论文

在视觉问答与视觉定位任务上对多模态紧凑双线性池化（MCB）进行了广泛评测，结果一致表明 MCB 优于去掉 MCB 的消融版本This work extensively evaluates Multimodal Compact Bilinear pooling (MCB) on the visual question answering and grounding tasks and consistently shows the benefit of MCB over ablations without MCB.

提出 WorldCycle，一种自验证 RL 框架，从普通动作序列中构建闭合动作循环及其重复执行，并优化两个互补奖励：空间闭合奖励（强制镜像的前向与反向片段之间的对称性）以及时间一致性奖励（对齐多次循环执行间的状态）。WorldCycle is introduced, a self-verifiable RL framework that constructs closed action cycles and their repeated executions from ordinary action sequences, and optimizes two complementary rewards: a spatial closure reward enforcing symmetry between mirrored forward and reverse segments, and a temporal consistency reward aligning states across repeated cycle executions.

提出 RSTG（Recovering Learning Signals via Adaptive Teacher Guidance），在最关键的位置选择性地、精确地施加蒸馏，显著优于朴素的 GRPO+OPD。RSTG (Recovering Learning Signals via Adaptive Teacher Guidance), which applies distillation selectively and precisely where it matters most, and substantially outperforms naive GRPO+OPD.

提出 CoCoEvolve，通过定义显式的一一对应关系，并利用表征间的一致性优化模型，在无需额外标注的前提下提升图表、表格与代码表征之间的跨模态一致性。This work introduces CoCoEvolve, a method to improve consistency across chart, table, and code representations by defining explicit one-to-one correspondences and optimizing models using agreement between representations, without additional annotations.

提出 DRIFT（Denoising Redirection via Input perturbation of the Flow-matching Trajectory），一种测试期通用对抗补丁，贴在机器人夹爪上，针对现成策略的去噪速度场进行攻击。This work introduces DRIFT (Denoising Redirection via Input perturbation of the Flow-matching Trajectory), a test-time universal adversarial patch placed on the robot's gripper that attacks the denoising velocity field of an off-the-shelf policy.

文章论证了 Transformers 可作为医学图像分割任务的强大编码器，并通过与 U-Net 结合，恢复了局部空间信息以增强更精细的细节。It is argued that Transformers can serve as strong encoders for medical image segmentation tasks, with the combination of U-Net to enhance finer details by recovering localized spatial information.

文章提出了 SegFormer，一个简单高效且强大的语义分割框架，将 Transformers 与轻量级 MLP 解码器统一，并在 Cityscapes-C 上展示了出色的零样本鲁棒性。SegFormer is presented, a simple, efficient yet powerful semantic segmentation framework which unifies Transformers with lightweight multilayer perception (MLP) decoders and shows excellent zero-shot robustness on Cityscapes-C.

分析表明，VisualBERT 能够在无任何显式监督的情况下将语言元素对应到图像区域，甚至对句法关系敏感，例如能够跟踪动词与其论元对应图像区域之间的关联。Analysis demonstrates that VisualBERT can ground elements of language to image regions without any explicit supervision and is even sensitive to syntactic relationships, tracking, for example, associations between verbs and image regions corresponding to their arguments.

在输入和输出直接进行 4 倍下采样与上采样的设定下，实验表明，基于纯 Transformer 的 U 形编码器-解码器网络优于完全卷积或 Transformer 与卷积相结合的方法。Under the direct down-sampling and up-sampled of the inputs and outputs by 4x, experiments demonstrate that the pure Transformer-based U-shaped Encoder-Decoder network outperforms those methods with full Convolution or the combination of transformer and convolution.

BLIP 通过引导式 caption 方式有效利用含噪网络数据，由 captioner 生成合成 caption，并由 filter 去除噪声样本；在以零样本方式直接迁移到视频-语言任务时，展现出强大的泛化能力。BLIP effectively utilizes the noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones, and demonstrates strong generalization ability when directly transferred to video-language tasks in a zero-shot manner.

本文提出了首个用于睡眠阶段分类的深度学习方法，无需计算频谱图或提取手工特征即可端到端学习，利用了全部多变量多模态 PSG 信号（EEG、EMG、EOG），并能利用每个 30 秒窗口数据的时序上下文。This work introduces here the first deep learning approach for sleep stage classification that learns end-to-end without computing spectrograms or extracting handcrafted features, that exploits all multivariate and multimodal polysomnography (PSG) signals (EEG, EMG, and EOG), and that can exploit the temporal context of each 30-s window of data.

PaDoc 是一个布局驱动的解析器，将预测布局视为共享页面表示上的分支结构，在五个并发级别下均为最快的 end-to-end 解析器。PaDoc is proposed, a layout-grounded parser that treats the predicted layout as a branching structure over a shared page representation that is the fastest end-to-end parser at five concurrency levels and is the fastest end-to-end parser at five concurrency levels.

World-to-Wrist VLA (W2-VLA) 是一个用于细粒度机器人操作的 VLA 模型，具备任务条件下的未来腕部建模；W2-CoT 是一个合成流水线，生成描述操作进度、物理过渡线索和腕部局部证据的结构化标注。World-to-Wrist VLA (W2-VLA), a VLA model for fine-grained robot manipulation with task-conditioned future wrist modeling, and W2-CoT, a synthesis pipeline that produces structured annotations describing manipulation progress, physical transition cues, and wrist-local evidence.

SmartMage 是一个统一的 MLLM，动态调度异构模态以实现语义感知的 3D 场景理解，在五个 3D 场景理解基准上达到 SOTA，并在仅 RGB 视频理解基准上取得具有竞争力的结果。SmartMage is proposed, a unified MLLM that dynamically orchestrates heterogeneous modalities for semantic-aware 3D scene understanding and achieves state-of-the-art performance across five 3D scene understanding benchmarks, and attains competitive results on RGB-only video understanding benchmarks.

识别出像素级嵌入的不可见元数据痕迹，表明大规模语义监督（无论是类别标签还是亿级规模的 caption）在预训练中会自然引发元数据-语义相关性，导致模型将低层信号转化为预测特征。Invisible metadata traces embedded at the pixel level are identified, suggesting that large-scale semantic supervision, whether through categorical labels or billion-scale captions, naturally induces metadata-semantics correlations during pretraining, leading models to convert low-level signals into predictive features.

结果表明，显式且权威的状态建模为可扩展、一致的多智能体世界仿真提供了可行基础。The results show that explicit, authoritative state modeling provides a practical foundation for scalable and consistent multi-agent world simulation.

本工作指出，这些局部 patch 内部的注意力同样是构建高性能视觉 Transformer 的关键，并探索了一种新架构，即 Transformer iN Transformer (TNT)。It is pointed out that the attention inside these local patches are also essential for building visual transformers with high performance and a new architecture, namely, Transformer iN Transformer (TNT), is explored.

本文提出了一种通用的无监督学习方法——对比预测编码（Contrastive Predictive Coding），用于从高维数据中提取有用的表征，并在语音、图像、文本和 3D 环境中的强化学习四个不同领域取得了出色的性能。This work proposes a universal unsupervised learning approach to extract useful representations from high-dimensional data, which it calls Contrastive Predictive Coding, and demonstrates that the approach is able to learn useful representations achieving strong performance on four distinct domains: speech, images, text and reinforcement learning in 3D environments.

SimWAM 是一种简洁而有效的 WAM，利用未来视频预测作为训练期监督信号，通过 joint flow matching 联合训练一个预训练视频专家与一个轻量级动作专家，并采用强化学习在轨迹模仿之上优化组合式驾驶奖励。SimWAM, a simple yet effective WAM that leverages future-video prediction as a training-time supervision signal, co-trains a pretrained video expert and a lightweight action expert with joint flow matching and applies reinforcement learning to optimize a compositional driving reward beyond trajectory imitation.

通过引入扩散卷积运算，本文展示了如何从图结构数据中学习基于扩散的表示，并将其作为节点分类的有效基础。Through the introduction of a diffusion-convolution operation, it is shown how diffusion-based representations can be learned from graph-structured data and used as an effective basis for node classification.

本文提出新的计算机视觉基础模型 Florence，通过融入来自 Web 规模图文数据的通用视觉-语言表示，将表征范围从粗粒度（场景）扩展到细粒度、从静态（图像）扩展到动态（视频）、从 RGB 扩展到多种模态（描述、深度等）。This work introduces a new computer vision foundation model, Florence, to expand the representations from coarse (scene) to fine, from static (images) to dynamic (videos), and from RGB to multiple modalities (caption, depth), by incorporating universal visual-language representations from Web-scale image-text data.

本文提出 Enfold，将构建未来的计算转移到由当前视觉上下文和语言指令预测出的表征中，并把世界生成器重塑为预测控制表征的来源，前提是其内部结构可被折叠（enfold）到当下。This work presents Enfold, which transfers this computation that constructs a future into a representation predicted from the current visual context and language instruction, and recast a world generator as a source of predictive control representations if its internal structure can be enfolded into the present.

本文提出 DuplexGen，一个通过少量 slot 级人类偏好标注对 LLM 预测进行校准从而生成具有场景自适应轮换特性的对话框架；结果表明，使轮换合成具备场景特异性的关键是人类校准，而非单纯的语料规模或提示设计。DuplexGen is introduced, a framework for generating dialogues with scenario-adaptive turn-taking by calibrating LLM predictions against a small set of slot-level human preference annotations, and results show that human calibration, not corpus scale or prompt design alone, is what allows turn-taking synthesis to be scenario-specific.

RynnValue 是一个用于机器人操控的开源价值基础模型，用时间距离（即从观测到语言指定目标的有向 cost-to-go）替代任务内部锚点，将时间距离确立为通用机器人策略的可扩展监督目标和实用奖励接口。RynnValue, an open-source value foundation model for robotic manipulation that replaces task-internal anchors with temporal distance, the directed cost-to-go from an observation to the language-specified goal, establishes temporal distance as a scalable supervision target and practical reward interface for generalist robot policies.

Steerling-8B 在与训练计算量多 2–16 倍的开源同侪模型对比中仍保持竞争力，表明存在一种不同的可扩展范式：可解释性可以被设计进训练过程中，并随规模放大而提升。Steerling-8B remains competitive with open peer models trained on substantially 2-16x more compute, suggesting a different scaling paradigm: interpretability can be designed into training, and it improves with scale.

一个三阶段框架，用于减少建议编辑与当前图像之间的视觉不一致性，并将推荐 CTR、图像带走率以及用户平均对话轮次分别显著提升 39.90%（所有 p<0.05）。A three-stage framework to reduce visual inconsistencies between suggested edits and the current image, which significantly improves recommendation CTR, image take-away rate, and average conversation turns per user by 39.90% (all p<0.05).

本文提出 Ego-OSCAR，一种用于野外自我中心数据采集的开源硬件、低成本、头戴式立体惯性采集设备，旨在成为众包自我中心采集中最廉价且可辩护的载体，降低任何团队大规模采集自我中心数据的启动门槛。Ego-OSCAR is presented, an open-hardware, low-cost, head-mounted stereo-inertial capture device for egocentric data collection in the wild that aims to be the cheapest defensible substrate for crowdsourced egocentric capture, and to lower the activation energy for any team that wants to collect egocentric data at scale.

该形式化方法将短语定位、指代表达定位以及开放词汇检测等常见 grounding 任务统一起来，将文本分割、图像分割以及跨模态对齐视为单一的对应预测问题。This formulation unifies common grounding tasks, including phrase grounding, referring expression grounding, and open-vocabulary detection, by treating text segmentation, image segmentation, and cross-modal alignment as a single correspondence prediction problem.

Atelier 是一种面向艺术家风格图像生成的捷径感知控制状态规划框架，可提升艺术家级风格保真度，更忠实地保持源结构，并相较提示工程、检索增强与通用 agent 基线大幅减少捷径替换。Atelier, a shortcut-aware control-state planning framework for artist-grounded image generation, improves artist-level style fidelity, preserves source structure more faithfully, and substantially reduces shortcut substitution compared with prompt-engineered, retrieval-augmented, and general-purpose agent baselines.

本文表明，利用在大规模数据上预训练的 HPE 模型先验知识作为骨干网络可在该任务上取得高性能，并验证了手部可见性检测器在通过 2D 关键点多视角三角化进行 3D 手部姿态标注的下游任务中的有效性。It is shown that leveraging the prior knowledge of HPE models pretrained on large-scale data as a backbone yields high performance in this task, and the utility of Hand Visibility Detector on a downstream task of 3D hand pose annotation via multi-view triangulation of 2D keypoints.

AtlasVLA 是一种新框架，通过持久化的世界-自我状态从直接反应式操作转向主动推理，显著优于多视角基线，在 LIBERO-Long 上取得 9.4% 的绝对成功率提升，在真实世界长周期任务中取得 17.5% 的提升。AtlasVLA is a novel framework that transitions from direct reactive manipulation to proactive reasoning through a persistent world-ego state and decisively outperforms multi-view baselines, yielding absolute success rate improvements of 9.4% on LIBERO-Long and 17.5% in real-world long-horizon tasks.

本文提出 Simplax，一种精确的 Dirichlet-类别增强方法，将每个被破坏的类别状态与一个辅助的 simplex 值变量耦合，同时保留原始均匀扩散过程作为其类别边缘分布。Simplax is introduced, an exact Dirichlet--categorical augmentation that couples each corrupted categorical state with an auxiliary simplex-valued variable while preserving the original uniform diffusion process as its categorical marginal.

本文提出提示式注视目标估计（PGE）任务，一种用于注视分析的端到端、概念驱动新范式，并推出首个为 PGE 设计的模型 GazeAnywhere，它使用基于 Transformer 的检测器融合冻结编码器特征，同时解决主体定位、画内/画外存在性以及注视目标热图估计。The Promptable Gaze Target Estimation (PGE) task is introduced, a new end-to-end, concept-driven paradigm for gaze analysis and GazeAnywhere, the first model designed for PGE, uses a transformer-based detector to fuse features from frozen encoders and simultaneously solves subject localization, in/out-of-frame presence, and gaze target heatmap estimation.

本文提出一种用于机器人操作的动作条件视频世界模型，给定观测帧、语言指令以及由末端执行器位姿和夹爪状态组成的预定动作序列，预测对应的未来观测结果。An action-conditioned video world model for robotic manipulation that, given an observed frame, a language instruction, and a prescribed action sequence comprising end-effector poses and gripper states, predicts the resulting future observations is presented.

本文提出 UniSwap，这是首个用于说话视频中流式联合音视频身份替换的框架，并引入 swap-and-reconstruct 流程，从真实片段中移除视觉和声音身份，同时使用原始片段作为重建目标。This work presents UniSwap, the first framework for streaming joint audio-visual identity replacement in talking videos, and introduces a swap-and-reconstruct pipeline that removes visual and vocal identity from real clips and uses the original clips as reconstruction targets.

本文认为关键在于设计合适的 search-state 表示，将 LLM 的内部知识与 visual-token 剪枝的结构要求和约束相连接，并提出 AutoPrune，一种用于 LLM 驱动的 visual-token 剪枝策略设计的免训练框架。This paper argues that the key lies in designing an appropriate search-state representation that connects the internal knowledge of LLMs with the structural requirements and constraints of visual-token pruning, and proposes AutoPrune, a training-free framework for LLM-driven visual-token pruning policy design.