👍 272
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 本研究揭示了多模态语言模型(MLLMs)中的S-Space,一种线性表示子空间,其中物体标记表示沿水平方向和垂直方向编码连续坐标。通过探索其内部机制,论文旨在改善对空间推理能力的理解。这一发现可能对机器人视觉和自主系统的空间理解具有重要意义。
👍 247
09/29 20:00
Precise grounding matters. It specifies which object is the target and where that object is, even in clutter and for tiny objects, and it has to be fast enough for closed-loop control. Yet vision-language-action (VLA) and world-action models (WAMs) take perception from general-purpose vision-languag
中文介绍 为实现快速且准确的物体定位与控制,本文提出GroundingPI,一个以视觉原语为基础的 grounding 模型。该模型针对视觉语言动作(VLA)和世界动作模型(WAMs)的局限,优化了物体识别及其空间位置的准确性,为物理智能的发展提供了新的基础。其方法能有效处理拥挤环境下的小物体定位。
👍 215
10/07 20:00
Large Language Models (LLMs) increasingly ship with explicit "thinking modes", yet their counterpart, "no-thinking", has received far less attention. We study LLMs' no-thinking behavior along two axes. a. How to measure no-thinking? Prior work typically defines no-thinking through proxies such as a
中文介绍 本文研究了大型语言模型(LLMs)在被要求不进行思考时的表现,提出了新的度量方法来评估其无思考状态。通过探索其反应特征,论文揭示了LLMs潜在的内在推理过程,并对理解这些模型的工作机制具有重要意义。
👍 201
10/08 20:00
As the production of software passes into the hands of software itself, coding agents are increasingly commissioned to build agents fitted to their users' own workflows. This delegation carries a quiet assumption, that the delivered agent will faithfully serve the requirement it was built for, rathe
中文介绍 本文探讨了在软件生成中,编码代理如何根据用户的工作流程构建需求代理。通过对实现匹配用户期望的算法设计,研究表明现有代理可通过学习用户偏好来更有效地服务于用户。其成果有助于推动自动化编程工具的发展。
👍 133
10/08 20:00
Terminal agents are moving beyond local operations toward completing real-world professional tasks. Short trajectories can demonstrate commands and tool calls, but they do not fully show how agents accumulate evidence through exploration, use it to inform judgments, continually adjust strategies bas
中文介绍 为了使终端代理在复杂任务中表现更出色,论文提出了一种新的数据扩展策略,允许代理在长时间的任务中通过探索积累证据并调整策略。该方法的有效应用有助于提升代理在实际环境中的能力和适应性,为复杂的自动化任务提供了新的思路。
👍 78
10/06 20:00
Video interactive storytelling enables viewers to actively steer how a video unfolds. However, once we allow viewers to intervene during generation, a new challenge arises: The viewer's request can have latent dependencies on both the grounding source and the current rendered video state. These depe
中文介绍 本文研究了视频互动叙事中的上下文基础,使观众能够影响故事的展开。然而,当观众介入生成过程时,带来了新的挑战:观众请求与基础和当前视频状态之间存在潜在依赖关系。这一研究不仅丰富了视频生成的概念,也为未来的动态叙事提供了启示。
👍 73
09/29 20:00
Autoregressive (AR) grounding models serialize spatial predictions, introducing sequential latency and imposing a causal order on output tokens. We view grounding as visual evidence extraction: objects, locations, and spatial relations are jointly constrained by the image and query, yet their depend
中文介绍 为了提高视觉 grounding 速度,本文提出GroundAnything模型,兼顾精确的空间预测与低延迟的并行解码。通过视图与查询的联合约束,研究构建了快速且准确的物体识别机制,为实时视觉处理提供了新的技术路径。
👍 34
10/08 20:00
Attention Residuals (AttnRes) enables content-dependent retrieval over network depth, which can be viewed as single-stream softmax attention along the depth axis. Inspired by the success of multi-head attention along the sequence axis, we introduce Multi-Stream Attention Residuals (MSAR), an extensi
中文介绍 本论文提出了多流注意力残差(MSAR)模型,能够在网络深度上实现内容依赖的检索,灵感来源于序列轴上的多头注意力。这种新方法扩展了传统的注意力机制,为信息检索和处理提供了更强大的工具,对深度学习领域非常重要。
👍 30
09/26 20:00
Personal Graphical User Interface (GUI) agents rely on interaction history to infer what a user wants from ambiguous instructions and to anticipate recurring routines. Existing approaches retrieve task-relevant history and append it to the policy's context, implicitly assuming that experience releva
中文介绍 针对个人图形用户界面(GUI)代理在模糊指令下如何推断用户需求的问题,本文提出了经验激活机制,允许代理从历史交互中获取信息。研究发现现有方法在处理一般性任务时存在假设偏差,为增强用户体验提供了新的视角。
👍 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 通过并行多草稿上下文校正,本文提出了一种加速大型语言模型推理的规范性解码方法。依赖于新兴的基于扩散的草稿器,这一策略显著提升了模型响应速度,对后续模型设计与应用具有深远影响。
👍 21
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 研究提出了一项基准,以评估多模态模型在直观视觉推理中的能力。该模型模拟人类对场景的感知,包括时间相关信息,从而揭示稳定性和动态性的平衡。这项工作对增强机器的直观理解能力提供了前沿参考。
👍 20
09/28 20:00
Language-model agents are usually trained by reinforcement learning from one reward per episode, and privileged self-distillation enriches it by letting the same policy, given a skill, teach its skill-free self through token probabilities. However, we identify two phenomena that question this channe
中文介绍 本文提出了一种特权表示在策略自蒸馏中的应用,弥补传统强化学习中每次仅基于单一回报评估策略的不足,通过让具有技能的策略指导无技能策略的学习,以提高代理的效果。这一策略有助于强化学习领域的智能体自我改善。
👍 20
09/28 20:00
Historical street-view imagery records urban evolution, but uneven coverage leaves substantial gaps in historical records. Generating plausible past appearances requires restoring changed structures while preserving persistent scene content. We construct VIGOR-his, a decade-spanning cross-view datas
中文介绍 针对城市历史街景图像的不均衡记录,本文构建了跨十年的跨视角数据集,提出了奖励引导的编辑方法,旨在生成可信的过去场景。这一工作为城市历史的研究与重塑提供了新的数据来源与技术手段。
👍 17
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 本文提出MetaKernelBench,测量GPU内核知识转移的效果,超越传统的实现准确性与速度评估,探讨保留经验的复用价值。这一新方法为GPU优化领域的研究提供了重要的评估标准与启示。
👍 16
10/08 20:00
When solving complex spatial reasoning tasks, ordinary humans naturally revisit the scenes from task-relevant viewpoints and mentally reconstruct the spatial layouts from those viewpoints. But can Large Vision-Language Models (LVLMs) proactively think in the same way? We term this ability Active Spa
中文介绍 论文探讨了通过主动空间思维解决复杂空间推理任务的能力,提出了一种基于大规模视觉-语言模型(LVLMs)的内部化方案,以支持例如场景重建等复杂操作。这对增强模型在空间推理任务中的智能性具有重要的推动作用。
👍 11
10/06 20:00
The evaluation of coding agents generally strives to be as realistic as possible. In our study, we collect 4,782 agent sessions of real software engineers in JetBrains IDEs, which we call Production Sessions. Since our subject is interactive agents, we study the sessions with at least three user mes
中文介绍 本文收集了真实软件工程师在JetBrains IDE中的交互会话数据,探讨编码代理评估的现实性。同时,提出的方法有助于通过更好地匹配用户的任务流程,提升软件开发工具的有效性,为智能编程的未来发展提供了重要依据。
👍 11
10/07 20:00
Knowledge distillation (KD) transfers knowledge from stronger Teacher models to weaker Student models, but most methods require training the Student parameters, thereby binding the distilled knowledge to a specific architecture and checkpoint. This implicit representation is difficult to interpret o
中文介绍 知识蒸馏(KD)方法通常要求学生模型参数训练,难以利用通用架构之间的知识传递。本文提出了一种普适文本教学方法,旨在加强知识转移的灵活性,推动机器学习领域知识利用的更广泛应用。
👍 10
10/01 20:00
We reconstruct high-fidelity 3D scene meshes from posed multi-view images without per-scene optimization, across scales ranging from single objects to large outdoor scenes. Per-scene optimization methods lack the learned 3D prior needed when observations are sparse or surfaces are glossy or transpar
中文介绍 本研究提出了一种在任意分辨率下从图像生成高保真样貌的重建方法,能够在不同规模的场景中进行有效的3D重建,弥补传统方法在稀疏观测和表面光滑处理上的不足。这一创新为计算机视觉中的三维重建技术提供了新的思路。
👍 10
10/06 20:00
Vision-and-Language Navigation (VLN) has largely focused on a single agent following a single instruction, yet many real-world applications require teams of robots to tackle tasks beyond the capabilities of any individual agent. We present Systematic Multi-Agent Vision-and-Language Navigation, provi
中文介绍 本文提出了一种系统的多智能体视觉与语言导航方法,旨在解决单代理任务无法满足的复杂多代理合作问题。这一方法为实际应用场景中多个机器人协同工作提供了新的解决方案,对未来的智能体协作机制具有重要影响。
👍 7
10/08 20:00
Large language models have achieved remarkable advances in complex reasoning. However, scaling reasoning post-training with ever-larger corpora can substantially increase training cost without proportional performance gains. In this study, we therefore ask: how far can reasoning supervision be compr
中文介绍 本文提出了一种高效推理的进阶收缩方法,旨在通过不增加训练成本提升大型语言模型的推理能力。这项研究对优化后续模型性能和减轻资源投入具有重要意义,推动高效推理技术的研究进展。