👍 667
10/14 20:00
Policy-shift distillation reuses the difference between a teacher's log probabilities before and after reinforcement learning (RL) as supervision on student-generated trajectories. However, this endpoint policy shift cannot reveal whether local action preferences evolved consistently during teacher
中文介绍 Waypoint-OPD 通过教师的强化学习(RL)前后日志概率差异为 student 生成的轨迹提供监督,从而实现策略转移。然而,终点策略转移未能显示教师在本地行动偏好演变的一致性。提出的方法提供一种新的监督方式,有助于更好地理解和利用 RL 所引发的策略变化。意义:对于 Agent 和学习策略的优化具有重要意义。
👍 353
10/14 20:00
Spatial intelligence lets an agent perceive and act in the physical world, and many of its judgments must be made at interaction rates: which spatial relation holds, how far an object is, or where to place it. Vision-language models (VLMs) produce these answers by free-form text generation, paying f
中文介绍 MJev-Spatial 旨在通过本地注意力匹配提升智能体的空间智能,支持其在物理世界中的感知和行动。通过生成自由形式的文本答案来处理特定空间关系,如物体距离和放置位置,模型可以处理实时交互。此研究对视觉语言模型(VLMs)的能力有进一步增强。意义:推动了智慧型 Agent 和交互系统的发展。
👍 316
10/14 20:00
Computer use has reached a turning point: the focus is expanding from GUI-only interaction to systems that complete tasks on users' computers through visual and programmatic interfaces. We define a generalist computer-use agent (GCUA) as a system in which the model chooses how to observe, act, and o
中文介绍 OpenGCUA 定义了一种通用计算机使用代理,支持在用户计算机上通过视觉和编程接口完成任务。随着用户界面交互的扩展,该系统使模型能够灵活选择观察和行动方式,从而实现更高效的工作流程。此创新为普适智能体应用奠定基础。意义:对人机交互和智能助手领域的进展具有重要影响。
👍 285
10/14 20:00
Existing agent benchmarks typically evaluate agents on well-specified tasks defined by issue reports, specifications, or task tickets. Real-world customer delivery is different: forward-deployed engineers (FDEs) must clarify ambiguous requirements, work in live systems, and uncover unstated constrai
中文介绍 FDE-Gym 针对真实世界的客户交付挑战,提出了一种新的代理基准,以应对模糊需求和未陈述约束的情况。通过模拟复杂的交付场景,该平台帮助前线工程师快速应对变化,提升理解和解决现实问题的能力。这一框架为智能体及其在动态环境中的适应性提供了新的考量。意义:直接影响 AI 在复杂交互中的应用。
👍 202
10/13 20:00
Can vision-language models turn 2D observations into 3D understanding that supports precise action? This requires inferring object geometry, anticipating the effects of rotation, and refining actions through visual feedback. We introduce RoTA-Bench to evaluate these abilities through interactive 3D-
中文介绍 RoTA-Bench 旨在评估视觉语言模型在将二维观察转化为三维理解方面的能力,尤其是在预测转换效果和通过视觉反馈调整行动方面。该框架通过交互式 3D 投影评估,推动了模型在实际操作中的可用性和精确度。意义:对三维推理和视觉理解的进展具有重要信号。
👍 200
10/12 20:00
Harness self-evolution revises an agent's software scaffolding, including prompts, memory, workflows, and executable components, while keeping model weights fixed. We formalize a feedback-driven process in which accepted revisions persist across runs. The framework separates update targets from prop
中文介绍 Self-Evolving Agent Harness 提出了一个反馈驱动连续自我演化的框架,允许智能体在固定模型权重的情况下修改其软件骨架,并持续保留所接受的修订。这一方法提升了代理在不断变化环境中的适应性和灵活性,有助于推动自我改进策略的发展。意义:对推理与 Agent 系统的演化具有深远影响。
👍 154
10/13 20:00
Online reinforcement learning (RL) with verifiable rewards has driven rapid progress in mathematics, coding, and tool use, but scaling this success to GUI agents remains challenging due to the high cost of interactive environments and the scarcity of realistic training tasks. We introduce MAI-Gym, a
中文介绍 MAI-Gym 作为一个开放的全能平台,旨在解决移动和计算机环境中的图形用户界面(GUI)代理评估及强化学习训练挑战,通过提供可验证的奖励机制来推动性能改进。此平台为代理在复杂环境中的训练提供了新机遇。意义:促进了智能体在多种环境中的迁移学习。
👍 54
10/14 20:00
Vision-language-action (VLA) models transfer semantic knowledge acquired through vision-language pretraining to robot control, achieving strong performance across various benchmarks. However, most VLAs directly map observations to demonstrated actions without explicit supervision of intermediate phy
中文介绍 VLA-Δ 提出了通过物理状态转换学习来增强视觉语言行动(VLA)模型的能力,使其更有效地将语义知识转移到机器人控制中。通过显式监督中间物理状态的转换,该模型在多个基准测试中实现了强性能,推动了智能体在控制领域的应用。意义:对物理推理和控制理论的发展具有重要提示。
👍 54
10/14 20:00
Most existing agentic reinforcement learning methods assume single-session trajectories, where interaction proceeds within one continuously accumulated context. However, agentic techniques such as context compaction and subagent forking may produce trajectories spanning multiple sessions, creating a
中文介绍 Mosaic 引入了多会话智能增强强化学习方法,解决了传统方法只假设单一连续会话下的交互问题。通过结合上下文压缩和子代理分叉,该方法允许跨会话轨迹的灵活展现,促进了更复杂任务场景中的智能体学习。意义:对多任务学习和动态环境下的智能决策有启示作用。
👍 32
10/14 20:00
Generative visual verifiers play a crucial role in advancing multimodal intelligence. To enhance the robustness of visual verifiers in complex multimodal scenarios involving compositional requirements, we move beyond conventional binary verifiers that produce only True/False discrete judgments and i
中文介绍 OmniVerifier-C1 是一种分解的等级验证器,旨在提升生成视觉验证器在复杂多模态场景中的鲁棒性。区别于传统的二进制验证器,本研究引入了更灵活的判断机制,支持生成更全面的多样性评估,推动生成模型的智能化应用。意义:为多模态 AI 系统的可信度提升提供了重要信号。
👍 30
10/13 20:00
Humans interpret multimodal observations of the world and forecast how it will evolve. Recent multimodal generative systems have demonstrated similar capabilities: they can integrate multimodal context and synthesize visual outputs that depict changes in the world. To characterize this emerging traj
中文介绍 WorldCast 探索多模态生成系统在世界状态预测中的智能视觉生成能力,支持更好地解析环境变化并产生相应的视觉输出。该研究推进了多模态理解与预测的结合,有助于开发更加智能化的交互系统。意义:为智能视觉生成技术提供了新的研究方向。
👍 28
10/14 20:00
Data agents use code to process heterogeneous files and derive analytical insights. Scaling their training faces costly environment curation, privacy and intellectual-property restrictions, and difficulty verifying open-ended findings without known generative truth. Our core insight is to specify th
中文介绍 Data World 提出了一个全新的数据环境扩展策略,旨在解决数据代理在环境中处理异构文件和分析洞察时面临的挑战。通过特定的环境构建方法,该研究为大规模数据处理提供了新的思路,提升了代理在现实问题中的应用潜能。意义:对具备数据洞察能力的智能体设计具有重要意义。
👍 25
10/14 20:00
Large language models (LLMs) are expanding from assisting scientific research and engineering to autonomously developing and improving AI systems, advancing AI for AI (AI4AI) as a pathway toward recursive self-improvement (RSI). Workflow-driven machine learning engineering (MLE) systems provide a pr
中文介绍 MLExpert 致力于发展通用主义智能代理模型,使其能够自主进行机器学习工程。通过引入工作流驱动的方法,该模型能够更有效地在复杂的 ML 任务中进行自我改进,推动 AI4AI 的实践进程。意义:为自主机器学习的未来发展提供了有力信号。
👍 21
10/14 20:00
Large language model agents solve tasks through long sequences of tool calls, and the prevailing design has a single strong model generate every step, although many steps are routine enough for a much smaller model. Existing ways of sharing this work keep the strong model as the generator: routers a
中文介绍 Trajectory-Level Speculation 提供了一种优化工具使用代理的方案,通过语义验证实现更高效的长序列工具调用。本研究使得较小的模型可以承担较为常规的步骤,从而增强整个过程的高效性,推动加强学习的灵活运用。意义:对代理任务解决策略的多样性具有重要影响。
👍 11
10/14 20:00
The capabilities of an agent swarm depend on both its individual members and how they coordinate. Yet existing multi-agent systems often train agents within fixed orchestration structures, separating the learning of individual capabilities from the organization of collective behavior. We introduce m
中文介绍 Learning Self-Orchestration 在去中心化代理群体中提出了一种新的学习方式,旨在提升成员间的协调能力。通过在固定的编排结构中训练,研究引入了个体能力学习与集体行为组织的新视角,有助于提升智能体群体的整体表现。意义:对群体智能和协作学习研究提供了新的实验范式。
👍 10
10/12 20:00
The design of attention mechanisms in long-context large language models faces an ``impossible triangle'' among KV cache size, training and prefill FLOPs, and model quality. GQA achieves strong quality but incurs high KV cache overhead; alternative approaches such as MLA and MFA can compress the cac
中文介绍 Virtual Head Attention 研究了长上下文大语言模型中注意力机制设计的‘不可能三角’问题,提出了 GQA 在保持强质量的同时优化 KV 缓存。该方法提供了对缓存机制有效使用的新思路,为大型模型的训练效率提升奠定了基础。意义:推动了大规模语言模型的训练策略优化。
👍 8
10/11 20:00
Reinforcement learning with verifiable rewards (RLVR) typically uses small rollout groups per prompt, where repeated sampling from the same policy can yield highly similar trajectories, reducing within-group reward variation and weakening group-relative advantage signals. Existing remedies such as h
中文介绍 Beyond Policy Rollouts 探索嵌入空间方向探索的可能性,通过锚定噪声解决强化学习中可验证奖励的挑战。该研究降低了回滚组内的奖励差异,提供了一种新的探索和优化强化学习策略的方法,推动决策过程的多样性和有效性。意义:对强化学习理论与实践的革新具有深刻影响。
👍 8
10/14 20:00
Single image 3D generation aims to dress a given 3D mesh with relightable materials while faithfully preserving the visual details of a reference image. Existing methods either introduce artifacts when fusing inconsistent multi-view predictions or struggle to preserve fine details through implicit i
中文介绍 SUPRA-TEX 关注单幅图像的三维生成,通过为三维网格着色实现真实的视觉效果,并针对多视图预测的融合引入改进,减轻了视觉细节损失的问题。该研究推动了技术在三维生成领域的应用,展现出强大的实用潜力。意义:对三维视觉生成技术的发展具有重要启示。
👍 8
10/12 20:00
Cowork agents navigate dependencies across tools and files while adapting to changing user requirements and edits to shared workspaces. Scaling such environments requires maintaining consistency among task objectives, executable resources, user behavior, and success criteria. We introduce Cowork-Gym
中文介绍 Cowork-Gym 提供了一种新的环境合成方案,以支持代理在动态工作场景中的导航与适应。该研究通过维持任务目标与资源的一致性,提出了自适应的工作环境设计思路,有助于提升代理在复杂协作任务中的表现。意义:对工作环境智能化设计有助益,尤其是在实际工作场景中的应用。
👍 7
10/14 20:00
Computer-use agents increasingly tackle long-horizon tasks, where running a frontier model at every step is expensive and a small open-weight model alone often fails. Weak-to-strong escalation offers a middle ground where a weak agent acts by default and hands control to a strong one only when neede
中文介绍 LEASH 提出了一种不确定性感知的弱到强强升与监督方法,以应对计算机使用代理在长时间任务中的挑战。通过有效整合弱代理与强代理,该研究实现了动态任务控制过程中资源的最优化分配,推动了智能体的有效应用。意义:对智能体在长远任务中的适应能力具有重要影响。