论文

本文提出 Vision-aligned Latent Reasoning（VaLR），一种简洁而有效的推理框架，在每个 Chain of Thought 推理步骤之前动态生成视觉对齐的 latent token，引导模型在 latent space 中基于感知线索进行推理。Vision-aligned Latent Reasoning (VaLR) is introduced, a simple, yet effective reasoning framework that dynamically generates vision-aligned latent tokens before each Chain of Thought reasoning step, guiding the model to reason based on perceptual cues in the latent space.

本文提出 ContextRL，一种上下文感知的强化学习方法，通过间接辅助目标提升长周期推理与多模态性能，并与将相同对比上下文复用作标准 query–context–answer 样本的数据增强基线进行对比。This work proposes ContextRL, a context-aware reinforcement learning (RL) method that improves long-horizon reasoning and multimodal performance through an indirect auxiliary objective, and compares against data-augmentation baselines that repurpose the same contrastive contexts as standard query--context--answer examples.

本研究建立了一套实用的 LongPT 方案，为推进长上下文 vision-language 模型奠定了经验基础，并提出 MMProLong，无需任务专属监督即可泛化至基于网页的多模态 needle 检索、长上下文图文压缩以及长视频理解等任务。This study establishes a practical LongPT recipe and an empirical foundation for advancing long-context vision-language models, and introduces MMProLong, which generalizes to webpage-based multimodal needle retrieval, long-context vision-text compression, and long-video understanding without task-specific supervision.

本文提出 LLaDA-V，一种完全基于扩散范式的多模态大语言模型 (MLLM)，将视觉指令微调与 masked diffusion 模型相结合，脱离了当前多模态方法中主流的自回归范式。LLaDA-V is introduced, a purely diffusion-based Multimodal Large Language Model (MLLM) that integrates visual instruction tuning with masked diffusion models, representing a departure from the autoregressive paradigms dominant in current multimodal approaches.

本文推出 STEP3-VL-10B，一个面向"紧凑效率与前沿级多模态智能"权衡的轻量级开源基础模型，并发布完整模型套件，为社区提供强大、高效且可复现的 baseline。STEP3-VL-10B is presented, a lightweight open-source foundation model designed to redefine the trade-off between compact efficiency and frontier-level multimodal intelligence, and the full model suite is released to provide the community with a powerful, efficient, and reproducible baseline.

本文概述了第二届 MAGMaR（Multimodal Retrieval 驱动的多模态增强生成）研讨会共享任务的成果，参赛系统聚焦于视频检索，或在给定检索视频的基础上进行有依据的文章生成。This overview paper presents the results of the shared task for the second workshop on Multimodal Augmented Generation via Multimodal Retrieval (MAGMaR), where participants submitted systems focused on either video retrieval or grounded generation of articles given retrieved videos.

我们提出 MOSS-VL，一个开源视觉-语言模型系列，将实时交互（边说边看）视为一等能力。它贯穿整个栈进行协同设计：语言解码器仅通过门控交叉注意力访问视觉，因此模型在生成过程中可以自然地感知新输入帧；合成的交互语料用于监督何时说话、何时沉默、何时修正；分阶段课程将所有实时相关训练集中在一个轻量的最终阶段，基于强大的离线基础模型。在离线场景下，MOSS-VL-Instruct 在We present MOSS-VL, an open vision-language model family that treats real-time interaction -- perceiving while it speaks -- as a first-class capability. It is co-designed across the stack: the language decoder attends to vision only through gated cross-attention, so the model can naturally see incoming frames while generating; a synthesized interaction corpus supervises when to speak, when to stay silent, and when to revise; and a staged curriculum concentrates all real-time-specific training in one light final stage over a strong offline foundation. Offline, MOSS-VL-Instruct is competitive at

性能评估显示，新训练的深度 CNN 模型 SentiBank 2.0（即 DeepSentiBank）在标注准确率与检索性能上较此前主要采用二分类 SVM 的版本有显著提升。Performance evaluation shows the newly trained deep CNNs model SentiBank 2.0 (or called DeepSentiBank) is significantly improved in both annotation accuracy and retrieval performance, compared to its predecessors which mainly use binary SVM classification models.

ViT-5 的设计与当代基础模型实践保持一致，可作为对 vanilla ViT 的直接替换升级方案，适用于 2020 年代中期的视觉骨干网络，并为生成建模提供更强大的骨干。With a design aligned with contemporary foundation-model practices, ViT-5 offers a simple drop-in upgrade over vanilla ViT for mid-2020s vision backbones and serves as a stronger backbone for generative modeling.

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.

提出 LingBot-Video，一种专为具身智能设计的基于 DiT 的视频预训练范式，并将其作为首个大规模开源 MoE 视频基础模型贡献给社区，致力于在数字创意与物理执行之间搭建桥梁。LingBot-Video is presented, a DiT-based video pretraining paradigm specifically tailored for embodied intelligence, and is contributed as the inaugural large-scale, open-source MoE video foundation model to the community, in a pioneering effort to bridge digital creativity and physical actuation.

首次在 World Modeling 领域引入 Agentic Harness 框架：由 pilot agent 负责规划并执行角色行为，director agent 负责随着场景推进合成新的环境要素。The integration of an agentic harness within the domain of world modeling is pioneered, wherein a pilot agent is tasked with planning and executing character behaviors, while a director agent is responsible for synthesizing novel environmental elements as the scene progresses.

提出 LaMem-VLA，一种以潜在记忆为核心框架的方法，将历史经验重建为潜在记忆 token，并直接与 VLA 推理交织，使记忆能够在有界上下文下直接参与 VLA 推理并引导动作生成。LaMem-VLA is introduced, a latent-memory-native framework that reconstructs historical experience into latent memory tokens and directly interweaves them with VLA reasoning, and enables memory to directly participate in VLA reasoning and guide action generation under a bounded context.

WildCity 是一个由自动驾驶车队在复杂城市环境中采集的真实多模态数据集，旨在推动城市级渲染的进展，并更广泛地推动 AI 在空间感知、记忆与推理方面达到与人类认知相当规模的能力。WildCity, a real-world multimodal dataset collected by autonomous fleets traversing complex urban environments, aims to catalyze progress not only in city-scale rendering, but more broadly in the pursuit of AI that can perceive, remember, and reason across space at a scale comparable to human cognition.

提出 MV-Forcing 框架，通过在顺序生成视角之间引入 4D 几何桥梁，在单一扩散模型中组合时间与视角自回归，并弥合时间与视角序列自回归中训练-推理的曝光偏差差距。MV-Forcing, a framework that composes temporal and view-wise autoregression within a single diffusion model by introducing a 4D geometric bridge between sequentially generated views, and closes the train-inference exposure bias gap for both temporal and view-sequential autoregression.

该工作通过轻量级 Gated MLP 将固定的 Gemini Embedding 2 视觉-语言表征映射到目标空间，使用 KL 散度与同方差不确定性加权进行训练，并展示了 AI Wizards 参加 EXIST 2026 多模态迷因性别歧视识别任务的提交方案。This work maps fixed Gemini Embedding 2 vision-language representations through a lightweight Gated MLP trained with KL divergence and homoscedastic uncertainty weighting, and presents the AI Wizards submission to EXIST 2026 for multimodal sexism identification in memes.

本工作推出 Gemma 4——Gemma 模型系列中新一代开源权重、原生多模态的语言模型，在 STEM、多模态与长上下文基准上实现性能跃升，在人类评分任务上可与更大的前沿开源模型相媲美。This work introduces Gemma 4, a new generation of open-weight, natively multimodal language models in the Gemma model family that establishes a leap in performance across STEM, multimodal, and long-context benchmarks, and rivals larger, frontier open models in human-rated tasks.

本文提出 OrbitQuant，一种数据无关的权重量化器，通过在归一化旋转基空间中进行量化以绕过范围估计，将图像扩散 Transformer 的 PTQ 推进到 W2A4 并保持可用生成质量。OrbitQuant is presented, a data-agnostic weight-activation quantizer that bypasses range estimation by quantizing in a normalized, rotated basis and pushes PTQ of image diffusion transformers to W2A4 with usable generation quality.

本文提出一个联合训练的对抗框架，通过建模对象语义和对象间关系来增强场景图，并在 Visual Genome 数据集上以代理场景图增强指标、图像质量比较、定性示例与用户研究进行评估。A jointly trained adversarial framework is proposed that enriches scene graphs by modeling object semantics and inter-object relations and is evaluated with proxy scene graph enrichment metrics, image-quality comparisons, qualitative examples, and user studies on the Visual Genome dataset.

VLA-Corrector：面向动作分块 VLA 策略的轻量级修正推理框架；引入轻量的潜空间视觉监控器，持续比对预测与实际视觉特征演化，可在线检测视觉动态偏差，缓解静态时域在执行鲁棒性与策略调用频率之间的权衡。VLA-Corrector, a lightweight corrective inference framework for action-chunked VLA policies, introduces a lightweight Latent-space Vision Monitor that continuously compares predicted and actual visual feature evolution, enabling online detection of visual dynamics deviations and mitigates the trade-off imposed by static horizons between execution robustness and policy-call frequency.

提出 Attention Separation，在保留与 Self-Flow 相同的双时间步输入的同时，阻止被分配到不同噪声水平 token 之间的注意力，并表明 Attention Separation 本身通过将单张图像拆分为多个有效训练部分来扩充训练数据，从而带来增强效果。Attention Separation is introduced, which preserves the same dual-timestep input as Self-Flow while blocking attention between tokens assigned to different noise levels, and shows that Attention Separation itself provides an augmentation effect by splitting a single image into multiple effective training parts to expand the training data.

AMVL 在潜空间集成的 MLLM 中实例化，持续优于多种强离散和潜空间推理基线，在复杂 BLINK 基准上平均得分提升 +10.83，单个推理任务最高提升 +32.00，分析也确认了潜空间稳定性的改善。AMVL is instantiate in a latent-integrated MLLM and it consistently outperforms strong discrete and latent-reasoning baselines, improving the average score on the complex BLINK benchmark by +10.83 and achieving gains of up to +32.00 on individual reasoning tasks, with analyses confirming improved latent-space stability.

在富文本图像生成基准上的实验表明，在数据预算匹配条件下，DataEvolver 比固定数据集基线生成更有用的训练数据，且结果表明被拒绝的样本可为改进富文本图像数据构建提供可操作的反馈信号。Experiments on text-rich image generation benchmarks show that DataEvolver produces more useful training data than fixed-dataset baselines under matched data budgets, and results suggest that rejected samples can provide actionable feedback for improving text-rich image data construction.

本文提出 QVal，一个无需训练即可直接评估密集监督信号的测试平台，发现简单提示基线始终优于文献中近期提出的密集监督方法，且性能按模型家族高度聚类。QVal, a training-free testbed for directly evaluating dense supervision signals, is introduced, finding that simple prompting baselines consistently outperform recent dense supervision methods from the literature, and that performance clusters strongly by family.

本工作提出 MuSViT (Music Score Vision Transformer)：首个面向乐谱表征的基础视觉模型——一个通过 Masked Autoencoders 在 IMSLP 970 万页数据上预训练的 ViT 编码器。This work introduces MuSViT (Music Score Vision Transformer): the first foundation vision model for sheet music representation -- a ViT encoder pre-trained via Masked Autoencoders on 9.7 million pages from the IMSLP.

本文提出 ILLUME-X，一种先进的统一多模态范式，通过提升多模态数据效率并稳定多模态训练过程，实现高质量、自由形式的交错图文生成。This paper introduces ILLUME-X, an advanced unified multimodal paradigm that enables high-quality, free-form interleaved text-image generation by improving multimodal data efficiency and stabilizing the multimodal training process.

本文将门控量子启发的 Kolmogorov-Arnold 网络快权重编程器用于直接多步 Abilene 流量矩阵预测，提出以经典慢速编程器搭配量子启发快速编程器的方案，作为面向资源受限场景的网络流量矩阵预测中一种兼顾精度与效率的有前景设计。This paper adapts gated quantum-inspired Kolmogorov-Arnold network fast-weight programmers to direct multi-step Abilene TM forecasting and identifies a classical slow programmer with a quantum-inspired fast programmer as a promising accuracy-efficiency design for resource-conscious network traffic-matrix forecasting.

本文提出 PhysRAG——一条通过检索增强生成（RAG）提升视频生成物理感知的新流程，并基于 WISA-80K 数据集设计了两阶段数据过滤流程，最终筛选出 7K 高质量视频用于训练。This work introduces PhysRAG, a novel pipeline that enhances physical awareness in video generation through Retrieval-Augmented Generation (RAG), and designs a two-stage data filtering pipeline based on the WISA-80K dataset, resulting in a curated set of 7K high-quality videos for training.

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.

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 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 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.