👍 228
10/07 20:00
Online reinforcement learning (RL) fine-tunes diffusion models with black-box rewards, but reverse-process methods such as Flow-GRPO are limited in efficiency by poor credit assignment, while forward-process methods such as DiffusionNFT are efficient yet unstable without carefully tuned exponential-
中文介绍 论文提出了一种在线强化学习方法改进扩散模型,克服了先前反向方法因信用分配差而导致的效率低下问题。通过引入新的前向处理方法,确保了在黑箱奖励下的高效且稳定的模型微调。该方法在性能和适用性上显著提高,能够有效应用于扩散模型的训练和优化。意义:推动了强化学习在模型训练中的应用,并影响了生成模型的优化方向。
👍 201
10/09 08:48
Multimodal language models (MLLMs) exhibit promising spatial reasoning capabilities, yet the internal mechanisms supporting them remain poorly understood. We uncover S-Space, a linear representation subspace in which object-token representations encode continuous coordinates along horizontal, vertic
中文介绍 研究揭示了多模态语言模型(MLLMs)内部存在的S-Space子空间,使得对象表示与空间坐标的连续性相结合,从而提升了模型的空间推理能力。这一新发现为理解多模态模型的运作机制提供了新的视角,可能推动更高效的模型设计与应用。意义:增强了多模态推理的研究,对智能体和推理应用有重要影响。
👍 83
10/07 20:00
Computer-use agents (CUAs) are rapidly improving, yet synthesizing diverse and reliable software environments at scale remains challenging. Native application environments require substantial construction and integration effort, which limits application coverage and increases the risk of application
中文介绍 为了解决计算机使用代理(CUAs)在多样化软件环境中训练的挑战,本文提出了SoftwareGym,通过合成不同软件环境来提高训练的灵活性和覆盖率。这种方法能够显著减少构建和整合环境的时间及成本,从而提高代理的实用性。意义:为计算机使用代理的培训方法注入新的活力,提升了其在实际应用中的可用性。
👍 57
10/05 20:00
Multimodal large language models have become the dominant paradigm for visual understanding, but incur substantial costs by encoding inputs into dense, fixed-size patch tokens. However, visual information is unevenly distributed: some regions require fine-grained detail, while others admit compact r
中文介绍 VisionWeave引入了一种新的表示方法,旨在提供大规模多模态语言模型中的弹性视觉表示,降低输入密度和固定大小标记带来的成本。该方法优化了视觉信息的编码,使得不同区域的信息加权合理,提升了视觉理解能力。意义:为多模态模型提供了新的视觉表示策略,推动了视觉计算的灵活性及其在智能应用中的可能性。
👍 42
10/07 20:00
Adversarial games have driven advances from heuristic search to reinforcement learning, yet learning and adapting strategies from limited samples remain challenging. AI agents offer an alternative by turning game experience into revisions of executable policies. Building on heuristic learning (HL),
中文介绍 探讨了人工智能代理在长期游戏竞争中的启发式学习能力,提出了一种新方法来根据有限样本调整策略。该研究表明,AI代理可以通过游戏体验不断修正可执行策略,从而提升其在复杂环境中的应对能力。意义:为增强AI在动态环境中学习能力提供了理论基础,可能影响到游戏和决策领域的未来应用。
👍 39
10/08 20:00
Computer-use agents can perform increasingly complex GUI tasks, yet unfamiliar software and workflows requiring human domain knowledge remain challenging. Human demonstrations convey procedural knowledge through the same GUI interaction space, but reuse requires interpreting actions in context and s
中文介绍 Work2Skill提出了一种新的方法,通过捕捉人类在GUI中的交互经验来引导计算机使用代理的技能生成。这一方法能够有效提取人类在复杂软件环境中的行为模式,从而提升机器在未见环境中的适应能力,减少其对人类知识的依赖。意义:为GUI任务中的人机协作开辟了新思路,有助于提升智能代理的实际操作能力。
👍 26
10/07 20:00
Speculative decoding accelerates large language model inference by using a lightweight drafter to propose multiple tokens that are subsequently verified in parallel by the target model. Recent diffusion-based drafters make this paradigm particularly attractive: deep attention-based neural backbones
中文介绍 提出了一种情境修正的并行草拟方法,以加速大规模语言模型的推理过程。该方法使用轻量级草拟器来生成多个候选标记,然后通过目标模型进行并行验证,从而提高推理效率,特别是在扩散模型环境下。意义:推动了推理优化技术的发展,对自然语言处理任务在速度和准确性上的提升有积极影响。
👍 20
10/07 20:00
Static coding benchmarks are fixed at release. Over time their tasks leak into training data, and stronger agents eventually saturate them. We argue that a benchmark should instead aim to be self-scaling: its tasks and oracles are created at evaluation time, so no content exists in advance to leak,
中文介绍 SWE-Duel倡导建立自我扩展的编码代理评估体系,通过动态生成的任务和评价指标来避免静态基准的不足。这一思路可以确保评估过程的公平性和相关性,避免训练数据的泄露问题。意义:为编码智能体的能力评估提供了一种创新思路,对后续人工智能代理评测具有指导意义。
👍 19
09/28 20:00
Process reward models (PRMs) have become a key component for LLMs, as their step-level feedback supports both post-training and test-time reasoning. However, training strong PRMs remains costly: human step annotation is difficult to scale, while Monte Carlo estimation is computationally expensive an
中文介绍 本研究提出了一种通过结果导向的强化学习诱导过程监督模型的新方法,以强化大规模语言模型中的步骤级反馈能力,旨在降低人工标注的开销。该方法通过改进Monte Carlo估计,增强了模型在复杂任务中的适应性与准确性。意义:为增强模型的推理能力提供了新的路径,可能影响长期任务的设计与执行。
👍 18
10/05 20:00
Automated research systems increasingly run LLM agents over long horizons, but more inference does not by itself produce more progress: agents replay growing histories, duplicate one another's work, or stop experimenting while token consumption continues. Yet most evaluations use short budgets or be
中文介绍 研究提出了一种无状态语言代理的概念,旨在扩大长时间自动化研究中的进展。该方法通过优化推理预算,使代理能够在长时间内有效探索,而不只是重复之前的工作。这种策略在实际应用中提高了代理的研究效率。意义:推动了自动化研究系统的演变,为智能代理的长期表现提供了新视角。
👍 18
10/08 20:00
In this paper, we study physically grounded articulated 3D asset generation---moving the goal from reproducing how objects look to capturing how they move and function. Inspired by the literature on procedural and agentic generation, we present Artisan, a system that extends the latest method Articr
中文介绍 本文探讨了物理驱动的关节3D资产生成,将重点从物体外观转向如何模拟其运动和功能。通过引入Artisan这一系统,研究提出了一种全新的多代理管道以生成复杂的3D资产,推动了程序生成和智能生成的结合。意义:促进了3D模型生成领域的创新,对增强真实感和交互性具有重要意义。
👍 16
10/03 20:00
Recent GPU kernel optimization agents retain what they learn in knowledge bases or as distilled skills. Kernel benchmarks score each attempt's implementation for correctness and speed but leave the reuse value of retained experience unmeasured. We introduce MetaKernelBench, which measures whether ex
中文介绍 MetaKernelBench工具旨在评估GPU内核优化代理的知识迁移能力,特别是自动化的再利用能力。它提供了一种新的衡量标准,以评估保持学习经验的再利用价值,为优化过程提供指导。意义:提高了GPU优化代理研究的深度与广度,影响未来在高性能计算领域的应用。
👍 16
10/07 20:00
Language model agents increasingly operate over long interaction streams, requiring online adaptation to distribution shifts through continual learning. However, existing agent harnesses typically either evolve their states only through forward information flow or rely on additional replay or valida
中文介绍 提出了一种新的持续学习框架,支持在长交互流中适应分布变化,专注于代理的在线学习能力。研究表明,该框架有效结合了前向信息流和回放机制,大幅提升了学习效率和代理表现。意义:为智能体在动态环境中长期学习能力的增强提供新方法,推动了持续学习研究的发展。
👍 14
10/07 20:00
Humans perceive far more in a scene than what is explicitly depicted: a single glance captures past causes and future trajectories; a quick peek determines if a vehicle can fit between two parked cars; a few seconds of video reveals who holds authority in a room; and a fleeting clip highlights subtl
中文介绍 针对人类在视觉推理中的直观能力,本文提出了一组基准以衡量多模态模型在复杂场景下的理解能力。通过评估模型在捕捉因果关系和空间推理上的表现,该研究深入探讨了直观推理对视觉理解的重要性。意义:推动智能体的视觉理解发展,对人机交互和推理能力的提升具有重要影响。
👍 13
10/07 20:00
Data video is a media form that integrates data visualization with video narrative, widely adopted in news reporting and business analysis. Compared with general video understanding, data video understanding places greater emphasis on accurately reading data from animated charts, integrating evidenc
中文介绍 DataVista提出了一种新方法,以分析和理解多模态视频中的数据可视化内容,该领域的研究尚处于起步阶段。相比于传统视频理解,这一方法突出了数据解读的准确性和实用性,有助于提升商业分析和新闻传播中的应用效果。意义:为多模态理解提供新视角,对实际应用具有深远影响。
👍 13
10/08 20:00
Decoder hidden states from different layers of large language models (LLMs) often encode complementary information. However, existing speculative decoding methods typically rely on a small set of target-model layers chosen using fixed, position-based heuristics. Selecting an optimal subset of layers
中文介绍 论文分析了大型语言模型(LLM)的解码器隐藏状态,提出了一种新的选择层的方法,以优化推测解码过程中的信息获取。通过借助各层的互补信息,该研究为提升LLM的推理效率提供了理论支持。意义:推动了解码技术的发展,增强了在多模态推理中的应用潜力。
👍 13
09/28 20:00
Video understanding agents acquire evidence through an executable harness that controls what they observe and how they use those observations. However, execution traces contain only the evidence acquired by the current harness, leaving competing explanations for failure unresolved and limiting the b
中文介绍 Video-RSI研究通过进化执行工具来优化视频理解代理的自我提升能力,旨在提高其在多样化任务中的适应性。该方法通过动态优化追踪执行痕迹来增强代理的学习反馈能力,从而拓宽应用范围。意义:推动了视频理解领域的智能提升,为长时间观察任务提供新思路。
👍 13
10/08 20:00
Large language models (LLMs) are increasingly explored for graph understanding and decision-making, while System One models such as Jev select directly from supplied options. However, the capabilities of System One models on graph-related tasks remain unclear. We introduce GraphDecide, a model-indep
中文介绍 GraphDecisionBench旨在评估系统一模型在图相关任务上的表现,探索大规模语言模型在决策过程中的应用。通过提供标准化测试,研究深入分析了系统一模型在图理解和决策中的能力,提升了模型可解释性。意义:推动了图任务的智能决策研究,对图计算与推理应用有重要影响。
👍 12
09/29 20:00
Foundation models can serve as clinical agents through tool-use harnesses. However, conventional medical benchmarks assess reasoning over preselected evidence rather than the ability to seek it across clinical records and longitudinal imaging. We propose CASE: a series of role-specific Clinical Agen
中文介绍 提出了CASE模型,旨在将基础模型转变为临床代理,通过工具使用强化其在医学推理中的能力。研究强调了对证据的主动获取而非被动选择,提升了医务决策的效率。意义:为临床代理的智能化提供新方向,增强了医学领域内AI的实际应用能力。
👍 12
10/08 20:00
Efficient visual tokenization requires balancing representation compactness with the preservation of fine-grained image content. Conventional grid-based tokenizers allocate tokens uniformly across spatial locations despite non-uniform visual complexity, whereas compact 1D tokenizers aggregate global
中文介绍 MixTok提出了一种自适应视觉标记化方法,通过全局1D标记与稀疏2D精化来平衡表示的紧凑性与细粒度图像内容的保留。这种方法有效优化了视觉信息的解析与存储,提升了模型的图像理解能力。意义:为视觉处理提供了新策略,推动了视觉生成及其在多个应用中的潜力。