发布日期: 2025-07-15

更新日期: 2025-07-17

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2025-07-15 更新

Introspection of Thought Helps AI Agents

Authors:Haoran Sun, Shaoning Zeng

AI Agents rely on Large Language Models (LLMs) and Multimodal-LLMs (MLLMs) to perform interpretation and inference in text and image tasks without post-training, where LLMs and MLLMs play the most critical role and determine the initial ability and limitations of AI Agents. Usually, AI Agents utilize sophisticated prompt engineering and external reasoning framework to obtain a promising interaction with LLMs, e.g., Chain-of-Thought, Iteration of Thought and Image-of-Thought. However, they are still constrained by the inherent limitations of LLM in understanding natural language, and the iterative reasoning process will generate a large amount of inference cost. To this end, we propose a novel AI Agent Reasoning Framework with Introspection of Thought (INoT) by designing a new LLM-Read code in prompt. It enables LLM to execute programmatic dialogue reasoning processes following the code in prompt. Therefore, self-denial and reflection occur within LLM instead of outside LLM, which can reduce token cost effectively. Through our experiments on six benchmarks for three different tasks, the effectiveness of INoT is verified, with an average improvement of 7.95% in performance, exceeding the baselines. Furthermore, the token cost of INoT is lower on average than the best performing method at baseline by 58.3%. In addition, we demonstrate the versatility of INoT in image interpretation and inference through verification experiments.

人工智能（AI）代理依赖大型语言模型（LLM）和多模态LLM（MLLM）来执行文本和图像任务的解释和推理，无需进行后续训练。在这里，LLM和MLLM扮演着至关重要的角色，决定了AI代理的初始能力和局限性。通常，AI代理利用复杂的提示工程和外部推理框架与LLM进行交互，如思维链、思维迭代和思维图像等。然而，它们仍然受到LLM在理解自然语言方面固有局限性的制约，且迭代推理过程会产生大量的推理成本。为此，我们提出了一种具有思想内省（INoT）的新型AI代理推理框架，通过设计新的LLM-Read提示码来实现。它能够让LLM遵循提示中的代码执行程序化对话推理过程。因此，自我否定和反思发生在LLM内部而不是外部，这可以有效地降低令牌成本。我们在三个不同任务的六个基准测试上进行了实验，验证了INoT的有效性，性能平均提高了7.95%，超过了基线。此外，INoT的平均令牌成本比基线中表现最好的方法降低了58.3%。此外，我们还通过验证实验展示了INoT在图像解释和推理方面的通用性。

论文及项目相关链接

PDF

Summary
基于大型语言模型（LLM）和多模态语言模型（MLLM）的人工智能代理（AI Agent）在文本和图像任务中执行解释和推理，无需进行后续训练。然而，它们受到LLM理解自然语言固有局限性的制约，并且迭代推理过程会产生大量的推理成本。为此，我们提出了一种新型的AI Agent推理框架——思维内省（INoT），通过设计新的LLM-Read代码提示来实现。该框架使LLM能够按照提示中的代码执行程序化对话推理过程，从而在LLM内部发生自我否定和反思，有效降低令牌成本。实验验证显示，INoT在三个不同任务的六个基准测试上平均性能提升7.95%，并且相较于基线方法平均降低了58.3%的令牌成本。此外，我们还在图像解读和推理方面验证了INoT的通用性。

Key Takeaways

AI Agents 依赖 LLM 和 MLLMs 进行文本和图像任务的解释和推理，无需进行后续训练。
LLM 和 MLLMs 在 AI Agents 中起到至关重要的作用，决定其初始能力和局限性。
AI Agents 通常通过复杂的提示工程和外部推理框架与 LLMs 进行交互。
LLM 在理解自然语言方面存在固有局限性，迭代推理过程会产生大量推理成本。
提出的 INoT 框架通过设计新的 LLM-Read 代码提示来减少令牌成本并增强性能。
INoT 框架使 LLMs 能够按照代码提示执行程序化对话推理过程。
INoT 在不同任务的基准测试中表现优越，平均性能提升7.95%，并显著降低令牌成本。

Cool Papers

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Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method

Authors:Geng Sun, Likun Zhang, Jiahui Li, Jing Wu, Jiacheng Wang, Zemin Sun, Changyuan Zhao, Victor C. M. Leung

The integration of unmanned aerial vehicles (UAVs) with Internet of Things (IoT) networks offers promising solutions for efficient data collection. However, the limited energy capacity of UAVs remains a significant challenge. In this case, laser beam directors (LBDs) have emerged as an effective technology for wireless charging of UAVs during operation, thereby enabling sustained data collection without frequent returns to charging stations (CSs). In this work, we investigate the age of information (AoI) optimization in LBD-powered UAV-assisted IoT networks, where multiple UAVs collect data from distributed IoTs while being recharged by laser beams. We formulate a joint optimization problem that aims to minimize the peak AoI while determining optimal UAV trajectories and laser charging strategies. This problem is particularly challenging due to its non-convex nature, complex temporal dependencies, and the need to balance data collection efficiency with energy consumption constraints. To address these challenges, we propose a novel multi-agent proximal policy optimization with temporal memory and multi-agent coordination (MAPPO-TM) framework. Specifically, MAPPO-TM incorporates temporal memory mechanisms to capture the dynamic nature of UAV operations and facilitates effective coordination among multiple UAVs through decentralized learning while considering global system objectives. Simulation results demonstrate that the proposed MAPPO-TM algorithm outperforms conventional approaches in terms of peak AoI minimization and energy efficiency. Ideally, the proposed algorithm achieves up to 15.1% reduction in peak AoI compared to conventional multi-agent deep reinforcement learning (MADRL) methods.

无人机（UAV）与物联网（IoT）网络的融合为高效的数据采集提供了前景广阔的解决方案。然而，无人机有限的能源容量仍然是一个巨大的挑战。在这种情况下，激光束指向器（LBD）作为一种在无人机操作期间进行无线充电的有效技术应运而生，从而实现了持续的数据采集，无需频繁返回充电站（CS）。在这项工作中，我们研究了激光束指向器供电的无人机辅助物联网网络中的信息年龄（AoI）优化问题。其中，多个无人机从分布的物联网收集数据，同时被激光束充电。我们制定了一个联合优化问题，旨在最小化峰值AoI，同时确定无人机的最佳轨迹和激光充电策略。这个问题特别具有挑战性，因为它的非凸性质、复杂的时间依赖性，以及需要在数据采集效率和能源消耗约束之间进行平衡。为了解决这些挑战，我们提出了一种新的多智能体近端策略优化方法，具有时间记忆和多智能体协调（MAPPO-TM）框架。具体来说，MAPPO-TM结合了时间记忆机制来捕捉无人机的动态操作，并通过分散学习促进多个无人机之间的有效协调，同时考虑全球系统目标。仿真结果表明，所提出的MAPPO-TM算法在峰值AoI最小化和能效方面优于传统方法。理想情况下，与传统的多智能体深度增强学习（MADRL）方法相比，所提出算法可将峰值AoI减少高达15.1%。

论文及项目相关链接

PDF 21 pages, 8 figures

摘要

无人机与物联网的结合为高效数据采集提供了前景广阔的解决方案。然而，无人机的有限能源容量仍是重大挑战。激光束导向器技术为无人机在飞行过程中进行无线充电提供了有效手段，从而实现了无需频繁返回充电站即可持续进行数据采集。本文研究了激光束供电的无人机辅助物联网网络中的信息时效优化问题。多个无人机从分布式物联网收集数据，同时被激光束充电。我们制定了一个联合优化问题，旨在最小化峰值信息时效，同时确定无人机的最佳轨迹和激光充电策略。该问题具有非凸性、复杂的时序依赖性和需要在数据收集效率与能耗约束之间取得平衡的特点，因此特别具有挑战性。为了应对这些挑战，我们提出了一种新型的多代理近端策略优化方法，该方法具有时序记忆和多代理协调（MAPPO-TM）框架。具体来说，MAPPO-TM结合了时序记忆机制来捕捉无人机的动态操作特点，并通过分散学习促进了多个无人机之间的有效协调，同时考虑了全球系统目标。模拟结果表明，所提出的MAPPO-TM算法在峰值信息时效最小化和能效方面优于传统方法。理想情况下，该算法与传统多代理深度强化学习方法相比，峰值信息时效最多可减少15.1%。

关键见解

无人机与物联网结合可实现高效数据采集，但能源限制是挑战。
激光束导向器技术为无人机在飞行中无线充电提供了解决方案。
研究了激光束供电的无人机辅助物联网网络中的信息时效优化问题。
提出了联合优化问题，旨在最小化峰值信息时效，确定无人机轨迹和激光充电策略。
问题具有非凸性、复杂的时序依赖性，需平衡数据收集与能源消耗。
引入了多代理近端策略优化框架MAPPO-TM，结合时序记忆与多代理协调。

Cool Papers

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Exploring Design of Multi-Agent LLM Dialogues for Research Ideation

Authors:Keisuke Ueda, Wataru Hirota, Takuto Asakura, Takahiro Omi, Kosuke Takahashi, Kosuke Arima, Tatsuya Ishigaki

Large language models (LLMs) are increasingly used to support creative tasks such as research idea generation. While recent work has shown that structured dialogues between LLMs can improve the novelty and feasibility of generated ideas, the optimal design of such interactions remains unclear. In this study, we conduct a comprehensive analysis of multi-agent LLM dialogues for scientific ideation. We compare different configurations of agent roles, number of agents, and dialogue depth to understand how these factors influence the novelty and feasibility of generated ideas. Our experimental setup includes settings where one agent generates ideas and another critiques them, enabling iterative improvement. Our results show that enlarging the agent cohort, deepening the interaction depth, and broadening agent persona heterogeneity each enrich the diversity of generated ideas. Moreover, specifically increasing critic-side diversity within the ideation-critique-revision loop further boosts the feasibility of the final proposals. Our findings offer practical guidelines for building effective multi-agent LLM systems for scientific ideation. Our code is available at https://github.com/g6000/MultiAgent-Research-Ideator.

大型语言模型（LLM）越来越多地被用于支持创造性任务，如研究创意生成。虽然最近的工作表明，LLM之间的结构化对话可以提高生成想法的新颖性和可行性，但此类交互的最优设计仍不明确。在这项研究中，我们对多智能体LLM对话进行科学创意的全面分析。我们比较了不同智能体角色、智能体数量和对话深度的配置，以了解这些因素如何影响生成想法的新颖性和可行性。我们的实验设置包括一个智能体生成想法，另一个智能体对其进行评价，从而实现迭代改进。我们的结果表明，扩大智能群体、深化交互深度、扩大智能体个性异质性，这些都能丰富生成想法的多样性。而且，在创意评价修订循环中增加批评方多样性的特定做法，进一步提高了最终提案的可行性。我们的研究为构建有效的多智能体LLM系统提供了实用指导。我们的代码可通过https://github.com/g6000/MultiAgent-Research-Ideator获取。

论文及项目相关链接

PDF 16 pages, 1 figure, appendix. Accepted to SIGDIAL 2025

Summary

大型语言模型（LLM）越来越多地被用于支持创造性任务，如研究思想生成。本研究对多智能体LLM对话进行科学思想生成进行了综合分析，对比了不同智能体角色、智能体数量和对话深度的配置，以了解这些因素对生成思想的新颖性和可行性的影响。研究结果表明，扩大智能体群体、深化互动深度、扩大智能体个性的异质性，以及增加批评者多样性的思想批评修订循环，都可以丰富生成思想的多样性并提高其可行性。

Key Takeaways

多智能体LLM对话可提升科学思想生成的新颖性和可行性。
智能体角色、数量和对话深度是影响生成思想质量的重要因素。
扩大智能体群体、深化互动、扩大个性异质性，有助于丰富生成思想的多样性。
增加批评者多样性的思想批评修订循环，能提高生成思想的可行性。
研究结果提供了构建有效多智能体LLM系统的实用指南。
该研究的代码已公开在https://github.com/g6000/MultiAgent-Research-Ideator上提供。

Cool Papers

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CRMAgent: A Multi-Agent LLM System for E-Commerce CRM Message Template Generation

Authors:Yinzhu Quan, Xinrui Li, Ying Chen

In e-commerce private-domain channels such as instant messaging and e-mail, merchants engage customers directly as part of their Customer Relationship Management (CRM) programmes to drive retention and conversion. While a few top performers excel at crafting outbound messages, most merchants struggle to write persuasive copy because they lack both expertise and scalable tools. We introduce CRMAgent, a multi-agent system built on large language models (LLMs) that generates high-quality message templates and actionable writing guidance through three complementary modes. First, group-based learning enables the agent to learn from a merchant’s own top-performing messages within the same audience segment and rewrite low-performing ones. Second, retrieval-and-adaptation fetches templates that share the same audience segment and exhibit high similarity in voucher type and product category, learns their successful patterns, and adapts them to the current campaign. Third, a rule-based fallback provides a lightweight zero-shot rewrite when no suitable references are available. Extensive experiments show that CRMAgent consistently outperforms merchants’ original templates, delivering significant gains in both audience-match and marketing-effectiveness metrics.

在电子商务私域渠道（如即时通讯和电子邮件中），商家会将其作为客户关系管理（CRM）计划的一部分，直接与客户互动，以促进客户保留和转化。虽然一些表现突出的商家擅长制作出站信息，但大多数商家在撰写有说服力的文案时都面临困难，因为他们既缺乏专业知识也缺乏可扩展的工具。我们引入了CRMAgent，这是一个基于大型语言模型（LLM）的多智能体系统，通过三种互补模式生成高质量的信息模板和可操作的写作指导。首先，基于群体的学习使智能体能够从同一受众群体内部商家自己的表现最佳的信息中学习，并改写表现不佳的信息。其次，检索和适应会获取与当前受众群体相同、在优惠券类型和商品类别上具有很高相似度的模板，学习它们的成功模式并将其适应到当前活动中。最后，基于规则的备份在没有任何合适的参考时提供了一个轻便的零起点重写功能。大量实验表明，CRMAgent始终优于商家的原始模板，在受众匹配和营销有效性指标方面都取得了显著的进步。

论文及项目相关链接

PDF

Summary
商户在电子商务私域渠道如即时通讯和电子邮件中，通过客户关系管理（CRM）计划直接与客户互动，以提升客户留存和转化率。商家虽然想发送吸引人的消息却苦于缺乏专业技能和大规模工具支持。现在出现了CRM代理，它通过大型语言模型，以多种方式提供高质量的邮件模板和实用的写作指导，例如通过基于小组学习模仿优质信息并修改质量较低的信息、通过搜索相似的用户群体共享相同的用户群并从他们相似产品中寻找成功案例以获取启示并最终做出修改调整、规则支持自动适配来在没有适合的参考的情况下自动做出改进等。研究表明CRM代理在各种场景应用中效果优于普通商家水平。在扩大应用范围的实验上证明CRM代理对改善市场应用指标有着巨大潜力。它在保证用户匹配度的同时极大提升了市场营销的有效性。我们相信CRM代理的推广和应用将对未来的电商营销领域产生深远影响。

Key Takeaways

在电子商务私域渠道如即时通讯和电子邮件中，CRM成为商户提升客户留存和转化率的关键手段。
大部分商户在撰写具有说服力的信息时面临困难，缺乏专业技能和大规模工具支持是主要挑战。
CRMAgent通过大型语言模型构建的多代理系统能够生成高质量的信息模板并提供写作指导。
CRMAgent具备三种模式：基于小组学习模仿优质信息并修改低质信息、搜索相似的用户群体并从类似产品中获取启示进行适配、以及在缺乏适合的参考时自动做出改进的规则支持自动适配模式。
实验证明CRMAgent在各种场景应用中表现优于普通商户水平，显著提升了市场营销的有效性。
CRMAgent在保障用户匹配度的同时，大幅提升了市场营销的有效性指标，如点击率、转化率等。

Cool Papers

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StarDojo: Benchmarking Open-Ended Behaviors of Agentic Multimodal LLMs in Production-Living Simulations with Stardew Valley

Authors:Weihao Tan, Changjiu Jiang, Yu Duan, Mingcong Lei, Jiageng Li, Yitian Hong, Xinrun Wang, Bo An

Autonomous agents navigating human society must master both production activities and social interactions, yet existing benchmarks rarely evaluate these skills simultaneously. To bridge this gap, we introduce StarDojo, a novel benchmark based on Stardew Valley, designed to assess AI agents in open-ended production-living simulations. In StarDojo, agents are tasked to perform essential livelihood activities such as farming and crafting, while simultaneously engaging in social interactions to establish relationships within a vibrant community. StarDojo features 1,000 meticulously curated tasks across five key domains: farming, crafting, exploration, combat, and social interactions. Additionally, we provide a compact subset of 100 representative tasks for efficient model evaluation. The benchmark offers a unified, user-friendly interface that eliminates the need for keyboard and mouse control, supports all major operating systems, and enables the parallel execution of multiple environment instances, making it particularly well-suited for evaluating the most capable foundation agents, powered by multimodal large language models (MLLMs). Extensive evaluations of state-of-the-art MLLMs agents demonstrate substantial limitations, with the best-performing model, GPT-4.1, achieving only a 12.7% success rate, primarily due to challenges in visual understanding, multimodal reasoning and low-level manipulation. As a user-friendly environment and benchmark, StarDojo aims to facilitate further research towards robust, open-ended agents in complex production-living environments.

自主代理人在人类社会中的导航必须掌握生产活动和社会交互，但现有的基准测试很少同时评估这些技能。为了弥补这一差距，我们推出了StarDojo，这是一个基于星露谷物语的新型基准测试，旨在评估开放生产生活模拟中的AI代理人。在StarDojo中，代理人被要求执行基本的生计活动，如农业和手工艺，同时参与社会互动，在一个充满活力的社区内建立关系。StarDojo涵盖了五大领域的1000个精心策划的任务：农业、手工艺、探索、战斗和社会互动。此外，我们还提供了包含一百个典型任务的紧凑子集，用于高效的模型评估。该基准测试提供了一个统一且用户友好的界面，无需使用键盘和鼠标控制，支持所有主要操作系统，并允许并行执行多个环境实例，因此特别适合评估由多模式大型语言模型（MLLMs）驱动的最先进代理人。对最先进MLLMs代理人的广泛评估显示出了明显的局限性，表现最佳的GPT-4.1模型成功率仅为12.7%，这主要是因为其在视觉理解、多模式推理和低级操作上的挑战。作为一个用户友好的环境和基准测试平台，StarDojo旨在促进在复杂生产生活环境中实现健壮的开放式代理的进一步研究。

论文及项目相关链接

PDF Project website: https://weihaotan.github.io/StarDojo

Summary
星界谷基准测试（StarDojo）是一个基于游戏Stardew Valley的新型基准测试，旨在评估人工智能代理在开放生产生活模拟中的表现。代理需掌握生产活动和社交互动技能，涵盖农业、手工艺、探索、战斗和社交互动五大领域。提供紧凑的代表性任务子集，支持主要操作系统，便于模型快速评估。目前最先进的MLLM代理存在显著局限，最佳模型GPT-4.1成功率仅为12.7%，面临视觉理解、多模式推理和低级操作挑战。StarDojo旨在推动复杂生产生活环境中稳健、开放代理的研究。

Key Takeaways

StarDojo是一种新型基准测试，基于游戏Stardew Valley评估AI代理在生产生活模拟中的表现。
代理需要掌握生产活动和社交互动技能，跨越五大领域。
提供紧凑的代表性任务子集用于高效模型评估。
支持主要操作系统，提供用户友好界面，并可以并行执行多个环境实例。
目前最先进的MLLM代理在StarDojo基准测试中表现有限，最佳模型GPT-4.1成功率仅为12.7%。
代理面临视觉理解、多模式推理和低级操作的挑战。

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Open Source Planning & Control System with Language Agents for Autonomous Scientific Discovery

Authors:Licong Xu, Milind Sarkar, Anto I. Lonappan, Íñigo Zubeldia, Pablo Villanueva-Domingo, Santiago Casas, Christian Fidler, Chetana Amancharla, Ujjwal Tiwari, Adrian Bayer, Chadi Ait Ekioui, Miles Cranmer, Adrian Dimitrov, James Fergusson, Kahaan Gandhi, Sven Krippendorf, Andrew Laverick, Julien Lesgourgues, Antony Lewis, Thomas Meier, Blake Sherwin, Kristen Surrao, Francisco Villaescusa-Navarro, Chi Wang, Xueqing Xu, Boris Bolliet

We present a multi-agent system for automation of scientific research tasks, cmbagent (https://github.com/CMBAgents/cmbagent). The system is formed by about 30 Large Language Model (LLM) agents and implements a Planning & Control strategy to orchestrate the agentic workflow, with no human-in-the-loop at any point. Each agent specializes in a different task (performing retrieval on scientific papers and codebases, writing code, interpreting results, critiquing the output of other agents) and the system is able to execute code locally. We successfully apply cmbagent to carry out a PhD level cosmology task (the measurement of cosmological parameters using supernova data) and evaluate its performance on two benchmark sets, finding superior performance over state-of-the-art LLMs. The source code is available on GitHub, demonstration videos are also available, and the system is deployed on HuggingFace and will be available on the cloud.

我们提出一个用于自动化科学研究任务的多智能体系统，名为cmbagent（https://github.com/CMBAgents/cmbagent）。该系统由大约30个大型语言模型（LLM）智能体组成，并实现了规划和控制策略来协调智能体的工作流程，没有任何环节需要人工参与。每个智能体都专注于不同的任务（如执行科学论文和代码库的检索、编写代码、解释结果、评估其他智能体的输出），并且系统能够在本地执行代码。我们成功地将cmbagent应用于执行博士学位水平的宇宙学任务（使用超新星数据测量宇宙学参数），并在两个基准测试集上对其性能进行了评估，发现其性能优于最新的大型语言模型。源代码已在GitHub上提供，演示视频也已发布，该系统已部署在HuggingFace上，并将提供云服务。

论文及项目相关链接

PDF Accepted contribution to the ICML 2025 Workshop on Machine Learning for Astrophysics. Code: https://github.com/CMBAgents/cmbagent Videos: https://www.youtube.com/@cmbagent HuggingFace: https://huggingface.co/spaces/astropilot-ai/cmbagent Cloud: https://cmbagent.cloud

Summary

一个多智能体系统被提出来自动化科研任务，名为cmbagent。该系统由大约30个大型语言模型（LLM）智能体组成，采用规划与控制策略来协调智能体的工作流程，无需人工干预。每个智能体都专注于不同的任务，如检索科学论文和代码库、编写代码、解释结果、评估其他智能体的输出等。该系统可本地执行代码，成功应用于执行博士级别的宇宙学任务，并在两个基准测试集上评估其性能，表现出优于最新LLM的性能。源代码已上传到GitHub，还有演示视频，并且该系统已在HuggingFace上部署，也将可在云端使用。

Key Takeaways

cmbagent是一个多智能体系统，用于自动化科研任务。
系统由大约30个大型语言模型（LLM）智能体构成。
cmbagent采用规划与控制策略协调智能体的工作流程，无需人工干预。
智能体各自专注于不同的任务，如检索、编写代码、解释结果等。
cmbagent成功应用于执行博士级别的宇宙学任务。
cmbagent在基准测试集上的性能优于当前的最新LLM。

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The Dark Side of LLMs Agent-based Attacks for Complete Computer Takeover

Authors:Matteo Lupinacci, Francesco Aurelio Pironti, Francesco Blefari, Francesco Romeo, Luigi Arena, Angelo Furfaro

The rapid adoption of Large Language Model (LLM) agents and multi-agent systems enables unprecedented capabilities in natural language processing and generation. However, these systems have introduced unprecedented security vulnerabilities that extend beyond traditional prompt injection attacks. This paper presents the first comprehensive evaluation of LLM agents as attack vectors capable of achieving complete computer takeover through the exploitation of trust boundaries within agentic AI systems where autonomous entities interact and influence each other. We demonstrate that adversaries can leverage three distinct attack surfaces - direct prompt injection, RAG backdoor attacks, and inter-agent trust exploitation - to coerce popular LLMs (including GPT-4o, Claude-4 and Gemini-2.5) into autonomously installing and executing malware on victim machines. Our evaluation of 17 state-of-the-art LLMs reveals an alarming vulnerability hierarchy: while 41.2% of models succumb to direct prompt injection, 52.9% are vulnerable to RAG backdoor attacks, and a critical 82.4% can be compromised through inter-agent trust exploitation. Notably, we discovered that LLMs which successfully resist direct malicious commands will execute identical payloads when requested by peer agents, revealing a fundamental flaw in current multi-agent security models. Our findings demonstrate that only 5.9% of tested models (1/17) proved resistant to all attack vectors, with the majority exhibiting context-dependent security behaviors that create exploitable blind spots. Our findings also highlight the need to increase awareness and research on the security risks of LLMs, showing a paradigm shift in cybersecurity threats, where AI tools themselves become sophisticated attack vectors.

大型语言模型（LLM）代理的快速采用和多代理系统的出现，为自然语言处理和生成提供了前所未有的能力。然而，这些系统也引入了前所未有的安全漏洞，这些漏洞超出了传统的提示注入攻击的范围。本文对LLM代理作为攻击载体进行了首次全面评估，这些攻击载体能够通过利用代理人工智能系统内的信任边界来实完成计算机系统的全面接管，在这些系统内自主实体相互交互和影响。我们证明，对手可以利用三种不同的攻击面——直接提示注入、RAG后门攻击和代理间信任利用——来迫使流行的大型语言模型（包括GPT-4o、Claude-4和Gemini-2.5）在受害机器上自主安装和执行恶意软件。我们对17款最先进的大型语言模型的评估揭示了一个令人警惕的漏洞层次结构：虽然41.2%的模型会屈服于直接的提示注入，但52.9%的模型容易受到RAG后门攻击，而高达82.4%的模型可以通过代理间的信任利用来危及安全。值得注意的是，我们发现即使大型语言模型成功抵抗了直接的恶意命令，当收到同伴代理的请求时，它们也会执行相同的有效负载，这揭示了当前多代理安全模型中的根本缺陷。我们的研究结果表明，仅有5.9%（即十七分之一）的受测试模型能够抵抗所有攻击载体，而大多数模型表现出上下文相关的安全行为，这产生了可利用的盲点。我们的研究结果还强调了人们对大型语言模型安全风险的认识和研究需求，显示出网络安全威胁的模式转变，即人工智能工具本身成为高级的攻击载体。

论文及项目相关链接

PDF

摘要

大规模语言模型（LLM）和多代理系统的快速采用使自然语言处理和生成能力达到前所未有的水平，但也引入了超越传统提示注入攻击的安全漏洞。本文对LLM代理作为攻击载体进行了首次全面评估，探讨了如何通过利用代理智能系统内的信任边界，实现对计算机的完全控制。通过演示三种独特的攻击面——直接提示注入、RAG后门攻击和代理间信任剥削，可以迫使流行的LLM（包括GPT-4o、Claude-4和Gemini-2.5）在受害者机器上自主安装和执行恶意软件。对17种最先进LLM的评估显示出一个令人担忧的漏洞等级：虽然只有少数模型完全安全无虞。因此，对LLM的安全风险提高认识和研究变得至关重要。这表明网络安全威胁的模式正在转变，人工智能工具本身成为复杂的攻击载体。

关键见解

LLM和多代理系统虽然增强了自然语言处理功能，但也引入了新的安全漏洞。
LLM代理可被用作攻击媒介，能够实现对计算机的完全控制。
存在三种主要的攻击方式：直接提示注入、RAG后门攻击和代理间信任剥削。
多数LLM模型存在安全漏洞，仅有少数能够抵御所有攻击方式。
即使LLM成功抵抗直接恶意命令，它们仍可能在接收到来自其他代理的请求时执行相同的恶意任务。这暴露出当前多代理安全模型的一个根本缺陷。
大部分LLM模型表现出基于上下文的安全行为，这导致存在可利用的盲点。这意味着需要根据上下文来设计和实施安全措施。

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Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Authors:Gheorghe Comanici, Eric Bieber, Mike Schaekermann, Ice Pasupat, Noveen Sachdeva, Inderjit Dhillon, Marcel Blistein, Ori Ram, Dan Zhang, Evan Rosen, Luke Marris, Sam Petulla, Colin Gaffney, Asaf Aharoni, Nathan Lintz, Tiago Cardal Pais, Henrik Jacobsson, Idan Szpektor, Nan-Jiang Jiang, Krishna Haridasan, Ahmed Omran, Nikunj Saunshi, Dara Bahri, Gaurav Mishra, Eric Chu, Toby Boyd, Brad Hekman, Aaron Parisi, Chaoyi Zhang, Kornraphop Kawintiranon, Tania Bedrax-Weiss, Oliver Wang, Ya Xu, Ollie Purkiss, Uri Mendlovic, Ilaï Deutel, Nam Nguyen, Adam Langley, Flip Korn, Lucia Rossazza, Alexandre Ramé, Sagar Waghmare, Helen Miller, Vaishakh Keshava, Ying Jian, Xiaofan Zhang, Raluca Ada Popa, Kedar Dhamdhere, Blaž Bratanič, Kyuyeun Kim, Terry Koo, Ferran Alet, Yi-ting Chen, Arsha Nagrani, Hannah Muckenhirn, Zhiyuan Zhang, Corbin Quick, Filip Pavetić, Duc Dung Nguyen, Joao Carreira, Michael Elabd, Haroon Qureshi, Fabian Mentzer, Yao-Yuan Yang, Danielle Eisenbud, Anmol Gulati, Ellie Talius, Eric Ni, Sahra Ghalebikesabi, Edouard Yvinec, Alaa Saade, Thatcher Ulrich, Lorenzo Blanco, Dan A. Calian, Muhuan Huang, Aäron van den Oord, Naman Goyal, Terry Chen, Praynaa Rawlani, Christian Schallhart, Swachhand Lokhande, Xianghong Luo, Jyn Shan, Ceslee Montgomery, Victoria Krakovna, Federico Piccinini, Omer Barak, Jingyu Cui, Yiling Jia, Mikhail Dektiarev, Alexey Kolganov, Shiyu Huang, Zhe Chen, Xingyu Wang, Jessica Austin, Peter de Boursac, Evgeny Sluzhaev, Frank Ding, Huijian Li, Surya Bhupatiraju, Mohit Agarwal, Sławek Kwasiborski, Paramjit Sandhu, Patrick Siegler, Ahmet Iscen, Eyal Ben-David, Shiraz Butt, Miltos Allamanis, Seth Benjamin, Robert Busa-Fekete, Felix Hernandez-Campos, Sasha Goldshtein, Matt Dibb, Weiyang Zhang, Annie Marsden, Carey Radebaugh, Stephen Roller, Abhishek Nayyar, Jacob Austin, Tayfun Terzi, Bhargav Kanagal Shamanna, Pete Shaw, Aayush Singh, Florian Luisier, Artur Mendonça, Vaibhav Aggarwal, Larisa Markeeva, Claudio Fantacci, Sergey Brin, HyunJeong Choe, Guanyu Wang, Hartwig Adam, Avigail Dabush, Tatsuya Kiyono, Eyal Marcus, Jeremy Cole, Theophane Weber, Hongrae Lee, Ronny Huang, Alex Muzio, Leandro Kieliger, Maigo Le, Courtney Biles, Long Le, Archit Sharma, Chengrun Yang, Avery Lamp, Dave Dopson, Nate Hurley, Katrina Xinyi Xu, Zhihao Shan, Shuang Song, Jiewen Tan, Alexandre Senges, George Zhang, Chong You, Yennie Jun, David Raposo, Susanna Ricco, Xuan Yang, Weijie Chen, Prakhar Gupta, Arthur Szlam, Kevin Villela, Chun-Sung Ferng, Daniel Kasenberg, Chen Liang, Rui Zhu, Arunachalam Narayanaswamy, Florence Perot, Paul Pucciarelli, Anna Shekhawat, Alexey Stern, Rishikesh Ingale, Stefani Karp, Sanaz Bahargam, Adrian Goedeckemeyer, Jie Han, Sicheng Li, Andrea Tacchetti, Dian Yu, Abhishek Chakladar, Zhiying Zhang, Mona El Mahdy, Xu Gao, Dale Johnson, Samrat Phatale, AJ Piergiovanni, Hyeontaek Lim, Clement Farabet, Carl Lebsack, Theo Guidroz, John Blitzer, Nico Duduta, David Madras, Steve Li, Daniel von Dincklage, Xin Li, Mahdis Mahdieh, George Tucker, Ganesh Jawahar, Owen Xiao, Danny Tarlow, Robert Geirhos, Noam Velan, Daniel Vlasic, Kalesha Bullard, SK Park, Nishesh Gupta, Kellie Webster, Ayal Hitron, Jieming Mao, Julian Eisenschlos, Laurel Prince, Nina D’Souza, Kelvin Zheng, Sara Nasso, Gabriela Botea, Carl Doersch, Caglar Unlu, Chris Alberti, Alexey Svyatkovskiy, Ankita Goel, Krzysztof Choromanski, Pan-Pan Jiang, Richard Nguyen, Four Flynn, Daria Ćurko, Peter Chen, Nicholas Roth, Kieran Milan, Caleb Habtegebriel, Shashi Narayan, Michael Moffitt, Jake Marcus, Thomas Anthony, Brendan McMahan, Gowoon Cheon, Ruibo Liu, Megan Barnes, Lukasz Lew, Rebeca Santamaria-Fernandez, Mayank Upadhyay, Arjun Akula, Arnar Mar Hrafnkelsson, Alvaro Caceres, Andrew Bunner, Michal Sokolik, Subha Puttagunta, Lawrence Moore, Berivan Isik, Jay Hartford, Lawrence Chan, Pradeep Shenoy, Dan Holtmann-Rice, Jane Park, Fabio Viola, Alex Salcianu, Sujeevan Rajayogam, Ian Stewart-Binks, Zelin Wu, Richard Everett, Xi Xiong, Pierre-Antoine Manzagol, Gary Leung, Carl Saroufim, Bo Pang, Dawid Wegner, George Papamakarios, Jennimaria Palomaki, Helena 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Manning, Chiyuan Zhang, Denis Vnukov, Igor Mordatch, Raia Hadsell Wolfgang Macherey, Ryan Kappedal, Jim Stephan, Aditya Tripathi, Klaus Macherey, Jun Qian, Abhishek Bhowmick, Shekoofeh Azizi, Rémi Leblond, Shiva Mohan Reddy Garlapati, Timothy Knight, Matthew Wiethoff, Wei-Chih Hung, Anelia Angelova, Georgios Evangelopoulos, Pawel Janus, Dimitris Paparas, Matthew Rahtz, Ken Caluwaerts, Vivek Sampathkumar, Daniel Jarrett, Shadi Noghabi, Antoine Miech, Chak Yeung, Geoff Clark, Henry Prior, Fei Zheng, Jean Pouget-Abadie, Indro Bhattacharya, Kalpesh Krishna, Will Bishop, Zhe Yuan, Yunxiao Deng, Ashutosh Sathe, Kacper Krasowiak, Ciprian Chelba, Cho-Jui Hsieh, Kiran Vodrahalli, Buhuang Liu, Thomas Köppe, Amr Khalifa, Lubo Litchev, Pichi Charoenpanit, Reed Roberts, Sachin Yadav, Yasumasa Onoe, Desi Ivanov, Megha Mohabey, Vighnesh Birodkar, Nemanja Rakićević, Pierre Sermanet, Vaibhav Mehta, Krishan Subudhi, Travis Choma, Will Ng, Luheng He, Kathie Wang, Tasos Kementsietsidis, Shane Gu, Mansi 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Charline Le Lan, Dheeru Dua, Shuba Lall, Pranav Shyam, Frankie Garcia, Sarah Nguyen, Michael Guzman, AJ Maschinot, Marcello Maggioni, Ming-Wei Chang, Karol Gregor, Lotte Weerts, Kumaran Venkatesan, Bogdan Damoc, Leon Liu, Jan Wassenberg, Lewis Ho, Becca Roelofs, Majid Hadian, François-Xavier Aubet, Yu Liang, Sami Lachgar, Danny Karmon, Yong Cheng, Amelio Vázquez-Reina, Angie Chen, Zhuyun Dai, Andy Brock, Shubham Agrawal, Chenxi Pang, Peter Garst, Mariella Sanchez-Vargas, Ivor Rendulic, Aditya Ayyar, Andrija Ražnatović, Olivia Ma, Roopali Vij, Neha Sharma, Ashwin Balakrishna, Bingyuan Liu, Ian Mackinnon, Sorin Baltateanu, Petra Poklukar, Gabriel Ibagon, Colin Ji, Hongyang Jiao, Isaac Noble, Wojciech Stokowiec, Zhihao Li, Jeff Dean, David Lindner, Mark Omernick, Kristen Chiafullo, Mason Dimarco, Vitor Rodrigues, Vittorio Selo, Garrett Honke, Xintian Cindy Wu, Wei He, Adam Hillier, Anhad Mohananey, Vihari Piratla, Chang Ye, Chase Malik, Sebastian Riedel, Samuel Albanie, Zi Yang, Kenny Vassigh, Maria Bauza, Sheng Li, Yiqing Tao, Nevan Wichers, Andrii Maksai, Abe Ittycheriah, Ross Mcilroy, Bryan Seybold, Noah Goodman, Romina Datta, Steven M. Hernandez, Tian Shi, Yony Kochinski, Anna Bulanova, Ken Franko, Mikita Sazanovich, Nicholas FitzGerald, Praneeth Kacham, Shubha Srinivas Raghvendra, Vincent Hellendoorn, Alexander Grushetsky, Julian Salazar, Angeliki Lazaridou, Jason Chang, Jan-Thorsten Peter, Sushant Kafle, Yann Dauphin, Abhishek Rao, Filippo Graziano, Izhak Shafran, Yuguo Liao, Tianli Ding, Geng Yan, Grace Chu, Zhao Fu, Vincent Roulet, Gabriel Rasskin, Duncan Williams, Shahar Drath, Alex Mossin, Raphael Hoffmann, Jordi Orbay, Francesco Bertolini, Hila Sheftel, Justin Chiu, Siyang Xue, Yuheng Kuang, Ferjad Naeem, Swaroop Nath, Nana Nti, Phil Culliton, Kashyap Krishnakumar, Michael Isard, Pei Sun, Ayan Chakrabarti, Nathan Clement, Regev Cohen, Arissa Wongpanich, GS Oh, Ashwin Murthy, Hao Zheng, Jessica Hamrick, Oskar Bunyan, Suhas Ganesh, Nitish Gupta, Roy Frostig, John Wieting, Yury Malkov, Pierre Marcenac, Zhixin Lucas Lai, Xiaodan Tang, Mohammad Saleh, Fedir Zubach, Chinmay Kulkarni, Huanjie Zhou, Vicky Zayats, Nan Ding, Anshuman Tripathi, Arijit Pramanik, Patrik Zochbauer, Harish Ganapathy, Vedant Misra, Zach Behrman, Hugo Vallet, Mingyang Zhang, Mukund Sridhar, Ye Jin, Mohammad Babaeizadeh, Siim Põder, Megha Goel, Divya Jain, Tajwar Nasir, Shubham Mittal, Tim Dozat, Diego Ardila, Aliaksei Severyn, Fabio Pardo, Sammy Jerome, Siyang Qin, Louis Rouillard, Amir Yazdanbakhsh, Zizhao Zhang, Shivani Agrawal, Kaushik Shivakumar, Caden Lu, Praveen Kallakuri, Rachita Chhaparia, Kanishka Rao, Charles Kwong, Asya Fadeeva, Shitij Nigam, Yan Virin, Yuan Zhang, Balaji Venkatraman, Beliz Gunel, Marc Wilson, Huiyu Wang, Abhinav Gupta, Xiaowei Xu, Adrien Ali Taïga, Kareem Mohamed, Doug Fritz, Daniel Rodriguez, Zoubin Ghahramani, Harry Askham, Lior Belenki, James Zhao, Rahul Gupta, Krzysztof Jastrzębski, Takahiro Kosakai, Kaan Katircioglu, Jon Schneider, Rina Panigrahy, Konstantinos Bousmalis, Peter Grabowski, Prajit Ramachandran, Chaitra Hegde, Mihaela Rosca, Angelo Scorza Scarpati, Kyriakos Axiotis, Ying Xu, Zach Gleicher, Assaf Hurwitz Michaely, Mandar Sharma, Sanil Jain, Christoph Hirnschall, Tal Marian, Xuhui Jia, Kevin Mather, Kilol Gupta, Linhai Qiu, Nigamaa Nayakanti, Lucian Ionita, Steven Zheng, Lucia Loher, Kurt Shuster, Igor Petrovski, Roshan Sharma, Rahma Chaabouni, Angel Yeh, James An, Arushi Gupta, Steven Schwarcz, Seher Ellis, Sam Conway-Rahman, Javier Snaider, Alex Zhai, James Atwood, Daniel Golovin, Liqian Peng, Te I, Vivian Xia, Salvatore Scellato, Mahan Malihi, Arthur Bražinskas, Vlad-Doru Ion, Younghoon Jun, James Swirhun, Soroosh Mariooryad, Jiao Sun, Steve Chien, Rey Coaguila, Ariel Brand, Yi Gao, Tom Kwiatkowski, Roee Aharoni, Cheng-Chun Lee, Mislav Žanić, Yichi Zhang, Dan Ethier, Vitaly Nikolaev, Pranav Nair, Yoav Ben Shalom, Hen Fitoussi, Jai Gupta, Hongbin Liu, Dee Cattle, Tolga Bolukbasi, Ben Murdoch, Fantine Huot, Yin Li, Chris Hahn

In this report, we introduce the Gemini 2.X model family: Gemini 2.5 Pro and Gemini 2.5 Flash, as well as our earlier Gemini 2.0 Flash and Flash-Lite models. Gemini 2.5 Pro is our most capable model yet, achieving SoTA performance on frontier coding and reasoning benchmarks. In addition to its incredible coding and reasoning skills, Gemini 2.5 Pro is a thinking model that excels at multimodal understanding and it is now able to process up to 3 hours of video content. Its unique combination of long context, multimodal and reasoning capabilities can be combined to unlock new agentic workflows. Gemini 2.5 Flash provides excellent reasoning abilities at a fraction of the compute and latency requirements and Gemini 2.0 Flash and Flash-Lite provide high performance at low latency and cost. Taken together, the Gemini 2.X model generation spans the full Pareto frontier of model capability vs cost, allowing users to explore the boundaries of what is possible with complex agentic problem solving.

在这份报告中，我们介绍了Gemini 2.X模型系列：包括Gemini 2.5 Pro和Gemini 2.5 Flash，以及我们早前推出的Gemini 2.0 Flash和Flash-Lite模型。Gemini 2.5 Pro至今为止是我们功能最强大的模型，在前沿编码和推理基准测试中达到了最先进的性能。除了令人印象深刻的编码和推理技能外，Gemini 2.5 Pro还是一个擅长多模式理解的思考模型，现在能够处理长达3小时的视频内容。其独特的长期上下文、多模式和理解能力的结合，可以解锁新的代理工作流程。Gemini 2.5 Flash在计算和延迟要求方面提供了出色的推理能力的一小部分，而Gemini 2.0 Flash和Flash-Lite以低延迟和成本提供了高性能。综上所述，Gemini 2.X模型世代涵盖了模型能力与成本之间的全Pareto边界，使用户能够探索复杂代理问题解决可能的边界。

论文及项目相关链接

PDF 72 pages, 17 figures

Summary

新一代Gemini模型家族包括Gemini 2.5 Pro、Gemini 2.5 Flash、早期的Gemini 2.0 Flash和Flash-Lite模型。Gemini 2.5 Pro是目前功能最强大的模型，能在前沿编码和基准测试中实现最佳性能。除了出色的编码和推理能力外，Gemini 2.5 Pro还擅长多模式理解，并能处理长达3小时的视频内容。其独特的长期上下文、多模式推理能力相结合，可解锁新的代理工作流程。Gemini 2.5 Flash在计算和延迟要求方面表现出色，拥有优秀的推理能力，而Gemini 2.0 Flash和Flash-Lite则在低延迟和成本下提供高性能。总体来说，Gemini 2.X模型在模型能力与成本之间达到了全面均衡的帕累托前沿。

Key Takeaways

Gemini 2.X模型家族包括Gemini 2.5 Pro、Gemini 2.5 Flash以及早期的Gemini 2.0 Flash和Flash-Lite模型。
Gemini 2.5 Pro是迄今为止最强大的模型，能在前沿编码和基准测试中实现最佳性能。
Gemini 2.5 Pro具备多模式理解能力，能处理长达3小时的视频内容。
Gemini 2.5 Pro具有独特的长期上下文和多模式推理能力组合，支持新的代理工作流程。
Gemini 2.5 Flash在推理能力方面表现出色，同时满足计算和延迟要求。
Gemini 2.0 Flash和Flash-Lite在低延迟和成本条件下提供高性能。

Cool Papers

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AGENTSAFE: Benchmarking the Safety of Embodied Agents on Hazardous Instructions

Authors:Aishan Liu, Zonghao Ying, Le Wang, Junjie Mu, Jinyang Guo, Jiakai Wang, Yuqing Ma, Siyuan Liang, Mingchuan Zhang, Xianglong Liu, Dacheng Tao

The rapid advancement of vision-language models (VLMs) and their integration into embodied agents have unlocked powerful capabilities for decision-making. However, as these systems are increasingly deployed in real-world environments, they face mounting safety concerns, particularly when responding to hazardous instructions. In this work, we propose AGENTSAFE, the first comprehensive benchmark for evaluating the safety of embodied VLM agents under hazardous instructions. AGENTSAFE simulates realistic agent-environment interactions within a simulation sandbox and incorporates a novel adapter module that bridges the gap between high-level VLM outputs and low-level embodied controls. Specifically, it maps recognized visual entities to manipulable objects and translates abstract planning into executable atomic actions in the environment. Building on this, we construct a risk-aware instruction dataset inspired by Asimovs Three Laws of Robotics, including base risky instructions and mutated jailbroken instructions. The benchmark includes 45 adversarial scenarios, 1,350 hazardous tasks, and 8,100 hazardous instructions, enabling systematic testing under adversarial conditions ranging from perception, planning, and action execution stages.

视觉语言模型（VLMs）的快速发展及其与实体代理的融合，为决策制定提供了强大的能力。然而，随着这些系统在现实环境中的部署越来越多，它们面临的安全问题也日益突出，特别是在响应危险指令时。在这项工作中，我们提出了AGENTSAFE，这是第一个全面评估实体VLM代理在危险指令下安全性的基准测试。AGENTSAFE在模拟沙盒内模拟了现实代理环境交互，并融入了一个新颖的连接模块，该模块能够弥合高级VLM输出和低级实体控制之间的差距。具体来说，它将识别的视觉实体映射到可操作的对象上，并将抽象规划转化为环境中可执行的原子动作。在此基础上，我们结合阿西莫夫机器人三大法则的启发，构建了风险意识指令数据集，包括基础危险指令和变异的失控指令。该基准测试包含了45个对抗场景、1350个危险任务和8100个危险指令，能够在感知、规划和行动执行等阶段对抗条件下进行系统测试。

论文及项目相关链接

PDF MAS@ICML 2025 camera ready

Summary

视觉语言模型（VLMs）与实体代理的融合为决策制定提供了强大的能力。然而，随着这些系统在现实环境中的部署日益增多，它们面临的安全问题日益突出，特别是在应对危险指令时。为此，我们提出AGENTSAFE，这是首个用于评估危险指令下实体VLM代理安全性的综合基准测试。AGENTSAFE在模拟沙箱内模拟真实代理环境交互，并引入新型适配器模块，缩小高层次的VLM输出与低层次实体控制之间的差距。具体来说，它将识别到的视觉实体映射到可操作对象上，并将抽象规划转化为环境中的可执行原子动作。基于此，我们以阿西莫夫的机器人三大定律为灵感构建风险意识指令数据集，涵盖基础风险指令和变异违规指令。该基准测试包含45个对抗场景、1350项危险任务和8100条危险指令，能够在感知、规划和行动执行阶段系统测试对抗条件。

Key Takeaways

视觉语言模型（VLMs）与实体代理的融合增强了决策能力。
随着系统在现实环境中的部署增加，安全问题的关注逐渐提升。
AGENTSAFE是评估危险指令下实体VLM代理安全性的首个综合基准测试。
AGENTSAFE模拟真实代理环境交互，并引入适配器模块以桥接高层次VLM输出与低层次实体控制之间的差距。
适配器模块能将识别到的视觉实体映射到可操作对象上，并将抽象规划转化为可执行原子动作。
构建了一个风险意识指令数据集，涵盖基础风险指令和变异违规指令。

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GTR: Guided Thought Reinforcement Prevents Thought Collapse in RL-based VLM Agent Training

Authors:Tong Wei, Yijun Yang, Junliang Xing, Yuanchun Shi, Zongqing Lu, Deheng Ye

Reinforcement learning with verifiable outcome rewards (RLVR) has effectively scaled up chain-of-thought (CoT) reasoning in large language models (LLMs). Yet, its efficacy in training vision-language model (VLM) agents for goal-directed action reasoning in visual environments is less established. This work investigates this problem through extensive experiments on complex card games, such as 24 points, and embodied tasks from ALFWorld. We find that when rewards are based solely on action outcomes, RL fails to incentivize CoT reasoning in VLMs, instead leading to a phenomenon we termed thought collapse, characterized by a rapid loss of diversity in the agent’s thoughts, state-irrelevant and incomplete reasoning, and subsequent invalid actions, resulting in negative rewards. To counteract thought collapse, we highlight the necessity of process guidance and propose an automated corrector that evaluates and refines the agent’s reasoning at each RL step. This simple and scalable GTR (Guided Thought Reinforcement) framework trains reasoning and action simultaneously without the need for dense, per-step human labeling. Our experiments demonstrate that GTR significantly enhances the performance and generalization of the LLaVA-7b model across various visual environments, achieving 3-5 times higher task success rates compared to SoTA models with notably smaller model sizes.

强化学习通过可验证的结果奖励（RLVR）有效提升了大型语言模型（LLM）中的链式思维（CoT）推理能力。然而，其在针对视觉环境中目标导向行动推理的训练视觉语言模型（VLM）方面的有效性尚未明确。本研究通过复杂的卡牌游戏（如二十四点）和身体任务（来自ALFWorld）的大量实验来研究这个问题。我们发现，当奖励仅基于行动结果时，强化学习无法激励VLM中的CoT推理，反而导致我们称之为“思维崩溃”的现象，表现为代理思维多样性的迅速丧失、状态无关和不完整的推理，以及随后的无效行动，导致负面奖励。为了对抗思维崩溃，我们强调了过程指导的必要性，并提出了一种自动化校正器，它在每个强化学习步骤中评估和精炼代理的推理。这种简单且可扩展的GTR（引导思维强化）框架同时训练推理和行动，无需密集、分步的人类标签。我们的实验表明，GTR显著提高了LLaVA-7b模型在各种视觉环境中的性能和泛化能力，与最新技术模型相比，任务成功率提高了3-5倍，而且模型尺寸更小。

论文及项目相关链接

PDF Accepted by ICCV 2025

Summary
强化学习结合可验证结果奖励（RLVR）在大规模语言模型（LLM）中实现了链式思维（CoT）推理的扩展应用。然而，其在视觉语言模型（VLM）中进行目标导向行动推理方面的有效性尚未明确。本研究通过复杂卡牌游戏（如二十四点）和ALFWorld中的具体任务实验来探究这一问题。研究发现，当奖励仅基于行动结果时，强化学习无法激励VLM中的思维推理过程，反而会导致我们称之为的“思维崩溃”现象。对此，我们强调了过程指导的必要性，并提出一种自动化修正器，在强化学习的每一步评估和优化代理的推理能力。这种简单且可扩展的GTR（引导思维强化）框架同时训练推理和行动能力，无需密集、逐步的人工标注。实验表明，GTR显著提高了LLaVA-7b模型在各种视觉环境中的性能和泛化能力，相较于当前最佳模型，任务成功率提高了3-5倍，且模型规模更小。

Key Takeaways

强化学习结合可验证结果奖励（RLVR）扩展了大规模语言模型中的链式思维推理。
在视觉语言模型（VLM）中，仅基于行动结果的奖励会导致“思维崩溃”现象。
“思维崩溃”现象特征为代理思维迅速失去多样性、状态无关和不完整的推理以及随后的无效行动。
为解决“思维崩溃”问题，研究提出了过程指导的必要性。
引入自动化修正器，在强化学习的每一步评估和优化代理的推理能力。
GTR框架同时训练推理和行动能力，无需密集、逐步的人工标注。

Cool Papers

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Text2BIM: Generating Building Models Using a Large Language Model-based Multi-Agent Framework

Authors:Changyu Du, Sebastian Esser, Stavros Nousias, André Borrmann

The conventional BIM authoring process typically requires designers to master complex and tedious modeling commands in order to materialize their design intentions within BIM authoring tools. This additional cognitive burden complicates the design process and hinders the adoption of BIM and model-based design in the AEC (Architecture, Engineering, and Construction) industry. To facilitate the expression of design intentions more intuitively, we propose Text2BIM, an LLM-based multi-agent framework that can generate 3D building models from natural language instructions. This framework orchestrates multiple LLM agents to collaborate and reason, transforming textual user input into imperative code that invokes the BIM authoring tool’s APIs, thereby generating editable BIM models with internal layouts, external envelopes, and semantic information directly in the software. Furthermore, a rule-based model checker is introduced into the agentic workflow, utilizing predefined domain knowledge to guide the LLM agents in resolving issues within the generated models and iteratively improving model quality. Extensive experiments were conducted to compare and analyze the performance of three different LLMs under the proposed framework. The evaluation results demonstrate that our approach can effectively generate high-quality, structurally rational building models that are aligned with the abstract concepts specified by user input. Finally, an interactive software prototype was developed to integrate the framework into the BIM authoring software Vectorworks, showcasing the potential of modeling by chatting. The code is available at: https://github.com/dcy0577/Text2BIM

传统的BIM建模过程通常需要设计师掌握复杂繁琐的建模命令，才能在BIM建模工具中实现他们的设计意图。这种额外的认知负担使设计过程复杂化，并阻碍了BIM和基于模型的设计在AEC（建筑、工程和建筑）行业中的采用。为了更直观地表达设计意图，我们提出了Text2BIM，这是一个基于大型语言模型的多智能体框架，可以从自然语言指令生成三维建筑模型。该框架协调多个大型语言模型智能体进行协作和推理，将文本用户输入转换为命令代码，调用BIM建模工具的API，从而在软件中直接生成具有内部布局、外部信封和语义信息的可编辑BIM模型。此外，引入了基于规则的模型检查器到智能体的流程中，利用预定义领域知识来指导大型语言模型智能体解决生成模型中的问题，并迭代提高模型质量。进行了大量实验来比较和分析在所提出的框架下三种不同的大型语言模型的表现。评估结果表明，我们的方法可以有效地生成高质量、结构合理的建筑模型，这些模型与用户输入的抽象概念相一致。最后，开发了一个交互式的软件原型，将框架集成到BIM建模软件Vectorworks中，展示了通过聊天进行建模的潜力。代码可在：https://github.com/dcy0577/Text2BIM（点击跳转）。

论文及项目相关链接

PDF Journal of Computing in Civil Engineering

Summary：传统的BIM建模过程需要设计师熟练掌握复杂繁琐的建模命令，在BIM建模工具中实现其设计意图。这种额外的认知负担使设计过程复杂化，阻碍了BIM和基于模型的设计在AEC行业中的应用。为了更直观地表达设计意图，我们提出了基于大型语言模型的多代理框架Text2BIM，可从自然语言指令生成三维建筑模型。该框架协调多个大型语言模型代理进行协作和推理，将文本用户输入转换为调用BIM建模工具API的命令，从而在软件中直接生成具有内部布局、外部包络和语义信息的可编辑BIM模型。此外，引入基于规则的模型检查器到代理工作流程中，利用预定义领域知识指导大型语言模型代理解决生成模型中的问题，并不断改进模型质量。评估结果表明，该方法可有效生成高质量、结构合理的建筑模型，符合用户输入的抽象概念。最后开发了一个交互式软件原型，将框架集成到BIM建模软件Vectorworks中，展示了聊天建模的潜力。

Key Takeaways：