论文

数据混合（而非过滤）是构建高质量训练数据集的关键：以指令型数据为主的混合在扩展时优于以描述型数据为主的混合，且规模越大优势越明显。It is found that data mixing, not filtering, is key to a high-quality training dataset: instruction-heavy mixtures scale better than caption-heavy ones, with gains widening at larger scales.

本文适配了一款专家混合扩散语言模型 DiffusionGemma-26B，并在医学视觉问答数据集上，使用相同的 LoRA 配置将其与同规模的自回归模型 Gemma-4-26B 进行基准对比，由对冗长度鲁棒的 LLM 裁判打分。This work adapts a mixture-of-experts diffusion language model, DiffusionGemma-26B, and benchmark it against its same-size AR sibling Gemma-4-26B under an identical LoRA recipe on medical visual question answering datasets, scored by a verbosity-robust LLM judge.

在三种多模态 LLM 主干模型上，MRPO 均稳定优于标准 GRPO 及一项最新的 RL 基线；在 Qwen3-VL-8B-Instruct 上甚至超越规模显著更大的医学 MLLM（如 HuatuoGPT-Vision-34B）2.79 分。Across three multimodal LLM backbones, MRPO consistently outperforms standard GRPO and a recent RL baseline, and on Qwen3-VL-8B-Instruct even surpasses substantially larger medical MLLMs such as HuatuoGPT-Vision-34B by 2.79 points.

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.

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.

本文将关键帧执行拆解为存在性、保真度、时序顺序、定位、持续性与唯一性六个互补指标，并通过结合专用感知模型的、基于证据的 MLLM 判断来评估整体视频质量。This work decomposes keyframe execution into six complementary metrics covering presence, fidelity, temporal ordering, localization, persistence, and uniqueness, while assessing overall video quality through evidence-grounded MLLM judgments augmented with specialized perception models.

本文提出 MultiRef-Compass，一个面向 MR2AV 生成的统一基准，将自动指标与引入复判增强的 MLLM-as-a-Judge 框架相结合，实现对感知保真度与参考条件合成能力的可扩展、可审计评估。MultiRef-Compass is introduced, a unified benchmark for MR2AV generation that integrates automatic metrics with a rejudging-enhanced MLLM-as-a-Judge framework, enabling scalable and auditable evaluation of both perceptual fidelity and reference-conditioned composition.

提出 VIABench，一个专为评估 MLLM 在视障辅助（VIA）场景中表现而设计的综合视频基准，采用视障人士（VIIs）自行录制或分享的第一人称视频，并提出一套严格的评测流水线，同时支持在线（实时）与离线设置。VIABench is introduced, a comprehensive video benchmark specifically designed to evaluate MLLMs in Visually Impaired Assistance scenarios using first-person videos recorded or shared by VIIs themselves, and proposes a rigorous benchmarking pipeline that supports both online (real-time) and offline settings.

本文推出 ScanNet，一个 RGB-D 视频数据集，包含 1513 个场景中的 250 万视角，标注有三维相机位姿、表面重建与语义分割，并表明使用该数据可在多项三维场景理解任务上取得 SOTA 性能。This work introduces ScanNet, an RGB-D video dataset containing 2.5M views in 1513 scenes annotated with 3D camera poses, surface reconstructions, and semantic segmentations, and shows that using this data helps achieve state-of-the-art performance on several 3D scene understanding tasks.

研究发现 ChatGPT 在大多数任务上以零样本学习优于其他 LLM，在部分任务上甚至超过微调模型，并且对非拉丁文字语言的理解能力优于生成能力。It is found that ChatGPT outperforms LLMs with zero-shot learning on most tasks and even outperforms fine-tuned models on some tasks and is better at understanding non-Latin script languages than generating them.

本文介绍 Matterport3D，一个大规模 RGB-D 数据集，包含来自 90 个建筑物级场景共 194,400 张 RGB-D 图像的 10,800 个全景视图，可支持多种监督与自监督计算机视觉任务，包括关键点匹配、视角重叠预测、由彩色图像预测法线、语义分割和区域分类。Matterport3D is introduced, a large-scale RGB-D dataset containing 10,800 panoramic views from 194,400RGB-D images of 90 building-scale scenes that enable a variety of supervised and self-supervised computer vision tasks, including keypoint matching, view overlap prediction, normal prediction from color, semantic segmentation, and region classification.

OCT-Bench能够对MLLM进行全面且细粒度的评估，为识别能力瓶颈和推进临床可信的OCT理解奠定基础。OCT-Bench enables comprehensive and fine-grained evaluation of MLLMs, providing a foundation for identifying capability bottlenecks and advancing clinically grounded OCT understanding.

本文提出一个时间感知的音频 LLM，能够基于大规模合成监督（来自级联 pipeline）在长达 120 分钟的输入上回答带有显式时间戳的问题，并在短时长和长时长 benchmark 上取得强劲的时间定位准确率。This work presents a time-aware audio LLM that answers questions with explicit timestamps over up to 120 minutes of input using large-scale synthetic supervision from a cascaded pipeline and achieves strong temporal-grounding accuracy on short and long benchmarks.

人类视觉是一个闭环：注视点不断被中间假设而非单一快照持续重定向。数十年的心理物理学与认知科学研究表明，主动观察对多种任务至关重要。当代多模态大语言模型 (MLLM) 是否进行主动观察，是一个现有视觉语言基准无法回答的经验问题。我们提出 ActiveVision，一个使 MLLM 主动观察可度量的基准，包含 3 个类别共 17 个任务，任务设计强制进行重复视觉感知……Human vision is a closed loop: gaze is continuously redirected by intermediate hypotheses rather than a single snapshot. Decades of psychophysics and cognitive science have argued that this active observation is essential for a wide range of tasks. Whether today's multimodal large language models (MLLMs) exercise active observation is an empirical question that current vision-language benchmarks do not answer. We introduce ActiveVision, a benchmark that makes active observation measurable for MLLMs, comprising 17 tasks across 3 categories. Tasks are designed to force repeated visual perception

提出 ENTRAP-VL（ENTRainment Assessment Probe for Vision and Language），一个由人工策展的 1,500 条数据的数据集，涵盖八个类别，按一个跨双轴的分类体系组织，并划分为文本诱发流和视觉诱发流。ENTRAP-VL (ENTRainment Assessment Probe for Vision and Language), a manually curated dataset of 1,500 items across eight categories, organized by a taxonomy that spans two axes and split into a textual-entrainment stream and a visual-entrainment stream, is introduced.

本研究考察是否存在一条实用路径，在没有坐标界面、且无需高成本区域级监督的条件下提升归因效果，并指出了这样一条可行路径。A study investigates whether there is a practical path to improve attribution without a coordinate interface and without costly region-level supervision, and indicates a practical path to improve attribution without a coordinate interface and without costly region-level supervision.

本文介绍 CLBench-V，一个多模态上下文学习 benchmark，围绕三个维度组织任务——上下文 grounding、新信息应用与新知识学习——以解决定位上下文使用失效位置的难题。This work introduces CLBench-V, a benchmark for multimodal context learning that addresses the difficulty of localizing where context use breaks down by organizing tasks around three dimensions: context grounding, new information application, and new knowledge learning.

对代表性闭源与开源多模态模型的评测表明，视觉推理强依赖于模型与环境，没有任何单一设置能在所有任务上持续占优。Evaluating representative proprietary and open-source multimodal models, it is found that visual reasoning is strongly model- and environment-dependent, with no single setting consistently dominating across tasks.