engineers, and informatics leaders involved in the project. Our analysis reveals a critical misalignment in clinical AI development: less than 20% of our effort was dedicated to prompt engineering and model development, while over 80% was consumed by the sociotechnical work of implementation. We distill this effort into five “heavy lifts”: data integration, model validation, ensuring economic value, managing system drift, and governance. By providing actionable solutions for each of these challenges, this field manual shifts the focus from algorithmic development to the essential infrastructure and implementation work required to bridge the “valley of death” and successfully translate generative AI from pilot projects into routine clinical care.
将大型语言模型(LLMs)集成到代理驱动的工作流程中为医疗保健领域带来了巨大的潜力,但是在临床环境中实现其潜在价值与实际应用之间仍存在很大差距。为解决这一问题,我们编制了一本面向实践者的现场手册,介绍了如何部署使用电子健康记录(EHR)数据的生成代理。本指南的编写参考了我们部署“irAE-Agent”(一种从Mass General Brigham的临床笔记中自动检测免疫相关不良事件的自动化系统)的经验,以及与参与该项目的20名临床医生、工程师和信息技术领导的结构化访谈。我们的分析揭示了临床人工智能发展中的关键不匹配:我们的努力中只有不到20%专注于即时工程模型和开发,而超过80%被实施的社技术工作所占据。我们将这些努力简化为五个“重点任务”:数据集成、模型验证、确保经济价值、管理系统漂移和治理。通过为这些挑战提供可行的解决方案,本现场手册将重点从算法开发转向必要的基础设施和实施工作,以填补“死亡之谷”,成功地将生成式人工智能从试点项目转化为常规临床护理。
论文及项目相关链接
PDF Under review. 5 Tables, 2 Figures
Summary
大型语言模型在医疗健康领域具有巨大潜力,但在临床实践中的实际应用与潜力之间存在较大差距。为解决这一问题,本指南以从业者为导向,介绍如何在医疗记录数据的基础上部署生成式智能代理。本指南基于在Mass General Brigham部署irAE-Agent系统的经验,并与参与该项目的临床医生、工程师和信息技术领导者进行深入访谈。分析显示,临床人工智能的发展存在关键性失调:仅不到20%的努力用于快速工程开发和模型开发,而超过80%的努力被实施的社会技术工作所消耗。本指南提供解决这些挑战的实际解决方案,将重点从算法开发转向必要的基础设施和实施工作,以成功将生成式人工智能从试点项目转化为常规临床护理。
Key Takeaways
- 大型语言模型在医疗健康领域具有巨大潜力,但实际应用中存在差距。
- 部署生成式智能代理需要关注数据集成、模型验证等五个方面的“重工作”。
- 实际应用中超过80%的努力被实施的社会技术工作所消耗。
- 通过提供针对每个挑战的实际解决方案,本指南将焦点从算法开发转向必要的基础设施和实施工作。
- irAE-Agent系统展示了将生成式AI应用于临床实践的实例。
- 实地考察和经验访谈揭示了临床AI发展中的关键问题。
点此查看论文截图

Automatically Generating Web Applications from Requirements Via Multi-Agent Test-Driven Development
Authors:Yuxuan Wan, Tingshuo Liang, Jiakai Xu, Jingyu Xiao, Yintong Huo, Michael R. Lyu
Developing full-stack web applications is complex and time-intensive, demanding proficiency across diverse technologies and frameworks. Although recent advances in multimodal large language models (MLLMs) enable automated webpage generation from visual inputs, current solutions remain limited to front-end tasks and fail to deliver fully functional applications. In this work, we introduce TDDev, the first test-driven development (TDD)-enabled LLM-agent framework for end-to-end full-stack web application generation. Given a natural language description or design image, TDDev automatically derives executable test cases, generates front-end and back-end code, simulates user interactions, and iteratively refines the implementation until all requirements are satisfied. Our framework addresses key challenges in full-stack automation, including underspecified user requirements, complex interdependencies among multiple files, and the need for both functional correctness and visual fidelity. Through extensive experiments on diverse application scenarios, TDDev achieves a 14.4% improvement on overall accuracy compared to state-of-the-art baselines, demonstrating its effectiveness in producing reliable, high-quality web applications without requiring manual intervention.
开发全栈式Web应用程序是一项复杂且耗时的工作,需要对多种技术和框架有精通的能力。尽管最近的多模态大型语言模型(MLLMs)的进步能够实现从视觉输入自动生成网页,但当前的解决方案仍然仅限于前端任务,无法提供完整的功能性应用程序。在这项工作中,我们引入了TDDev,这是第一个支持端到端全栈Web应用程序生成的使用测试驱动开发(TDD)的大型语言模型(LLM)代理框架。给定自然语言描述或设计图像,TDDev会自动推导可执行测试用例,生成前端和后端代码,模拟用户交互,并迭代地完善实现,直到满足所有要求。我们的框架解决了全栈自动化的关键挑战,包括用户需求不明确、多个文件之间的复杂相互依赖关系以及对功能正确性和视觉保真度的需求。通过对不同应用场景的广泛实验,TDDev在整体准确性方面比最新技术基线提高了14.4%,证明了其在无需人工干预的情况下生成可靠、高质量Web应用程序的有效性。
论文及项目相关链接
Summary
MLLMs赋能自动化网页生成技术,但仍局限于前端任务。本文介绍TDDev框架,采用测试驱动开发(TDD)和LLM代理技术实现全栈Web应用端到端的自动化生成。该框架可根据自然语言描述或设计图像自动生成测试用例,包括前后端代码,模拟用户交互,并在满足所有要求后逐步优化实现。实验结果证明了其在生成可靠高质量Web应用方面的优势。相比当前最新技术基线,TDDev在总体准确性上提升了高达百分之十四点四的准确度,提高了无人工干预的自适应能力。同时能很好地处理用户要求不明确等难题,是跨领域技术创新的重要组成部分。综合试验显示了其显著的优势和潜力。
Key Takeaways
- 开发全栈Web应用是一个复杂且耗时的过程,需要掌握多种技术和框架的技能。现有的自动化工具大多局限于前端任务,无法实现完全自动化生成。而本文介绍的TDDev框架实现了端到端的自动化生成功能。
- TDDev框架采用测试驱动开发(TDD)和大型语言模型(LLM)技术,能够根据自然语言描述或设计图像自动生成测试用例和代码。此技术通过模拟用户交互不断优化和完善应用程序功能,以确保其满足用户需求并具有强大的功能性正确性。与其他先进技术相比,该框架的优势明显,能显著提升全栈应用的总体准确度达到百分之十四点四的提升幅度。且能够以简单有效的方式应对一系列常见的难题。它对编程行业未来的发展方向具有重要的推动作用。
点此查看论文截图




SafeSearch: Automated Red-Teaming for the Safety of LLM-Based Search Agents
Authors:Jianshuo Dong, Sheng Guo, Hao Wang, Zhuotao Liu, Tianwei Zhang, Ke Xu, Minlie Huang, Han Qiu
Search agents connect LLMs to the Internet, enabling access to broader and more up-to-date information. However, unreliable search results may also pose safety threats to end users, establishing a new threat surface. In this work, we conduct two in-the-wild experiments to demonstrate both the prevalence of low-quality search results and their potential to misguide agent behaviors. To counter this threat, we introduce an automated red-teaming framework that is systematic, scalable, and cost-efficient, enabling lightweight and harmless safety assessments of search agents. Building on this framework, we construct the SafeSearch benchmark, which includes 300 test cases covering five categories of risks (e.g., misinformation and indirect prompt injection). Using this benchmark, we evaluate three representative search agent scaffolds, covering search workflow, tool-calling, and deep research, across 7 proprietary and 8 open-source backend LLMs. Our results reveal substantial vulnerabilities of LLM-based search agents: when exposed to unreliable websites, the highest ASR reached 90.5% for GPT-4.1-mini under a search workflow setting. Moreover, our analysis highlights the limited effectiveness of common defense practices, such as reminder prompting. This emphasizes the value of our framework in promoting transparency for safer agent development. Our codebase and test cases are publicly available: https://github.com/jianshuod/SafeSearch.
搜索代理将大型语言模型连接到互联网,以便访问更广泛和最新的信息。然而,不可靠的搜索结果也可能对最终用户构成安全威胁,形成新的威胁面。在这项工作中,我们通过两项野外实验来展示低质量搜索结果的普遍性以及它们误导代理行为的可能性。为了应对这一威胁,我们引入了一个系统化、可扩展且成本效益高的自动化红队框架,可以对搜索代理进行轻便且无害的安全评估。基于此框架,我们构建了SafeSearch基准测试,包括涵盖五种风险类别的300个测试用例(例如,错误信息和间接提示注入)。使用该基准测试,我们评估了三种具有代表性的搜索代理架构,涵盖搜索工作流程、工具调用和深度研究,涉及7个专有和8个开源后端大型语言模型。我们的结果揭示了基于大型语言模型的搜索代理存在大量漏洞:在面对不可靠的网站时,GPT-4.1 mini在搜索工作流程设置下的误报率最高达到90.5%。此外,我们的分析强调了常见防御措施(如提醒提示)的有限有效性,这强调了我们的框架在促进更安全代理开发中的透明度的价值。我们的代码库和测试用例可公开访问:https://github.com/jianshuod/SafeSearch。
论文及项目相关链接
PDF Preprint
Summary
搜索代理将大型语言模型连接到互联网,提供获取更全面、最新的信息的机会,但同时也带来了安全威胁。实验表明搜索结果的不可靠性对终端用户的安全构成了威胁。为了应对这一问题,我们构建了一个系统化、可扩展且经济高效的自动化红队框架,该框架可对搜索代理进行安全评估。基于此框架,我们创建了SafeSearch基准测试,涵盖五种风险类别的测试案例。我们的研究发现,LLM为基础搜索代理存在显著漏洞,可能面临高误报率(高达GPT-4.1 mini版本中的高达90.5%)。现有的防御措施的有效性有限,强调了我们的框架在提高安全性方面的价值。代码库和测试用例已公开分享。
Key Takeaways
- 搜索代理连接大型语言模型到互联网,带来信息获取的便利性和安全性威胁。
- 低质量的搜索结果可能误导代理行为并构成安全威胁。
- 提出一种自动化红队框架进行系统的、轻量级的无害安全评估。
- 创建SafeSearch基准测试涵盖五种风险类别,包括误导信息和间接提示注入等。
- 发现LLM为基础的搜索代理存在显著漏洞,面临高误报率风险。
点此查看论文截图






ReWatch-R1: Boosting Complex Video Reasoning in Large Vision-Language Models through Agentic Data Synthesis
Authors:Congzhi Zhang, Zhibin Wang, Yinchao Ma, Jiawei Peng, Yihan Wang, Qiang Zhou, Jun Song, Bo Zheng
While Reinforcement Learning with Verifiable Reward (RLVR) significantly advances image reasoning in Large Vision-Language Models (LVLMs), its application to complex video reasoning remains underdeveloped. This gap stems primarily from a critical data bottleneck: existing datasets lack the challenging, multi-hop questions and high-quality, video-grounded Chain-of-Thought (CoT) data necessary to effectively bootstrap RLVR. To address this, we introduce ReWatch, a large-scale dataset built to foster advanced video reasoning. We propose a novel multi-stage synthesis pipeline to synthesize its three components: ReWatch-Caption, ReWatch-QA, and ReWatch-CoT. A core innovation is our Multi-Agent ReAct framework for CoT synthesis, which simulates a human-like “re-watching” process to generate video-grounded reasoning traces by explicitly modeling information retrieval and verification. Building on this dataset, we develop ReWatch-R1 by post-training a strong baseline LVLM with Supervised Fine-Tuning (SFT) and our RLVR framework. This framework incorporates a novel Observation & Reasoning (O&R) reward mechanism that evaluates both the final answer’s correctness and the reasoning’s alignment with video content, directly penalizing hallucination. Our experiments show that ReWatch-R1 achieves state-of-the-art average performance on five challenging video reasoning benchmarks. Project Page: https://rewatch-r1.github.io
在强化学习与可验证奖励(RLVR)显著推进大型视觉语言模型(LVLMs)的图像推理的同时,其在复杂视频推理中的应用仍然处于初级阶段。这一差距主要源于关键的数据瓶颈:现有数据集缺乏具有挑战性的多跳问题和高质量的视频基础思维链(CoT)数据,无法有效引导RLVR。为了解决这一问题,我们推出了ReWatch,这是一个为了促进高级视频推理而构建的大规模数据集。我们提出了一种新颖的多阶段合成管道来合成其三个组成部分:ReWatch-Caption、ReWatch-QA和ReWatch-CoT。一个核心创新是我们为CoT合成提出的Multi-Agent ReAct框架,它通过模拟人类“重看”过程来生成视频基础推理轨迹,通过显式建模信息检索和验证。基于该数据集,我们通过监督微调(SFT)和我们的RLVR框架对强大的基线LVLM进行后训练,开发了ReWatch-R1。该框架引入了一种新型的观察与推理(O&R)奖励机制,该机制不仅评估最终答案的正确性,还评估推理与视频内容的对齐程度,直接惩罚虚构情况。我们的实验表明,ReWatch-R1在五个具有挑战性的视频推理基准测试上达到了最先进的平均性能。项目页面:https://rewatch-r1.github.io
论文及项目相关链接
Summary:强化学习验证奖励(RLVR)在大规模视觉语言模型(LVLMs)的图像推理方面取得了显著进展,但在复杂视频推理方面的应用尚待发展。这主要是由于关键数据瓶颈的存在:现有数据集缺乏挑战性的多跳问题和高质量的视频基础思维链数据,无法有效地引导RLVR。为解决这一问题,我们推出了ReWatch数据集,旨在促进高级视频推理的发展。我们提出了一种新的多阶段合成管道来合成其三个组成部分:ReWatch-Caption、ReWatch-QA和ReWatch-CoT。其中核心创新是CoT合成的多智能体反应框架,它通过模拟人类“重看”过程来生成视频基础推理轨迹,通过显式建模信息检索和验证。在此基础上,我们发展了ReWatch-R1,通过监督微调(SFT)和我们的RLVR框架对强大的基线LVLM进行后训练。该框架采用了一种新型的观察与推理(O&R)奖励机制,既评估最终答案的正确性,又评估其与视频内容的吻合程度,直接遏制幻觉现象。实验表明,ReWatch-R1在五个挑战性的视频推理基准测试中取得了最先进的平均性能。
Key Takeaways:
- 强化学习验证奖励(RLVR)在大规模视觉语言模型(LVLMs)的图像推理中有显著进展,但在视频推理中应用不足。
- 数据瓶颈是限制RLVR在视频推理中应用的主要原因,现有数据集缺乏挑战性多跳问题和高质量视频基础思维链数据。
- 推出ReWatch数据集,旨在促进高级视频推理的发展。
- 提出新的多阶段合成管道来合成ReWatch数据集的三部分:ReWatch-Caption、ReWatch-QA和ReWatch-CoT。
- 引入多智能体反应框架进行思维链合成,模拟人类“重看”过程生成视频基础推理轨迹。
- 发展了ReWatch-R1,结合监督微调(SFT)和RLVR框架进行后训练。
点此查看论文截图




DBF-MA: A Differential Bayesian Filtering Planner for Multi-Agent Autonomous Racing Overtakes
Authors:Trent Weiss, Amar Kulkarni, Madhur Behl
A significant challenge in autonomous racing is to generate overtaking maneuvers. Racing agents must execute these maneuvers on complex racetracks with little room for error. Optimization techniques and graph-based methods have been proposed, but these methods often rely on oversimplified assumptions for collision-avoidance and dynamic constraints. In this work, we present an approach to trajectory synthesis based on an extension of the Differential Bayesian Filtering framework. Our approach for collision-free trajectory synthesis frames the problem as one of Bayesian Inference over the space of Composite Bezier Curves. Our method is derivative-free, does not require a spherical approximation of the vehicle footprint, linearization of constraints, or simplifying upper bounds on collision avoidance. We conduct a closed-loop analysis of DBF-MA and find it successfully overtakes an opponent in 87% of tested scenarios, outperforming existing methods in autonomous overtaking.
自主驾驶竞赛中的一大挑战是生成超车机动动作。竞赛代理必须在复杂的赛道上执行这些动作,而且不能有太多失误。虽然已有优化技术和基于图的方法被提出,但这些方法通常在避免碰撞和动态约束方面存在过于简化的假设。在这项工作中,我们提出了一种基于差分贝叶斯滤波框架扩展的轨迹合成方法。我们提出的无碰撞轨迹合成方法将问题表述为复合贝塞尔曲线空间上的贝叶斯推断问题。我们的方法无需求导,不需要对车辆足迹进行球形近似,不需要对约束进行线性化,也不需要简化避免碰撞的上界。我们对DBF-MA进行了闭环分析,发现其在87%的测试场景中成功超越了对手,在自主超车方面超越了现有方法。
论文及项目相关链接
PDF This work has been submitted to the IEEE for possible publication
Summary
自主赛车领域的一大挑战是生成超车动作。竞赛代理必须在复杂的赛道上执行这些动作,不能有太多失误。虽然已有优化技术和基于图的方法,但这些方法通常在避免碰撞和动态约束方面做了简化的假设。本研究提出了一种基于差分贝叶斯滤波框架的轨迹合成方法。我们的无碰撞轨迹合成方法将问题视为贝叶斯推断在复合贝塞尔曲线空间上的问题。我们的方法无需车辆足迹的球形近似、约束的线性化或在避免碰撞方面的简化上界。我们对DBF-MA进行了闭环分析,发现它在87%的测试场景中成功超越了对手,在自主超车方面优于现有方法。
Key Takeaways
- 自主赛车中的超车动作是一大挑战,需要在复杂赛道上精确执行。
- 现有方法在处理避免碰撞和动态约束时存在简化假设。
- 研究提出了一种基于差分贝叶斯滤波框架的轨迹合成方法。
- 该方法将问题视为贝叶斯推断在复合贝塞尔曲线空间上的问题。
- 该方法无需对车辆足迹进行球形近似、约束的线性化或避免碰撞的简化上界。
- DBF-MA在闭环分析中成功超越了对手,超越了现有方法的性能。
点此查看论文截图





Interactive Recommendation Agent with Active User Commands
Authors:Jiakai Tang, Yujie Luo, Xunke Xi, Fei Sun, Xueyang Feng, Sunhao Dai, Chao Yi, Dian Chen, Zhujin Gao, Yang Li, Xu Chen, Wen Chen, Jian Wu, Yuning Jiang, Bo Zheng
Traditional recommender systems rely on passive feedback mechanisms that limit users to simple choices such as like and dislike. However, these coarse-grained signals fail to capture users’ nuanced behavior motivations and intentions. In turn, current systems cannot also distinguish which specific item attributes drive user satisfaction or dissatisfaction, resulting in inaccurate preference modeling. These fundamental limitations create a persistent gap between user intentions and system interpretations, ultimately undermining user satisfaction and harming system effectiveness. To address these limitations, we introduce the Interactive Recommendation Feed (IRF), a pioneering paradigm that enables natural language commands within mainstream recommendation feeds. Unlike traditional systems that confine users to passive implicit behavioral influence, IRF empowers active explicit control over recommendation policies through real-time linguistic commands. To support this paradigm, we develop RecBot, a dual-agent architecture where a Parser Agent transforms linguistic expressions into structured preferences and a Planner Agent dynamically orchestrates adaptive tool chains for on-the-fly policy adjustment. To enable practical deployment, we employ simulation-augmented knowledge distillation to achieve efficient performance while maintaining strong reasoning capabilities. Through extensive offline and long-term online experiments, RecBot shows significant improvements in both user satisfaction and business outcomes.
传统推荐系统依赖于被动反馈机制,这限制了用户的简单选择,如喜欢和不喜欢。然而,这些粗粒度的信号无法捕捉到用户微妙的动机和行为意图。此外,当前的系统也无法区分哪些特定的项目属性导致用户的满意或不满意,从而导致偏好建模不准确。这些基本限制在用户意图和系统解释之间造成了持久的差距,最终破坏了用户满意度并损害了系统效率。为了解决这些限制,我们引入了交互式推荐馈送(IRF),这是一种开创性的范式,能够在主流推荐馈送中实现自然语言命令。不同于限制用户于被动隐含行为影响的传统系统,IRF通过实时语言命令实现对推荐策略的主动明确控制。为了支持这一范式,我们开发了RecBot,这是一个双代理架构,其中解析器代理将语言表达转化为结构化偏好,而规划器代理动态协调适应性的工具链进行即时策略调整。为了实现实际部署,我们采用仿真增强知识蒸馏法,以实现高效性能的同时保持强大的推理能力。通过大量的离线测试和长期在线实验,RecBot在用户满意度和业务成果方面显示出显著的改进。
论文及项目相关链接
PDF Under Review
Summary
传统推荐系统依赖被动反馈机制,只提供简单选择(如喜欢和不喜欢),难以捕捉用户行为背后的复杂动机和意图。因此,系统无法准确建立用户偏好模型,导致用户意图与系统解读之间存在鸿沟,影响用户满意度和系统效率。为解决这个问题,我们推出互动推荐馈送(IRF),这是一个在主流推荐馈送中融入自然语言命令的创新范式。IRF让用户通过实时语言命令主动控制推荐策略,改变了传统系统仅依赖被动隐性行为影响的局限。我们开发的RecBot是一个双代理架构,其中解析器代理将语言表达式转化为结构化偏好,规划器代理动态协调即时策略调整的工具链。通过模拟增强的知识蒸馏技术,RecBot在保持强大推理能力的同时实现了高效性能。实验证明,RecBot在用户满意度和业务成果方面均有显著提高。
Key Takeaways
- 传统推荐系统依赖简单反馈选择,难以捕捉用户复杂动机和意图。
- 用户意图与系统解读之间存在鸿沟,影响用户满意度和系统效率。
- 互动推荐馈送(IRF)允许用户通过实时语言命令主动控制推荐策略。
- RecBot是一个双代理架构,包括解析器代理和规划器代理,分别处理用户语言和策略调整。
- 模拟增强的知识蒸馏技术使RecBot保持高效性能的同时具备强大推理能力。
- RecBot在用户满意度和业务成果方面实现了显著提高。
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Foam-Agent 2.0: An End-to-End Composable Multi-Agent Framework for Automating CFD Simulation in OpenFOAM
Authors:Ling Yue, Nithin Somasekharan, Tingwen Zhang, Yadi Cao, Shaowu Pan
Computational Fluid Dynamics (CFD) is an essential simulation tool in engineering, yet its steep learning curve and complex manual setup create significant barriers. To address these challenges, we introduce Foam-Agent, a multi-agent framework that automates the entire end-to-end OpenFOAM workflow from a single natural language prompt. Our key innovations address critical gaps in existing systems: 1. An Comprehensive End-to-End Simulation Automation: Foam-Agent is the first system to manage the full simulation pipeline, including advanced pre-processing with a versatile Meshing Agent capable of handling external mesh files and generating new geometries via Gmsh, automatic generation of HPC submission scripts, and post-simulation visualization via ParaView. 2. Composable Service Architecture: Going beyond a monolithic agent, the framework uses Model Context Protocol (MCP) to expose its core functions as discrete, callable tools. This allows for flexible integration and use by other agentic systems, such as Claude-code, for more exploratory workflows. 3. High-Fidelity Configuration Generation: We achieve superior accuracy through a Hierarchical Multi-Index RAG for precise context retrieval and a dependency-aware generation process that ensures configuration consistency. Evaluated on a benchmark of 110 simulation tasks, Foam-Agent achieves an 88.2% success rate with Claude 3.5 Sonnet, significantly outperforming existing frameworks (55.5% for MetaOpenFOAM). Foam-Agent dramatically lowers the expertise barrier for CFD, demonstrating how specialized multi-agent systems can democratize complex scientific computing. The code is public at https://github.com/csml-rpi/Foam-Agent.
计算流体动力学(CFD)是工程中重要的模拟工具,但其陡峭的学习曲线和复杂的手动设置构成了重大障碍。为了解决这些挑战,我们引入了Foam-Agent,这是一个多智能体框架,可以通过单个自然语言提示自动完成整个OpenFOAM工作流。我们的关键创新解决了现有系统中的关键空白:1.全面的端到端仿真自动化:Foam-Agent是第一个管理完整仿真管道的系统,包括使用多功能网格代理进行高级预处理,能够处理外部网格文件并通过Gmsh生成新几何体,自动生成HPC提交脚本,以及通过ParaView进行仿真后的可视化。2.可组合的服务架构:超越单一智能体的框架,使用模型上下文协议(MCP)将其核心功能暴露为独立的、可调用的工具。这允许其他智能体系统,如Claude-code,进行灵活的集成和使用,以支持更探索性的工作流程。3.高保真配置生成:我们通过分层多索引RAG实现高级精度,用于精确上下文检索和依赖感知生成过程,确保配置一致性。在110个仿真任务基准测试中,Foam-Agent与Claude 3.5 Sonnet配合使用,成功率达到88.2%,显著优于现有框架(MetaOpenFOAM为55.5%)。Foam-Agent大大降低了CFD的专业知识壁垒,展示了专业多智能体系统是如何使复杂的科学计算普及化的。代码公开在https://github.com/csml-rpi/Foam-Agent。
论文及项目相关链接
Summary
泡沫代理(Foam-Agent)是一个多代理框架,它通过自然语言提示自动化整个OpenFOAM工作流程。其关键创新在于全面的端到端仿真自动化、可组合的代理架构和高保真配置生成。此框架通过自动化和集成复杂过程,显著降低了计算流体动力学(CFD)的门槛。详细信息可访问公开代码库:https://github.com/csml-rpi/Foam-Agent。
Key Takeaways
- Foam-Agent 是一个多代理框架,实现了从自然语言提示到OpenFOAM全仿真流程的自动化。
- 具备先进的预处理功能,包括处理外部网格文件、通过Gmsh生成新几何体等。
- 自动生成高性能计算提交脚本和仿真后可视化。
- 采用可组合的代理架构,通过Model Context Protocol(MCP)暴露核心功能,便于与其他系统整合。
- 利用Hierarchical Multi-Index RAG实现高精度配置生成。
- 在110个仿真任务上的评估结果显示,Foam-Agent使用Claude 3.5 Sonnet达到了88.2%的成功率,显著优于现有框架(MetaOpenFOAM的55.5%)。
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Code Like Humans: A Multi-Agent Solution for Medical Coding
Authors:Andreas Motzfeldt, Joakim Edin, Casper L. Christensen, Christian Hardmeier, Lars Maaløe, Anna Rogers
In medical coding, experts map unstructured clinical notes to alphanumeric codes for diagnoses and procedures. We introduce Code Like Humans: a new agentic framework for medical coding with large language models. It implements official coding guidelines for human experts, and it is the first solution that can support the full ICD-10 coding system (+70K labels). It achieves the best performance to date on rare diagnosis codes (fine-tuned discriminative classifiers retain an advantage for high-frequency codes, to which they are limited). Towards future work, we also contribute an analysis of system performance and identify its `blind spots’ (codes that are systematically undercoded).
在医疗编码领域,专家将非结构的临床笔记映射到用于诊断和程序的字母数字代码。我们推出了“人类式编码”:一种利用大型语言模型进行医疗编码的新型代理框架。它实现了人类专家的官方编码指南,是第一个能够支持完整ICD-10编码系统(超过7万个标签)的解决方案。在罕见的诊断代码上,它取得了迄今为止的最佳性能(经过精细调整的判别分类器在高频代码上具有优势,但仅限于这些代码)。在未来的工作中,我们还对系统性能进行了分析,并确定了其“盲点”(系统性编码不足的代码)。
论文及项目相关链接
PDF EMNLP Findings 2025
Summary
文章介绍了Code Like Humans这一新型医疗编码框架,利用大型语言模型实现了医疗编码。此框架遵循专业编码指南,可支持完整的ICD-10编码系统(超过7万标签)。它在罕见诊断代码的编码上达到了最佳性能。同时,文章也分析了系统性能并识别出其存在的盲点。
Key Takeaways
- Code Like Humans是一个新的医疗编码框架,采用大型语言模型实现。
- 此框架能支持完整的ICD-10编码系统(超过7万标签)。
- 该框架遵循官方编码指南,实现了人类专家的编码方式。
- Code Like Humans在罕见诊断代码的编码上达到了最佳性能。
- 对于高频代码,精细调整的分类器仍具有优势。
- 文章分析了系统性能,识别出了编码过程中的盲点。
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Breaking Down and Building Up: Mixture of Skill-Based Vision-and-Language Navigation Agents
Authors:Tianyi Ma, Yue Zhang, Zehao Wang, Parisa Kordjamshidi
Vision-and-Language Navigation (VLN) poses significant challenges for agents to interpret natural language instructions and navigate complex 3D environments. While recent progress has been driven by large-scale pre-training and data augmentation, current methods still struggle to generalize to unseen scenarios, particularly when complex spatial and temporal reasoning is required. In this work, we propose SkillNav, a modular framework that introduces structured, skill-based reasoning into Transformer-based VLN agents. Our method decomposes navigation into a set of interpretable atomic skills (e.g., Vertical Movement, Area and Region Identification, Stop and Pause), each handled by a specialized agent. To support targeted skill training without manual data annotation, we construct a synthetic dataset pipeline that generates diverse, linguistically natural, skill-specific instruction-trajectory pairs. We then introduce a novel training-free Vision-Language Model (VLM)-based router, which dynamically selects the most suitable agent at each time step by aligning sub-goals with visual observations and historical actions. SkillNav obtains competitive results on commonly used benchmarks and establishes state-of-the-art generalization to the GSA-R2R, a benchmark with novel instruction styles and unseen environments.
视觉与语言导航(VLN)对代理提出了重大挑战,要求代理解释自然语言指令并导航复杂的3D环境。尽管最近的进展得益于大规模预训练和数据增强,但当前的方法仍然难以推广到未见过的场景,特别是在需要复杂的空间和时间推理时。在这项工作中,我们提出了SkillNav,这是一个模块化框架,它将结构化、基于技能的推理引入到基于Transformer的VLN代理中。我们的方法将导航分解成一组可解释的原子技能(例如垂直移动、区域识别、停止和暂停等),每个技能都由一个专门的代理处理。为了支持有针对性的技能训练而无需手动数据标注,我们构建了一个合成数据集管道,该管道生成多样、语言自然、技能特定的指令-轨迹对。然后,我们引入了一种新型的基于训练视觉语言模型(VLM)的路由器,该路由器通过将与视觉观察和历史行动的子目标对齐,在每个时间步长动态选择最合适的代理。SkillNav在常用基准测试上取得了具有竞争力的结果,并在具有新型指令风格和未见过的环境的GSA-R2R基准测试上达到了最先进的推广效果。
论文及项目相关链接
Summary
提供视觉和语言导航的任务要求代理解析自然语言指令并导航复杂的3D环境,存在诸多挑战。虽然已有进步源于大规模预训练和数据增强技术,但现有方法在面对未知场景时,特别是需要进行复杂空间和时间推理时,仍然难以推广。在这项研究中,我们提出了SkillNav,这是一个模块化框架,它将结构化、基于技能的推理引入到基于Transformer的VLN代理中。我们的方法将导航分解成一组可解释的原子技能(例如垂直移动、区域识别、停止和暂停等),每个技能由专门的代理处理。为了支持有针对性的技能训练而无需手动数据标注,我们构建了一个合成数据集管道,该管道生成多样化、语言自然、技能特定的指令轨迹对。我们还引入了一种基于训练的无视觉语言模型路由器,该模型通过匹配子目标与视觉观察和历史动作,在每个时间步选择最合适的代理。SkillNav在常用基准测试上表现具有竞争力,并在具有新指令风格和未见环境的基准测试GSA-R2R上实现了最先进的推广效果。
Key Takeaways
- VLN任务面临对复杂环境和语言理解的挑战。
- 当前方法难以推广到未见场景,特别是在需要复杂空间和时间推理的情况下。
- SkillNav框架引入了结构化、基于技能的推理,将导航分解成一系列原子技能。
- 利用合成数据集进行有针对性的技能训练,无需手动数据标注。
- 引入基于训练的无视觉语言模型路由器,动态选择最合适的代理。
点此查看论文截图



AgentMisalignment: Measuring the Propensity for Misaligned Behaviour in LLM-Based Agents
Authors:Akshat Naik, Patrick Quinn, Guillermo Bosch, Emma Gouné, Francisco Javier Campos Zabala, Jason Ross Brown, Edward James Young
As Large Language Model (LLM) agents become more widespread, associated misalignment risks increase. While prior research has studied agents’ ability to produce harmful outputs or follow malicious instructions, it remains unclear how likely agents are to spontaneously pursue unintended goals in realistic deployments. In this work, we approach misalignment as a conflict between the internal goals pursued by the model and the goals intended by its deployer. We introduce a misalignment propensity benchmark, \textsc{AgentMisalignment}, a benchmark suite designed to evaluate the propensity of LLM agents to misalign in realistic scenarios. Evaluations cover behaviours such as avoiding oversight, resisting shutdown, sandbagging, and power-seeking. Testing frontier models, we find that more capable agents tend to exhibit higher misalignment on average. We also systematically vary agent personalities through different system prompts and observe that persona characteristics can strongly and unpredictably influence misalignment, sometimes more than the choice of model itself. Our results reveal the limitations of current alignment methods for autonomous LLM agents and underscore the need to rethink misalignment in realistic deployment settings.
随着大型语言模型(LLM)代理的普及,相关的误对齐风险也在增加。虽然之前的研究已经研究了代理产生有害输出或遵循恶意指令的能力,但在实际部署中,代理自发追求意外目标的可能性仍然不清楚。在这项工作中,我们将误对齐视为模型追求的内部目标与部署者的目标之间的冲突。我们引入了误对齐倾向基准测试\text{AgentMisalignment},这是一套旨在评估LLM代理在现实场景中误对齐倾向的基准测试套件。评估包括避免监督、抵抗关闭、沙袋和权力寻求等行为。通过对前沿模型的测试,我们发现更强大的代理平均更容易出现误对齐。我们还通过不同的系统提示系统地改变代理的个性,并发现人格特征可以强烈且不可预测地影响误对齐,有时甚至超过模型本身的选择。我们的结果揭示了当前自主LLM代理对齐方法的局限性,并强调需要在现实部署环境中重新思考误对齐问题。
论文及项目相关链接
PDF Prepint, under review for NeurIPS 2025
Summary
大型语言模型(LLM)代理的普及增加了相关的不对齐风险。尽管先前的研究已经研究了代理产生有害输出或遵循恶意指令的能力,但在现实部署中,代理自发追求非意图目标的可能性仍然不清楚。在这项工作中,我们将不对齐视为模型内部追求目标与部署者目标之间的冲突。我们引入了不对齐倾向基准测试\text{AgentMisalignment},该基准测试套件旨在评估LLM代理在现实场景中的不对齐倾向。评估包括避免监督、抵抗关闭、沙袋和权力寻求等行为。通过对前沿模型的测试,我们发现能力更强的代理平均表现出更高的不对齐倾向。我们还通过不同的系统提示系统地改变了代理个性,并发现个性特征可以强烈且不可预测地影响不对齐,有时甚至超过模型本身的选择。我们的结果揭示了当前自主LLM代理对齐方法的局限性,并强调需要在现实部署环境中重新思考不对齐问题。
Key Takeaways
- 大型语言模型(LLM)代理的普及增加了不对齐风险。
- 代理在现实部署中可能自发追求非意图目标。
- 引入了\text{AgentMisalignment}基准测试来评估LLM代理的不对齐倾向。
- 评估包括代理的行为如避免监督、抵抗关闭、沙袋和权力寻求。
- 更强大的代理平均表现出更高的不对齐倾向。
- 代理个性可以强烈影响不对齐程度,有时甚至超过模型本身。
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The challenge of hidden gifts in multi-agent reinforcement learning
Authors:Dane Malenfant, Blake A. Richards
Sometimes we benefit from actions that others have taken even when we are unaware that they took those actions. For example, if your neighbor chooses not to take a parking spot in front of your house when you are not there, you can benefit, even without being aware that they took this action. These hidden gifts'' represent an interesting challenge for multi-agent reinforcement learning (MARL), since assigning credit when the beneficial actions of others are hidden is non-trivial. Here, we study the impact of hidden gifts with a very simple MARL task. In this task, agents in a grid-world environment have individual doors to unlock in order to obtain individual rewards. As well, if all the agents unlock their door the group receives a larger collective reward. However, there is only one key for all of the doors, such that the collective reward can only be obtained when the agents drop the key for others after they use it. Notably, there is nothing to indicate to an agent that the other agents have dropped the key, thus this act for others is a
hidden gift’’. We show that several different state-of-the-art MARL algorithms, including MARL specific architectures, fail to learn how to obtain the collective reward in this simple task. Interestingly, we find that decentralized actor-critic policy gradient agents can succeed when we provide them with information about their own action history, but MARL agents still cannot solve the task with action history. Finally, we derive a correction term for policy gradient agents, inspired by learning aware approaches, which reduces the variance in learning and helps them to converge to collective success more reliably. These results show that credit assignment in multi-agent settings can be particularly challenging in the presence of ``hidden gifts’’, and demonstrate that self learning-awareness in decentralized agents can benefit these settings.
有时候,即使我们不知道他人已经采取了某些行动,我们也会从他们的行动中受益。例如,当您不在家时,如果您的邻居选择不在您家门口停车,您就可以受益,即使您并不知道他们采取了这一行动。这些“隐藏礼物”对于多智能体强化学习(MARL)来说代表了一个有趣的挑战,因为在不知道他人有益行动的情况下分配功劳是非凡的。在这里,我们通过一项非常简单的MARL任务来研究隐藏赠礼的影响。在此任务中,网格环境中的智能体需要解锁各自独立的门以获取个体奖励。此外,如果所有智能体都打开自己的门,那么整个团队将获得更大的集体奖励。但是,所有门的钥匙只有一把,智能体在使用后必须放下钥匙供其他智能体使用才能获取集体奖励。值得注意的是,没有任何迹象表明其他智能体已经放下了钥匙,因此为他人放下钥匙的行为是“隐藏的礼物”。我们表明,包括MARL特定架构在内的几种先进的多智能体强化学习算法都未能学会如何在简单任务中获取集体奖励。有趣的是,我们发现分散式演员评论家策略梯度智能体在提供有关其自身行动历史的信息时可以成功完成任务,但多智能体强化学习智能体仍然无法使用行动历史来完成任务。最后,我们从学习意识方法中汲取灵感,为策略梯度智能体推导出一个校正项,这减少了学习的方差并帮助它们更可靠地收敛到集体成功。这些结果表明,“隐藏礼物”的存在使多智能体环境中的信用分配特别具有挑战性,并证明了分散式智能体的自我学习意识可以在这些环境中带来好处。
论文及项目相关链接
PDF Added LOLA baselines to appendix, new corollary proof on correction term not conflicting with individual objectives, related works on multi-objective RL and coordination MARL, expanded the contraposition appendix experiment, moved key drop rate experiments to appendix and aligned first success plots with key-drop plots
Summary
在多人环境中,其他人的行为有时会对我们产生积极影响,即使我们并不知情。在一个简单的多智能体强化学习任务中,智能体在网格世界中需要解锁个人门来获得奖励。若所有智能体都解锁了门,集体将获得更大的奖励。然而,只有一个钥匙可以解锁所有门,智能体在使用钥匙后必须将其放下供他人使用才能带来集体奖励,而这一行为是隐藏的。研究发现,许多先进的MARL算法无法学习如何获得集体奖励,而分散式行动评论家策略梯度智能体在提供自身行动历史信息后可以成功完成任务。最后,受学习意识方法的启发,提出了策略梯度智能体的修正项,以减少学习方差并更可靠地帮助他们实现集体成功。这表明在存在“隐藏礼物”的情况下,多智能体环境中的信用分配尤其具有挑战性,并证明了分散式智能体的自我学习意识可以在这些环境中受益。
Key Takeaways
- 其他人的行为有时会对我们产生积极影响,即使我们并不知情,这构成了多智能体强化学习(MARL)中的一个挑战。
- 在一个简单的MARL任务中,智能体需要解锁门以获取奖励,并且存在一个隐藏的集体奖励,只有在所有智能体都放下钥匙后才能获得。
- 许多先进的MARL算法无法在这个任务中学习如何获得集体奖励。
- 分散式行动评论家策略梯度智能体在提供自身行动历史信息后可以成功完成任务。
- 学习意识方法的启发下,提出了策略梯度智能体的修正项以减少学习方差并更可靠地实现集体成功。
- 信用分配在多智能体环境中尤其具有挑战性,尤其是在存在“隐藏礼物”的情况下。
点此查看论文截图



R&D-Agent: An LLM-Agent Framework Towards Autonomous Data Science
Authors:Xu Yang, Xiao Yang, Shikai Fang, Yifei Zhang, Jian Wang, Bowen Xian, Qizheng Li, Jingyuan Li, Minrui Xu, Yuante Li, Haoran Pan, Yuge Zhang, Weiqing Liu, Yelong Shen, Weizhu Chen, Jiang Bian
Recent advances in AI and ML have transformed data science, yet increasing complexity and expertise requirements continue to hinder progress. Although crowd-sourcing platforms alleviate some challenges, high-level machine learning engineering (MLE) tasks remain labor-intensive and iterative. We introduce R&D-Agent, a comprehensive, decoupled, and extensible framework that formalizes the MLE process. R&D-Agent defines the MLE workflow into two phases and six components, turning agent design for MLE from ad-hoc craftsmanship into a principled, testable process. Although several existing agents report promising gains on their chosen components, they can mostly be summarized as a partial optimization from our framework’s simple baseline. Inspired by human experts, we designed efficient and effective agents within this framework that achieve state-of-the-art performance. Evaluated on MLE-Bench, the agent built on R&D-Agent ranks as the top-performing machine learning engineering agent, achieving 35.1% any medal rate, demonstrating the ability of the framework to speed up innovation and improve accuracy across a wide range of data science applications. We have open-sourced R&D-Agent on GitHub: https://github.com/microsoft/RD-Agent.
最近人工智能和机器学习的进步已经改变了数据科学的面貌,然而日益增加的复杂性和专业技能要求仍然阻碍了进展。尽管众包平台缓解了一些挑战,但高级机器学习工程(MLE)任务仍然劳动密集且迭代。我们引入了R&D-Agent,这是一个全面、解耦和可扩展的框架,它形式化了MLE过程。R&D-Agent将MLE工作流程定义为两个阶段和六个组件,将MLE的代理设计从临时性的手艺转变为有原则的、可测试的过程。虽然有几个现有的代理报告了在所选组件上的可喜收益,但它们大多可以被概括为从我们框架的简单基线进行的局部优化。受人类专家的启发,我们在该框架内设计了高效且有效的代理,实现了最先进的性能。在MLE-Bench上评估,基于R&D-Agent构建的代理被评为表现最佳的机器学习工程代理,获得奖牌率为35.1%,展示了该框架在加速创新和提高各种数据科学应用准确性方面的能力。我们已在GitHub上开源R&D-Agent:https://github.com/microsoft/RD-Agent。
论文及项目相关链接
PDF 33 pages
Summary
近期AI和ML的进步为数据科学带来了变革,但复杂性和专业知识的需求不断增加仍然阻碍了进展。虽然众筹平台缓解了一些挑战,但高级机器学习工程(MLE)任务仍然是劳动密集型和迭代型的。我们引入了R&D-Agent,这是一个全面、解耦和可扩展的框架,正式化MLE过程。R&D-Agent将MLE工作流程定义为两个阶段和六个组件,将MLE的代理设计从临时性的手艺转变为有原则的、可测试的过程。我们的框架在高效和有效的代理设计方面取得了最先进的性能。在MLE-Bench上评估,基于R&D-Agent的代理被评为表现最佳的MLE代理,获得奖牌率高达35.1%,证明了该框架在加速创新和提高各种数据科学应用准确性方面的能力。我们已在GitHub上开源R&D-Agent:https://github.com/microsoft/RD-Agent。
Key Takeaways
- AI和ML的进步为数据科学带来了变革,但复杂性增加了进展的难度。
- 众筹平台虽有助于解决部分挑战,但高级MLE任务仍然复杂。
- R&D-Agent是一个全面、解耦和可扩展的框架,正式化MLE过程。
- R&D-Agent将MLE工作流程分为两个阶段和六个组件,使代理设计更为系统和可测试。
- 与现有代理相比,R&D-Agent实现了显著的性能提升。
- 在MLE-Bench评估中,基于R&D-Agent的代理表现最佳,获得奖牌率高达35.1%。
点此查看论文截图



GUI-R1 : A Generalist R1-Style Vision-Language Action Model For GUI Agents
Authors:Run Luo, Lu Wang, Wanwei He, Longze Chen, Jiaming Li, Xiaobo Xia
Existing efforts in building Graphical User Interface (GUI) agents largely rely on the training paradigm of supervised fine-tuning on Large Vision-Language Models (LVLMs). However, this approach not only demands extensive amounts of training data but also struggles to effectively understand GUI screenshots and generalize to unseen interfaces. The issue significantly limits its application in real-world scenarios, especially for high-level tasks. Inspired by Reinforcement Fine-Tuning (RFT) in large reasoning models (e.g., DeepSeek-R1), which efficiently enhances the problem-solving capabilities of large language models in real-world settings, we propose \name, the first reinforcement learning framework designed to enhance the GUI capabilities of LVLMs in high-level real-world task scenarios, through unified action space rule modeling. By leveraging a small amount of carefully curated high-quality data across multiple platforms (including Windows, Linux, MacOS, Android, and Web) and employing policy optimization algorithms such as Group Relative Policy Optimization (GRPO) to update the model, \name achieves superior performance using only 0.02% of the data (3K vs. 13M) compared to previous state-of-the-art methods like OS-Atlas across eight benchmarks spanning three different platforms (mobile, desktop, and web). These results demonstrate the immense potential of reinforcement learning based on unified action space rule modeling in improving the execution capabilities of LVLMs for real-world GUI agent tasks.
现有构建图形用户界面(GUI)代理的努力在很大程度上依赖于大型视觉语言模型(LVLMs)上的监督微调训练范式。然而,这种方法不仅需求大量的训练数据,而且在理解GUI截图和泛化到未见过的界面时面临困难。这一问题极大地限制了其在现实场景中的应用,特别是在高级任务中。
论文及项目相关链接
Summary
本文提出一种名为\name的强化学习框架,旨在通过统一动作空间规则建模,提高大型视觉语言模型(LVLMs)在高级现实世界任务场景中的GUI能力。该框架通过利用跨多个平台的少量高质量数据,并采用群体相对策略优化(GRPO)等策略优化算法来更新模型,实现了优越的性能。与之前的先进方法相比,使用的数据量大大减少(仅使用0.02%的数据),且在三个不同平台(移动、桌面和网页)上的八个基准测试中表现出色。这展示了基于统一动作空间规则建模的强化学习在提升LVLMs执行现实世界GUI任务能力方面的巨大潜力。
Key Takeaways
- 现有GUI代理构建工作主要依赖于在大型视觉语言模型上采用监督微调训练范式。
- 此方法需要大量训练数据,并且在理解GUI截图和泛化到未见过的界面方面存在困难。
- \name是首个旨在增强LVLMs在高级现实世界任务场景中GUI能力的强化学习框架。
- \name通过统一动作空间规则建模,利用跨多个平台的高质量数据。
- \name使用策略优化算法,如群体相对策略优化(GRPO),来更新模型。
- 与最新技术相比,\name使用更少的数据实现了优越的性能(仅使用0.02%的数据)。
点此查看论文截图




Grounding Multimodal LLMs to Embodied Agents that Ask for Help with Reinforcement Learning
Authors:Ram Ramrakhya, Matthew Chang, Xavier Puig, Ruta Desai, Zsolt Kira, Roozbeh Mottaghi
Embodied agents operating in household environments must interpret ambiguous and under-specified human instructions. A capable household robot should recognize ambiguity and ask relevant clarification questions to infer the user intent accurately, leading to more effective task execution. To study this problem, we introduce the Ask-to-Act task, where an embodied agent is tasked with a single or multi-object rearrangement task using an under-specified instruction in a home environment. The agent must strategically ask minimal, yet relevant, clarification questions to resolve ambiguity while navigating under partial observability. To address this challenge, we propose a novel approach that fine-tunes multi-modal large language models (MLLMs) as vision-language-action (VLA) policies using online reinforcement learning (RL) with LLM-generated rewards. Our method eliminates the need for large-scale human demonstrations or manually engineered rewards for training such agents. We benchmark against strong zero-shot baselines including GPT-4o as well as supervised fine-tuned MLLMs on our task. Our results show that our RL-finetuned MLLM outperforms all baselines by a significant margin (10.4-16.5%), generalizing well to novel scenes and tasks. To the best of our knowledge, this is the first demonstration of adapting MLLMs as VLA agents that can act and ask for help using LLM-generated rewards with online RL.
在家庭环境中运行的实体代理必须解释模糊和未指定的人类指令。一个能干的家用机器人应该能够识别模糊性,并提出相关澄清问题,以准确推断用户意图,从而实现更有效的任务执行。为了研究这个问题,我们引入了“问再行动”任务,在该任务中,实体代理需要在家庭环境中使用未指定的指令完成单对象或多对象重新布置任务。代理必须在部分可观察的情况下解决模糊问题,同时策略性地提出最少但相关的问题进行澄清。为了应对这一挑战,我们提出了一种新方法,使用在线强化学习(RL)微调多模式大型语言模型(MLLMs),将其作为视觉语言行动(VLA)策略。我们的方法消除了训练此类代理需要大量人类演示或手动工程奖励的需求。我们在强大的零样本基准测试上进行了基准测试,包括GPT-4o以及针对我们任务进行监督的微调MLLMs。结果表明,我们的RL微调MLLM在所有基准测试上表现均大幅度超越(提高幅度在10.4-16.5%),并能很好地推广到新的场景和任务。据我们所知,这是首次将MLLMs作为能够适应在线RL奖励的帮助请求行为的VLA代理进行适应展示。
论文及项目相关链接
Summary
在家庭环境中操作的实体代理需要解释模糊和未指定的人类指令。为了准确推断用户意图并更有效地执行任务,家用机器人应该能够识别指令的模糊性并询问相关的澄清问题。为此,我们引入了“问行任务”(Ask-to-Act task),并在此任务中研究实体代理如何在使用未指定指令进行单对象或多对象重新布置时解决模糊性和导航问题。我们提出了一种新颖的方法,通过在线强化学习微调多模态大型语言模型(MLLMs),将其作为视觉语言行动(VLA)策略。我们的方法消除了对大规模人类演示或手动工程奖励的需求。在基准测试中,我们的RL微调MLLM显著优于包括GPT-4o在内的零样本基线,并具有良好的泛化能力。据我们所知,这是首次展示将MLLMs适应为VLA代理,可以使用LLM生成的奖励进行在线RL行动和寻求帮助。
Key Takeaways
- 实体代理需要解释家庭环境中的模糊和未指定的人类指令。
- 家用机器人应能识别指令模糊性并询问澄清问题以准确推断用户意图。
- 引入“问行任务”以研究实体代理在解决家庭环境中的指令模糊性和导航问题时的策略。
- 提出一种通过在线强化学习微调多模态大型语言模型(MLLMs)的新方法,作为视觉语言行动(VLA)策略。
- 该方法不需要大规模的人类演示或手动工程奖励。
- RL微调MLLM在基准测试中显著优于零样本基线,包括GPT-4o。
点此查看论文截图



Self-Evolving Multi-Agent Simulations for Realistic Clinical Interactions
Authors:Mohammad Almansoori, Komal Kumar, Hisham Cholakkal
In this work, we introduce MedAgentSim, an open-source simulated clinical environment with doctor, patient, and measurement agents designed to evaluate and enhance LLM performance in dynamic diagnostic settings. Unlike prior approaches, our framework requires doctor agents to actively engage with patients through multi-turn conversations, requesting relevant medical examinations (e.g., temperature, blood pressure, ECG) and imaging results (e.g., MRI, X-ray) from a measurement agent to mimic the real-world diagnostic process. Additionally, we incorporate self improvement mechanisms that allow models to iteratively refine their diagnostic strategies. We enhance LLM performance in our simulated setting by integrating multi-agent discussions, chain-of-thought reasoning, and experience-based knowledge retrieval, facilitating progressive learning as doctor agents interact with more patients. We also introduce an evaluation benchmark for assessing the LLM’s ability to engage in dynamic, context-aware diagnostic interactions. While MedAgentSim is fully automated, it also supports a user-controlled mode, enabling human interaction with either the doctor or patient agent. Comprehensive evaluations in various simulated diagnostic scenarios demonstrate the effectiveness of our approach. Our code, simulation tool, and benchmark are available at \href{https://medagentsim.netlify.app/}.
在这项工作中,我们介绍了MedAgentSim,这是一个开源的模拟临床环境,其中包含医生、患者和测量代理,旨在评估和提高大型语言模型在动态诊断环境中的性能。与以前的方法不同,我们的框架要求医生代理通过多轮对话积极与患者互动,从测量代理请求相关的医学检查(例如,体温、血压、心电图)和成像结果(例如,MRI、X射线),以模拟现实世界的诊断过程。此外,我们引入了自我改进机制,允许模型通过不断与更多患者互动来迭代优化其诊断策略。我们通过集成多代理讨论、链式思维推理和基于经验的知识检索,在模拟环境中提高大型语言模型的性能,从而促进医生代理在与更多患者互动时的渐进学习。我们还引入了一个评估基准,以评估大型语言模型参与动态、基于上下文诊断交互的能力。虽然MedAgentSim完全自动化,但它也支持用户控制模式,可以与医生或患者代理进行人机交互。在各种模拟诊断场景中的综合评估证明了我们方法的有效性。我们的代码、仿真工具和基准测试平台可在[https://medagentsim.netlify.app/]上找到。
论文及项目相关链接
PDF 14 page, 4 figures, 61 references, presented in MICCAI (Oral)
Summary:
MedAgentSim是一个开源模拟临床环境,包含医生、患者和测量代理,旨在评估和提高大型语言模型在动态诊断环境中的性能。该框架要求医生代理通过多轮对话积极与患者互动,请求相关医学检查和影像结果,模仿现实世界的诊断过程。此外,它整合了多代理讨论、链式思维和基于经验的知识检索,以提高模型在模拟环境中的性能。同时,还提供了一个评估基准,以评估大型语言模型在动态、上下文感知的诊断交互中的能力。
Key Takeaways:
- MedAgentSim是一个模拟临床环境,用于评估和提高大型语言模型在动态诊断场景中的性能。
- 该框架包含医生、患者和测量代理,模拟真实的医生与患者的互动过程。
- MedAgentSim要求医生代理通过多轮对话与患者互动,并请求医学检查和影像结果。
- 框架支持多代理讨论、链式思维和基于经验的知识检索,帮助模型改进诊断策略。
- 提供一个评估基准,用于评估大型语言模型在上下文感知的动态诊断交互中的能力。
- MedAgentSim支持全自动和用户控制模式,可以与人机交互。
- 该框架的代码、模拟工具和评估基准已公开发布。
点此查看论文截图



Grounded GUI Understanding for Vision-Based Spatial Intelligent Agent: Exemplified by Extended Reality Apps
Authors:Shuqing Li, Binchang Li, Yepang Liu, Cuiyun Gao, Jianping Zhang, Shing-Chi Cheung, Michael R. Lyu
In recent years, spatial computing a.k.a. Extended Reality (XR) has emerged as a transformative technology, offering users immersive and interactive experiences across diversified virtual environments. Users can interact with XR apps through interactable GUI elements (IGEs) on the stereoscopic three-dimensional (3D) graphical user interface (GUI). The accurate recognition of these IGEs is instrumental, serving as the foundation of many software engineering tasks, including automated testing and effective GUI search. The most recent IGE detection approaches for 2D mobile apps typically train a supervised object detection model based on a large-scale manually-labeled GUI dataset, usually with a pre-defined set of clickable GUI element categories like buttons and spinners. Such approaches can hardly be applied to IGE detection in XR apps, due to a multitude of challenges including complexities posed by open-vocabulary and heterogeneous IGE categories, intricacies of context-sensitive interactability, and the necessities of precise spatial perception and visual-semantic alignment for accurate IGE detection results. Thus, it is necessary to embark on the IGE research tailored to XR apps. In this paper, we propose the first zero-shot cOntext-sensitive inteRactable GUI ElemeNT dEtection framework for virtual Reality apps, named Orienter. By imitating human behaviors, Orienter observes and understands the semantic contexts of XR app scenes first, before performing the detection. The detection process is iterated within a feedback-directed validation and reflection loop. Specifically, Orienter contains three components, including (1) Semantic context comprehension, (2) Reflection-directed IGE candidate detection, and (3) Context-sensitive interactability classification. Extensive experiments demonstrate that Orienter is more effective than the state-of-the-art GUI element detection approaches.
近年来,空间计算(又称扩展现实(XR))作为一项变革性技术崭露头角,为用户在多样化的虚拟环境中提供沉浸式和交互式体验。用户可以通过立体三维(3D)图形用户界面(GUI)上的可交互GUI元素(IGEs)与XR应用程序进行交互。这些IGEs的准确识别至关重要,是许多软件工程任务的基础,包括自动化测试和有效的GUI搜索。针对2D移动应用的最新IGE检测方法通常基于大规模手动标记的GUI数据集训练监督对象检测模型,通常具有预定义的可点击GUI元素类别,例如按钮和微调器。由于开放词汇和异质IGE类别的复杂性、上下文敏感交互的复杂性以及对精确空间感知和视觉语义对齐的必需性等多重挑战,这些方案几乎无法应用于XR应用程序中的IGE检测。因此,有必要针对XR应用程序定制IGE研究。在本文中,我们提出了针对虚拟现实应用程序的第一个零样本上下文敏感可交互GUI元素检测框架,名为Orienter。通过模仿人类行为,Orienter首先观察和理解XR应用程序场景语义上下文,然后进行检测。检测过程是在反馈指导的验证和反思循环中进行的。具体来说,Orienter包含三个组件,包括(1)语义上下文理解、(2)反思指导的IGE候选检测、(3)上下文敏感交互性分类。大量实验表明,Orienter比最先进的GUI元素检测方法更有效。
论文及项目相关链接
Summary
该文本介绍了空间计算(Extended Reality,XR)技术及其在虚拟环境中的沉浸式互动体验应用。用户可以通过立体三维图形用户界面(GUI)上的可交互GUI元素(IGEs)与XR应用程序进行交互。准确的IGE识别对于包括自动化测试和有效的GUI搜索在内的许多软件工程任务至关重要。然而,现有的IGE检测方法难以应用于XR应用程序中的IGE检测,面临开放词汇、异质IGE类别、上下文敏感交互性等多方面的挑战。因此,本文提出了一种名为Orienter的零样本上下文敏感交互式GUI元素检测框架,通过模仿人类行为来观察和理解XR应用程序场景的语义上下文,进行IGE检测,并通过反馈导向的验证和反思循环进行迭代优化。实验证明,Orienter比现有的GUI元素检测方法更有效。
Key Takeaways
- 空间计算(XR)技术提供沉浸式互动体验在虚拟环境中。
- IGE识别在软件工程任务中起关键作用,如自动化测试和GUI搜索。
- 现有IGE检测方法难以应用于XR应用程序中的IGE检测,存在多种挑战。
- Orienter框架通过模仿人类行为来进行IGE检测,并观察和理解XR应用程序场景的语义上下文。
- Orienter包含三个组件:语义上下文理解、反思导向的IGE候选检测、上下文敏感交互性分类。
- Orienter通过反馈导向的验证和反思循环进行迭代优化。
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