论文

本文提出 AV-GRPO，一种以模态为锚点的在线 diffusion RL 框架，以及 5DAV，一种解耦的、难度可调节的训练数据集；在 LoRA 与全量 fine-tuning 条件下，其在生成质量、语义对齐和跨模态同步性上均优于 LTX-2.3。This work proposes AV-GRPO, a modality-anchored online diffusion RL framework, and 5DAV, a decoupled, difficulty-controllable training dataset that outperforms LTX-2.3 in generation quality, semantic alignment and cross-modal synchronization under LoRA and full fine-tuning.

本文提出一种可调深度网络架构，借助适配器残差模块可在线切换至不同视觉域；同时引入 Visual Decathlon Challenge 基准，用于评估表征同时捕获十个差异显著视觉域的能力，并衡量其跨域均匀识别的能力。This paper develops a tunable deep network architecture that, by means of adapter residual modules, can be steered on the fly to diverse visual domains and introduces the Visual Decathlon Challenge, a benchmark that evaluates the ability of representations to capture simultaneously ten very differentVisual domains and measures their ability to recognize well uniformly.

TrackEverything 是首个在 40 GB GPU 显存内即可在超过 1000 帧的视频中跟踪所有可见点的 3D tracker；本文还提出 3D WAFT，用场景点云内的高效特征采样取代了显存开销巨大的 4D correlation volumes。TrackEverything is the first 3D tracker capable of tracking all visible points across videos exceeding 1000 frames within 40 GB of GPU memory, and 3D WAFT is proposed, replacing memory-prohibitive 4D correlation volumes with efficient feature sampling in the scene cloud.

本文提出一条全自动的数据生成流程，可在无需任何人工标注的情况下生成图像对之间的逐点感知距离标签，并证明 diffusion trajectory 与人类视觉系统高度一致。This paper proposes a fully automated data generation pipeline that generates pointwise perceptual distance labels between image pairs without any human annotation, and demonstrates that the diffusion trajectory aligns well with the human visual system.

BoundInk 是一种以书写者为条件的框架，将字符间的边界视为显式生成单元，既能保留书写者特有的字形外观，又能在完整文本行内改善连接性与字距。BoundInk is introduced, a writer-conditioned framework that treats inter-character boundaries as explicit generation units and preserves writer-specific glyph appearance while improving connectivity and spacing across complete text lines.

本文提出 VLA-Precision，一个面向真实世界的高效在线 RL 框架，包含 Asymmetric Co-Bootstrapping (ACoB) 算法与 ACoB-Stream 架构；其中 ACoB-Stream 以不变状态解耦与按需流式传输为设计原则，构建了经验-策略的闭环架构，可实现最高 10.9% 的吞吐与计算效率提升。VLA-Precision, an efficient real-world online RL framework featuring the Asymmetric Co-Bootstrapping (ACoB) algorithm and the ACoB-Stream architecture, and ACoB-Stream, a closed-loop experience--policy architecture that establishes invariant-state decoupling and on-demand streaming as design principles, deliver up to 10.9% improvements in throughput and computational efficiency.

LightMIS 通过 Scale-Aligned Projection 块将五级编码器的输出对齐至统一分辨率，仅聚合一次，并利用结合 Adaptive Kernel Fusion 与所提 Progressive Receptive Fusion 模块的 Adaptive Fusion Cascade 精炼融合表示。LightMIS aligns the outputs of a five-level encoder to a common resolution using Scale-Aligned Projection blocks, aggregates them once, and refines the fused representation with an Adaptive Fusion Cascade, which combines Adaptive Kernel Fusion with the proposed Progressive Receptive Fusion module.

LastOPD 仅在最后一层状态（LM head 共同读取的接口）施加潜在信号，且仅在进入 token 级 OPD 前的 10 步交叉淡入阶段施加，从而保留潜在信号中的有用部分，并在坍缩发生前将 student 交由 token 级监督。LastOPD is proposed, which applies the latent signal only at the last-layer state, the common interface both LM heads read, and only during a 10-step crossfade into token-level OPD, which keeps the useful part of the latent signal and hands the student to token-level supervision before the collapse sets in.

InfiniHand 是一个端到端流式前馈框架，联合估计 MANO 参数、相机轨迹与手部位置，直接从未标定的自我中心视频出发，相比 ViDiHand 在 ARCTIC PA-p 上降低 21%，并显著缓解世界空间漂移。InfiniHand is an end-to-end streaming feed-forward framework that jointly estimates MANO parameters, camera trajectories, and hand locations directly from uncalibrated egocentric video, achieving a 21% reduction in ARCTIC PA-p compared to ViDiHand while substantially mitigating world-space drift.

GeoVerse 是一个在世界一致条件下合成新视角的框架，在预训练 3D 基础模型的几何潜在空间内进行生成，并通过 ControlNet 风格 adapter 注入视频生成模型的外观先验。GeoVerse is a framework that synthesizes world-consistent novel views by performing generation within the geometric latent space of a pretrained 3D foundation model and injecting appearance priors from a video generative model via a ControlNet-style adapter.

UMM-Reflection 在统一模型内利用强化学习（RL）完成完整反思轨迹：兄弟轨迹共享同一初始图像，因此组相对优势可比较不同反思策略，而单条轨迹级优势同时更新反思 token 与基于 flow 的修订，避免了逐轮信用分配的组合爆炸。UMM-Reflection is introduced, which applies reinforcement learning (RL) to complete reflection trajectories inside one unified model: sibling trajectories share one initial image, so the group-relative advantage compares reflection strategies, and one trajectory-level advantage updates both the reflection tokens and the flow-based revisions, avoiding the combinatorial blow-up of per-round credit assignment.

本文提出 FlowTool，一个通过 conditional rectified flow 直接建模以输入图像和用户指令为条件的高质量工具参数分布的框架，并显著提升了推理效率。This work introduces FlowTool, a framework that directly models the distribution of high-quality tool parameters conditioned on the input image and user instruction using conditional rectified flow, and significantly improves inference efficiency.

本文勾勒了一幅计算机视觉版图：在其中，越来越复杂的视觉任务可通过通用接口访问，而精确且对保真度敏感的感知仍是重要前沿。A changing landscape of computer vision is mapped in which increasingly sophisticated visual tasks are accessible through a general-purpose interface, while precise and fidelity-sensitive perception remains an important frontier.

OLIVE 通过从持续演化的 student 生成前缀，在离线蒸馏进入平台期后继续提升，同时更好地保持了 student 的通用能力与可塑性。By regenerating prefixes from the evolving student, OLIVE continues improving after offline distillation plateaus while better preserving the general capabilities and plasticity of the student.

提出解耦信用自蒸馏，理论上将信用方向与幅度解耦为两个可靠信号，并据此校准特权教师监督，从而完成策略优化的 step-to-token 信用分配。Decoupled Credit Self-Distillation is introduced, which theoretically decouples credit direction and magnitude into two reliable signals and uses them to calibrate privileged teacher supervision, enabling step-to-token credit assignment for policy optimization.

NVV-SuperBench 与人工听力评测的结果显示，NVAlign 在标签跟随准确率上优于 SFT 与 Flow-GRPO 基线，证明直接对奖励梯度进行优化可提升连续自回归流匹配 TTS 中的非语言控制能力。Results from NVV-SuperBench and human listening evaluations show that NVAlign improves tag-following accuracy over SFT and Flow-GRPO baselines, demonstrating that direct reward-gradient optimization can improve non-verbal control in continuous autoregressive flow-matching TTS.

构建了 RoboGenesis，一个基于仿真的工作流与数据引擎，可从原子技能组合配置好的实验工作流，对 rollout 进行验证与过滤，并跨支持的机器人配置导出结构化演示数据。RoboGenesis is built, a simulation-based workflow and data engine that composes configured laboratory workflows from atomic skills, validates and filters rollouts, and exports structured demonstrations across supported robot profiles.

结果表明，harness 层面的能力组合是构建广泛、可演进 Omni 系统的实用路径，且无需改变宿主 agent 的推理核心。The results establish harness-level capability composition as a practical route to broad, evolvable Omni systems without changing the host agent's reasoning core.

提出 Omni-Decision，一个基于证据账本规划的 omni-modal agent：用显式的证据账本替代不断膨胀的对话历史，记录仍缺失的证据、已确认的内容以及记录间的冲突。Omni-Decision is presented, an omni-modal agent built on evidence-ledger planning: it replaces the growing dialogue history with an explicit evidence ledger that records what evidence is still missing, what has been confirmed, and where records conflict.

APM-Bench 将真实场景的流式交互重构为多会话生命轨迹，并揭示了一个清晰的效用—延迟—存储权衡：现有方法仍难以同时实现可靠的长程记忆、低开销以及跨会话有效的主动协助。APM-Bench is introduced, which reformulates real-world streaming interaction as multi-session life trajectories and reveals a clear utility--latency--storage trade-off: existing methods still struggle to simultaneously achieve reliable long-term recall, low overhead, and effective proactive assistance across sessions.

一种随机少步生成器，其 FID 在采样预算从 4 增加到 50 NFE 时不会下降，且相同配方可迁移到 text-to-image 生成。A stochastic few-step generator whose FID does not degrade as the sampling budget grows from 4 to 50 NFE, and the same recipe transfers to text-to-image generation.

提出 Budgeted ATTA：测试批次中仅有一小部分可获得标签，且监督时机是 active test-time adaptation 中一个关键但尚未充分探索的方面。Budgeted ATTA is introduced in which labels are available for only a fraction of test batches, and the timing of supervision is a key, yet underexplored, aspect of active test-time adaptation.

本文提出 AutoRef，在保持两侧模型冻结的前提下自动优化 harness：一个 coding Agent 迭代重写 harness 代码，并发现 AutoRef-Harness，可改进开源权重模型 FLUX。This work proposes AutoRef, which optimizes the harness automatically while keeping both models frozen: a coding agent iteratively rewrites the harness code, and discovers AutoRef-Harness, which improves the open-weight FLUX.

在包括多视角空间推理和碰撞预测在内的六项多样化视觉推理任务中，ReImaGin 始终优于纯文本推理和专用 vision-tool 基线，提升幅度高达 25%，证明了灵活、可生成的视觉推理的优势。Across six diverse visual reasoning tasks including multi-view spatial reasoning and collision prediction, ReImaGin consistently outperforms both text-only reasoning and specialist vision-tool baselines, with gains of up to 25\%, demonstrating the advantage of flexible, generative visual reasoning.

本文提出 UniEvo-VL，一种面向多模态模型的自演化框架，可在 test-time compute 阶段从这种建设性的自纠错反馈中学习，在无外部监督或指导的情况下提升用户使用多模态模型的体验。UniEvo-VL, a self-evolving framework for multimodal models to learn from this constructive self-correction feedback during test-time compute, is introduced to enhance the user experience when using multimodal models without external supervision or guidance.

转移层面的结果表明，state adaptation 选择性而非统一地应用时最为有效，且 adaptation 的价值取决于策略反转的频率与幅度。The transition-level results suggest that state adaptation is most useful when applied selectively rather than uniformly, and that adaptation value depends on both the frequency and magnitude of strategy reversals.

本文提出 MIST（Misleading-Image Stress Test）：200 个英文句子，每句围绕一个可作比喻或字面理解的短语，配以对齐图像（描绘其读法）、误导图像（描绘相反读法）或无图像三种条件。MIST, the Misleading-Image Stress Test: 200 English sentences, each built around a phrase readable either figuratively or literally and shown with an aligned image depicting its reading, a misleading image depicting the opposite, or no image at all is introduced.

本文提出一种方法，通过多层感知机的一次前馈传播生成独立样本（与近期提出的 GAN 类似），并使用 MMD 学习生成可被解码为样本的 codes。This work forms a method that generates an independent sample via a single feedforward pass through a multilayer perceptron, as in the recently proposed generative adversarial networks, using MMD to learn to generate codes that can then be decoded to produce samples.

Tacit-TTS 是一个从 IndexTTS2 蒸馏而来的高效无需转录的零样本语音克隆系统，用掩码非自回归生成替换自回归的文本到语义解码，引入无需训练的声学长度估计，并通过 ReFlow 蒸馏加速流匹配渲染器。Tacit-TTS is presented, an efficient transcript-free zero-shot voice cloning system distilled from IndexTTS2 that replaces autoregressive text-to-semantic decoding with masked non-autoregressive generation, introduces training-free acoustic length estimation, and accelerates the flow-matching renderer through ReFlow distillation.

统一多模态模型（UMM）通常依赖独立的视觉表示分别完成理解与生成，这增加了视觉上下文长度，并使其难以与既有视觉-语言预训练流程集成。近期 pixel-space modeling 的进展提供了一种无编码器的替代方案，但将该范式从图像扩展到视频并非易事：视频理解与生成采用不同的时间表示，统一视觉接口的设计仍是开放问题。本文提出 PixelUMM，一种用于统一图像与视频理解的无编码器模型……Unified Multimodal Models (UMMs) often rely on separate visual representations for understanding and generation, increasing visual context length and complicating integration with established vision-language pretraining pipelines. Recent advances in pixel-space modeling offer an encoder-free alternative, but extending this paradigm from images to videos is non-trivial: video understanding and generation adopt different temporal representations, leaving the design of a unified visual interface an open question. We present PixelUMM, an encoder-free model for unified image and video understanding

本文提出 LOCI，一种混合的空间记忆架构，同时维护键值缓存与循环记忆两种表示，在重访内容的复现上比代表性世界模型以及同配置的 full-softmax 模型都更为忠实。LOCI is introduced, a hybrid spatial-memory architecture that keeps both representations of key-value caches and recurrent memory that reproduces revisited content more faithfully than representative world models and a same-recipe full-softmax model.

提出一种新颖的深度架构与 GAN 形式化方法，有效衔接文本与图像建模领域的进展，将视觉概念从字符转换为像素。A novel deep architecture and GAN formulation is developed to effectively bridge advances in text and image modeling, translating visual concepts from characters to pixels.

提出 Ego2Act：一个面向目标的基准，包含来自 110 个真实日常任务的 2.640 段视频，覆盖不同的物体杂乱度与多步复杂度；同时提出 Ego2ActJudge，一种无参考评估流水线，在任务完成度与物理合理性评估上与人类共识的对齐效果优于相关基线。Ego2Act is introduced, a goal-directed benchmark featuring 2,640 videos from 110 real-world tasks across day-to-day settings, varying object clutter and multi-step complexity, and Ego2ActJudge, a reference-free evaluation pipeline that achieves better task completion and physics plausibility evaluation alignment with human consensus compared to relevant baselines.

提出了 Honeycomb，一种基于 HexMemory 构建的视频世界模型；HexMemory 是作者提出的低秩表示，用于在固定大小的内存中存储场景特征，共包含六个空间与时空平面。Honeycomb is introduced, a video world model built on HexMemory, the authors' proposed low-rank representation for storing scene features in a fixed-size memory with a total of six spatial and spatiotemporal planes.

提出了 World Embedding Benchmark，包含来自 80 个族的 8,000 个受控仿真案例，涵盖流体力学、固体力学、动力学以及光学与电磁学，旨在强调需要联合评估物理一致性与物理属性可恢复性，并展示物理表示对改进视频生成的效用。The World Embedding Benchmark is introduced, comprising 8,000 controlled simulation cases from 80 families spanning fluid mechanics, solid mechanics, dynamics, and optics&electromagnetism, to highlight the need to evaluate physical alignment and property recoverability jointly and demonstrate the utility of physical representations for improving video generation.

ProAR 引入两个关键组件：一是将生成锚定到长远结果，二是通过非对称注意力掩码将目标帧预测集成到自回归循环中，使预测的目标帧可指导中间状态的生成而不被其干扰。ProAR introduces two key components: to anchor generation to the long-range outcome, and to integrate goal-frame prediction into the autoregressive loop via an asymmetric attention mask, enabling the predicted goal frame to guide the generation of intermediate states without being disrupted by them.