论文

本工作通过强化学习循环改进视觉-语言-动作（VLA）策略，该循环预测成功、进展及若干任务相关的未来量，并驱动优势估计、实时失败检测与候选选择，在 LeHome Challenge 2026 中取得佳绩。The work improves a vision-language-action (VLA) policy with a reinforcement-learning loop that predicts success, progress, and a few task-relevant future quantities and drives advantage estimation, live failure detection, and candidate selection in the LeHome Challenge 2026.

地球观测（EO）预报旨在依据变化的天气条件，从卫星观测预测未来地表动态。本文将其建模为部分可观测、天气驱动的世界建模问题，其中天气作为条件信号，而由于观测稀疏和未观测的陆面状态，预报本身具有不确定性。然而现有方法未能完整刻画这一设定：确定性模型将不确定性坍缩为单一未来预测，而基于扩散的方法通常将天气变量视作无条（原文此句截断）。Earth Observation (EO) forecasting aims to predict future Earth surface dynamics from satellite observations under changing meteorological conditions. In this paper, we view this task as a partially observed, weather-driven world modeling problem, in which weather acts as a conditioning signal, while forecasting remains uncertain due to sparse observations and unobserved land-surface states. However, existing methods do not fully capture this setting: deterministic models collapse uncertainty into a single future prediction, while diffusion-based methods typically treat weather variables as un

所提系统为阿尔及利亚方言的端到端对话建模提供了可复现基线；实验结果显示各组件均表现优异：ASR 词错误率低，NLU 意图分类与实体识别得分高，语音合成质量稳定。The proposed system provides a reproducible baseline for end-to-end conversational modeling in Algerian Dialect, and experimental results show strong performance across all components, including low word error rate for ASR, high intent classification and entity recognition scores for NLU, and stable speech synthesis quality.

本文提出新框架 Diffusion-LLM，将条件扩散模型集成到基于 LLM 的预测流水线中，展示了分布感知正则化在提升时间序列 LLM 的鲁棒性与泛化能力方面的价值。This work proposes a new framework Diffusion-LLM that integrates a conditional diffusion model into an LLM-based forecasting pipeline, and demonstrates the value of distribution-aware regularization for enhancing robustness and generalization in time series LLMs.

本文提出 RaysUp，一个超轻量级、任务无关且与 VFM 无关的特征上采样框架，可在任意分辨率下重建高分辨率特征图，仅使用 AnyUp 16% 的参数即达到 SOTA 性能，推理速度提升约 7 倍。RaysUp is proposed, an ultra-lightweight, task-agnostic, and VFM-agnostic feature upsampling framework that reconstructs high-resolution feature maps at arbitrary resolutions that achieves state-of-the-art performance while using only 16% of the parameters of AnyUp and delivering approximately 7x faster inference.

ABACUS 是一个统一视觉语言模型，可在无需任何基准特定训练的情况下处理物体计数、人群计数、指代表达计数以及忠实计数的图像生成，性能超越任务特定的专家模型和更大的通用模型。ABACUS is a unified vision-language model that handles object counting, crowd counting, referring-expression counting, and count-faithful image generation without any benchmark-specific training required, outperforming both task-specific specialists and larger generalist models.

MedRLM 旨在将医疗 AI 从静态问答转向可审计、多模态且工作流感知的临床决策支持，并引入临床证据图记忆，将患者特定观察与检索到的证据相连接。MedRLM aims to move medical AI from static question answering toward auditable, multimodal, and workflow-aware clinical decision support, and introduces a Clinical Evidence Graph Memory to connect patient-specific observations with retrieved evidence.

提出 Vesta，一个统一的具身通用模型，将定位、空间推理、导航和长程规划能力整合到单个基础模型中，并证明通用模型能够达到或超越专家模型。Vesta is presented, a unified embodied generalist that consolidates localization, spatial reasoning, navigation, navigation, and long-horizon planning capabilities into a single foundation model and demonstrates that a generalist model can match or exceed specialists.

提出 CogniRoute，一种面向社交全模态推理的 schema 引导 Mixture-of-Experts（混合专家）框架，并引入路由感知强化学习，通过答案正确性、模态一致性推理与认知时序锚定等奖励联合优化 token 生成与专家分配。CogniRoute, a schema-guided Mixture-of-Experts framework for social omni reasoning, is introduced and route-aware reinforcement learning is introduced, which jointly optimizes token generation and expert allocation using rewards for answer correctness, modality-consistent reasoning, and cognitive temporal grounding.

提出一种基于物体中心的残差强化学习框架，利用物体位姿精化 VLA 动作，使观测空间紧凑，在仿真与现实之间能够一致迁移。An object-centric residual RL framework is proposed that refines VLA actions using object poses, enabling a compact observation space that transfers consistently between simulation and reality.

提出 RoboTALES，一个学习任务对齐模拟未来并据此训练机器人策略的单阶段框架，引入两项关键创新：基于 LLM 的分层规划器，将复杂任务拆解为子目标序列以引导模型的"想象"；基于 VLM 的评判器，用于评估这些"想象"出的未来。This work proposes RoboTALES, a single-stage framework that learns task-aligned simulated futures and uses them to train robot policies and introduces two key innovations: a hierarchical LLM-based planner that breaks complex tasks into a sequence of subgoals to guide the model's imagination and a VLM-based critic that evaluates these ``imagined'' futures.

一种以 Token 为中心的双视图学习框架，在冻结的视觉 Transformer 主干中统一基于 prompt 的适配与跨视图融合，相比线性探测、仅 prompt 适配以及传统融合基线均取得稳定提升。A token-centric dual-view learning framework that unifies prompt-based adaptation and cross-view fusion within a frozen vision transformer backbone and demonstrates consistent improvements over linear probing, prompt-only adaptation, and conventional fusion baselines.

[摘要] Splash 是一个面向 MLLMs 的掩码隔离触觉对齐学习框架，它量化每个预训练参数的重要性，并将参数空间划分为休眠子空间与关键子空间，从而有效防止灾难性遗忘，确保非破坏性的模态扩展。Splash is presented, a mask-isolated tactile alignment learning framework for MLLMs that quantifies the significance of each pretrained parameter, and partitions the parameter space into a dormant and critical subspace, which effectively prevents catastrophic forgetting and ensures non-destructive modality expansion.

CineMobile 采用三重优化策略，通过蒸馏引导的剪枝方法得到一个紧凑而高效的模型，保留实现电影级效果所需的核心视频生成能力，证明了其在移动端图像到视频创作中的实用可行性。CineMobile adopts a three-fold optimization strategy, leveraging a distillation-guided pruning approach to derive a compact yet efficient model that retains the essential video generation capabilities required for cinematic effects, demonstrating its practical applicability for mobile-based image-to-video creation.

该审计揭示现有基准样本中 55% 可在无视觉输入或时序上下文的情况下被解决，并提出 Video-Oasis，一个用于系统性审计现有视频理解基准的可持续诊断套件。This audit reveals that 55\% of existing benchmark samples are solvable without visual input or temporal context, and introduces Video-Oasis, a sustainable diagnostic suite for systematically auditing existing video understanding benchmarks.

本文论证了稀疏组合监督与动宾学习的不对称性会助长物体驱动的捷径学习，并指出减少捷径诊断可提升组合泛化能力。This work argues that sparse compositional supervision and verb-object learning asymmetry can promote object-driven shortcut learning and reduces shortcut diagnostics and consequently improves compositional generalization.

本文提出 LongE2V，一种利用预训练视频扩散先验来联合处理基于事件视频重建、预测与帧间插值的新方法，并引入自回归展开与自适应上下文切换机制，以缓解超长序列中的时序漂移问题。This work proposes LongE2V, a novel approach that leverages pre-trained video diffusion priors to jointly handle event-based video reconstruction, prediction, and frame interpolation, and introduces Autoregressive Unrolling and Adaptive Context Switching to mitigate temporal drift in extremely long sequences.

该紧凑且量化原生、带内置控制的运行时为物联网音频场景下的端侧语义音频提供了实用基础；通过对转向接口的案例分析，可生成在部分属性上具有真实但有界控制的、承载口味联想的音乐。A compact, quantized runtime with built-in control a practical basis for on-device semantic audio in Internet-of-Sounds settings and a case study of the steering interface generates music carrying taste associations with genuine but bounded control for a subset of attributes.

SAM-MT 成功将延迟与目标数量解耦，在保持 SAM2 鲁棒视频分割性能的同时，实现了与单目标基线相当的实时速度。SAM-MT successfully decouples latency from the number of targets, achieving real-time speed on par with single-target baselines while maintaining SAM2's robust video segmentation performance.

论文主张语音结构已隐含在自监督语音模型（S3M）的表征中，只需对其进行引导即可同时完成切分与识别任务。It is argued that phonetic structure is already latent in the representations of self-supervised speech models (S3Ms), and one only needs to steer them to solve both segmentation and recognition tasks.

提出 MedPMC——一种自动化、可持续更新的框架，可将宽松许可的文献转化为面向医学多模态模型的高保真基础设施，并公开发布该框架、语料库、基准与预训练模型。MedPMC, an automated, continuously updatable framework that transforms permissively licensed literature into high-fidelity infrastructure for medical multimodal models, is introduced and publicly release the framework, corpus, benchmarks, and pretrained models.

提出 VaseMuseum——一种面向古希腊陶器智能数字博物馆的轻量化、模块化多模态智能体框架，相比启用搜索的 VLM 基线，它提升了引用有效性，减少了知识密集型查询中的幻觉，并在含歧义场景下给出更中立的回答。VaseMuseum is proposed, a lightweight and modular multimodal agent framework for intelligent digital museums of ancient Greek pottery that improves citation validity, reduces hallucinations on knowledge-intensive queries, and produces more neutral answers under ambiguity compared with search-enabled VLM baselines.

Motion4Motion 对视频中角色的 motion flow（而非骨骼）进行建模，使跨物种运动迁移更加容易。Motion4Motionmodels the motion flow of the character in a video instead of skeletons, which makes motion transfer across species easier, which makes motion transfer across species easier.

提出 StudioRecon，一种通过解耦背景与人体、并利用视频扩散模型合成数百个相机可控新视角，从稀疏低重叠相机重建 4D 人体场景的流水线，在四个真实数据集上达到了 SOTA 的新视角合成效果StudioRecon is proposed, a pipeline that reconstructs 4D human scenes from sparse, low-overlap cameras by decoupling background and humans by synthesizing hundreds of camera-controlled novel views with a video diffusion model and achieves state-of-the-art novel view synthesis across four real-world datasets.

推出 CtrlVTON，一个将试穿重构为图像编辑问题并引入分割掩码作为对服装布局（包括风格、尺寸与身体空间位置）像素级控制的可控 VTO 框架CtrlVTON is introduced, a controllable VTO framework that recasts try-on as an image editing problem and adds segmentation masks as pixel-level control over garment layout, including style, size, and spatial placement on the body.

提出 LATO.2，一个因子化 flow matching 框架，将网格生成分解为 vertex flow 和随后以已实现顶点为条件的 connectivity flow，在几何保真度和连通性质量上超越 SOTA 的拓扑感知网格生成方法。LATO.2, a factorized flow matching framework that decomposes mesh generation into a vertex flow followed by a connectivity flow conditioned on the realized vertices, is presented, which surpasses state-of-the-art topology-aware mesh generators in geometric fidelity and connectivity quality.

本文探索了一个预训练、冻结 encoder 的潜空间用于 text-to-image 个性化，并表明可以在该空间及由选定 token 定义的子空间中识别出有意义的编辑方向，从而实现局部化、细粒度且语义一致的编辑。This work explores the latent space of a pre-trained, frozen encoder for text-to-image personalization, and shows that meaningful directions can be identified within this space and within subspaces defined by selected tokens, enabling localized, fine-grained, and semantically coherent edits.

Xiaomi-Robotics-U0 是首个支持跨多种机器人本体的高质量多视角场景生成、并引入结构化、可控的具身迁移以实现细粒度编辑的模型，同时保持多视角一致性与交互动态。Xiaomi-Robotics-U0 is the first model to support high-quality multi-view scene generation across multiple robot embodiments and to introduce structured, controllable embodied transfer for fine-grained editing while preserving multi-view consistency and interaction dynamics.

提出 ST-Evidence，首个同时面向判别式和生成式像素级 grounding 的人工验证 benchmark，并开发可扩展的自动生成流程，构建了 16 万规模、衔接高层推理与细粒度 grounding 的数据集 ST-Evidence-Instruct。ST-Evidence is introduced, the first human-verified benchmark for both discriminative and generative pixel-level grounding, and scalable, automated generation pipelines are developed to create ST-Evidence-Instruct, a 160k-scale dataset bridging high-level reasoning with fine-grained grounding.

针对一系列基本增广与任意具有平稳统计量的图像数据集，以解析方式根据对比损失计算最优表示，结果表明对于某些增广，最优解可由第一层滤波器为正弦函数的 CNN 实现。Analytically computing the optimal representation in terms of a contrastive loss for a range of basic augmentations and any image dataset with stationary statistics shows that for certain augmentations the optimum can be attained by a CNN whose first layer filters are sinusoids.

MAGIC 是一个四阶段 pipeline，能将单一自然语言提示转化为可运行的多场景游戏项目，相比 LLM 基线与 Holodeck 可恢复更多真实 portal，并生成显著更可导航的布局。MAGIC is a four-stage pipeline that turns a single natural-language prompt into a runnable multi-scene game project that recovers more ground-truth portals and yields markedly more navigable layouts than an LLM baseline and Holodeck.

Hallo4D 提出"生成-检测-修正"范式，利用大型多模态语言模型（LMMs）从多视角与多帧渲染中识别并归纳时空不一致性，为一致性感知的内容生成提供了一种可扩展且可泛化的方案。Hallo4D introduces a generation-detection-correction paradigm that leverages large multimodal language models (LMMs) to identify and summarize spatial and temporal inconsistencies from multi-view and multi-frame renderings, providing a scalable and generalizable solution for consistency-aware content generation.

研究表明，通过更强的多模态编码器、Agentic prompt 改写及相关技术来增强 Boogu-Image 系统的理解能力，并结合数据质量、训练流程和 Agentic 推理时扩展的改进，即使在计算预算极为受限的条件下，也能显著提升生成与编辑性能。It is demonstrated that strengthening the understanding capability of the Boogu-Image system, through a stronger multimodal encoder, agentic prompt rewriting, and related techniques, together with improvements in data quality, training pipelines, and agentic inference-time scaling, can substantially enhance generation and editing performance even under highly constrained compute budgets.

本研究表明 DiT 与 ViT 在一个关键方面存在差异：DiT 不会出现 patch-token 异常值，但仍能受益于 registers；并且 registers 在像素空间 DiT 中比在潜空间 DiT 中效果更显著。This work shows that DiTs differ from ViTs in a key respect: they do not exhibit patch-token outliers but still benefit from registers, and finds that registers are more effective in pixel-space DiTs than in latent-space DiTs.

面向第 11 届 ABAW 挑战赛的多任务学习系统，在标准确定性架构基础上扩展条件 Rectified Flow 头，建模真实场景下面部行为固有的模糊性，借助蒙特卡洛采样实现不确定性感知的一对多预测。A multi-task learning system for the 11th ABAW challenge that extends a standard deterministic architecture with a conditional rectified-flow head to model the inherent ambiguity of in-the-wild facial behavior, enabling uncertainty-aware one-to-many predictions through Monte Carlo sampling.

本文提出 VideoChat3，一个完全开放、高效且以视频为中心的通用 MLLM，仅以 4B 参数与更高效率，超越参数量相当或更大的已有开源模型。This work introduces VideoChat3, a fully open, efficient, and generalist video-centric MLLM, which surpasses prior open-source models with equal or larger parameter counts with only 4B parameters and higher efficiency.