👍 71
07/08 08:00
Structure-property relationships are foundational to biology, chemistry and materials science, where function, reactivity and physical response emerge from spatial, chemical and periodic organization. Mechanistically explaining these relationships requires interpreting structural evidence through sc
中文介绍 提出 Deep Native Structural Reasoning,一种深度学习方法,用于跨学科(生物、化学、材料)的结构-性质关系理解,通过解释结构证据实现准确、透明的机理分析。
👍 55
07/03 08:00
Scaling modern large language models (LLMs) to long contexts is limited by the quadratic computation cost, and poor length extrapolation of dense attention. Chunk-wise sparse attention offers a promising alternative, but all existing methods fall short of full attention because of their inaccurate c
中文介绍 提出一种层级稀疏注意力方法,用于无限上下文建模。通过分块稀疏注意力降低二次计算成本,并改善长度外推能力,在长上下文任务中接近全注意力性能。
👍 44
07/08 08:00
Mainstream Vision-Language-Action (VLA) models predict actions primarily from the current observation under a Markovian assumption, thus struggling with long-horizon, temporally dependent tasks. Existing memory-augmented VLAs either expand the observation window or retrieve history from the memory b
中文介绍 提出 Dual Latent Memory 模块,增强 Vision-Language-Action (VLA) 模型的长期记忆能力,解决长时域机器人操作任务中的马尔可夫假设局限,提升复杂任务成功率。
👍 39
07/02 08:00
We introduce Gemma 4, a new generation of open-weight, natively multimodal language models in the Gemma model family. Designed to advance compute efficiency and reasoning, the Gemma 4 model suite features dense and Mixture-of-Experts architectures, ranging from 2.3B to 31B parameters. Alongside impr
中文介绍 发布 Gemma 4 系列开放权重多模态语言模型,提供 Dense 和 Mixture-of-Experts 架构(2.3B-31B 参数),在计算效率和推理能力上提升,支持多种模态输入。
👍 38
07/07 08:00
We formulate computer vision as unified multimodal generation, where heterogeneous visual tasks are expressed in the native text and image generation spaces of a unified multimodal model, without task-specific architectures. Under this formulation, SenseNova-Vision uses natural-language instructions
中文介绍 将计算机视觉统一为多模态生成任务,提出 SenseNova-Vision,通过自然语言指令在文本和图像生成空间中表达多种视觉任务,无需任务专用架构,实现统一建模。
👍 25
07/06 08:00
Speculative decoding accelerates Large Language Model (LLM) inference by decoupling draft generation from target verification. While recent parallel drafters efficiently propose long token sequences in a single forward pass, they suffer from rapid acceptance decay due to a lack of inter-token depend
中文介绍 提出 DSpark,一种置信度调度的半自回归推测解码方法。通过引入半自回归草稿生成和置信度调度策略,缓解并行草稿的接受率衰减问题,显著加速 LLM 推理。
👍 21
07/08 08:00
We present LingBot-World 2.0 (also known as LingBot-World-Infinity), an advanced iteration of LingBot-World featuring four distinct upgrades. (1) Our model achieves an unbounded interaction horizon while maintaining consistent output quality, benefiting from a carefully crafted causal pretraining pa
中文介绍 提出 LingBot-World 2.0(LingBot-World-Infinity),支持无限交互视界的高质量输出。通过因果预训练等四项升级,实现一致的多模态交互,在 3D 世界生成与交互任务上性能显著提升。
👍 20
07/05 08:00
Reinforcement learning (RL) for non-verifiable instruction following increasingly relies on LLM judges with prompt-specific rubrics as reward signals. While recent methods adapt these rubrics to the evolving policy during training, the training prompts themselves remain static, drawn from fixed corp
中文介绍 提出 LLM-as-a-Tutor 框架,在非可验证指令跟随强化学习中,采用策略感知的提示自适应方法,动态调整训练提示和评分标准,提升奖励信号的准确性与训练效率。
👍 18
07/03 08:00
Dense video captioning aims to generate temporally grounded descriptions of video events, benefiting both event-level video understanding and generation. In this domain, autoregressive video large language models have emerged as a prevalent paradigm due to their strong generative and cross-modal mod
中文介绍 提出并行自回归解码方法,用于全模态密集视频描述。通过并行化生成过程,加速视频大语言模型的时序事件描述,同时保持跨模态生成质量。
👍 13
06/29 08:00
JD.com, one of the world's largest e-commerce platforms, serves over 700 million active users and millions of merchants, with a catalog of tens of billions of SKUs. At this scale, high-quality, structured item knowledge underpins a better consumer experience, lower management costs, and higher opera
中文介绍 京东提出 Oxygen AIIC V1,基于 LLM/VLM 的工业级商品理解与管理方案,处理数百亿 SKU,实现高质量结构化商品知识,提升用户体验与运营效率。
👍 13
07/07 08:00
On-policy distillation (OPD) trains a student policy by matching a stronger teacher on the student's own trajectories, offering a promising framework for language agent training. However, its application to long-horizon agentic tasks remains insufficiently explored. We identify two key inefficiencie
中文介绍 提出 TurnOPD,一种回合感知的 on-policy 蒸馏方法,解决长时域语言智能体训练中的分布式偏移和更新效率问题,加速学生策略的收敛。
👍 12
07/07 08:00
Despite recent progress of VLA foundation models, the disparity between laboratory conditions and real-world applications continues to impede their practical implementation. To bridge this gap, we present LingBot-VLA 2.0, which advances LingBot-VLA through improvements in three functional domains. (
中文介绍 提出 LingBot-VLA 2.0,针对现实应用场景改进基础 VLA 模型,在三个功能域(感知、推理、控制)进行优化,缩小实验室与实际部署的差距。
👍 11
07/03 08:00
We introduce MentalThink, a visual-symbolic reasoning paradigm that equips Multimodal LLMs (MLLMs) with an executable mechanism for "mental" visualization. The core of MentalThink is a think-with-SVG pipeline, where the model learns to generate, render, and interpret scalable vector graphics (SVG) c
中文介绍 提出 MentalThink,一种视觉-符号推理范式,让多模态大语言模型通过生成、渲染和解释 SVG 图形进行「心智」可视化,增强空间推理和抽象能力。
👍 9
07/06 08:00
Complex image creation and editing often require more than a single generation or editing model. A user request may involve synthesizing images, localizing objects, segmenting regions, editing selected content, compositing intermediate assets, reading text, and enhancing the final result. Such tasks
中文介绍 提出 CanvasAgent,一种基于视觉工具编排的智能体,能够自动组合图像生成、分割、编辑、合成等多种模型,完成复杂的图像创建与编辑任务。
👍 9
07/02 08:00
State-of-the-art single-image 3D reconstruction methods often rely on complex hybrid architectures and loss functions, or compress geometry into latent spaces in order to leverage pre-trained latent diffusion models. In this work, we show that such architectural overhead and intricate loss formulati
中文介绍 提出 PointDiT,直接在像素空间进行扩散建模,用于单目深度与几何估计,避免复杂的混合架构和潜在空间压缩,简化训练并提升精度。
👍 7
07/04 08:00
Challenges remain in ego-centric 3D scene generation due to limited view overlap and the dominant influence of individual perspectives on scene interpretation. These factors hinder the creation of viewpoint-consistent and semantically aligned visual content, as well as the construction of accurate g
中文介绍 提出 CGGS(Consistency-Augmented Geometric Gaussian Splatting),通过一致性约束增强高斯泼溅,解决自中心 3D 场景生成中视角重叠少、语义不一致的问题。
👍 6
07/08 08:00
Reinforcement learning (RL) is becoming increasingly important for post-training large language models (LLMs). Previous RL pipelines for LLMs were mostly synchronous and batch-interleaved, which is inefficient for long-horizon agentic tasks. Recently, asynchronous RL has emerged as a more efficient
中文介绍 提出单次 rollout 异步优化框架,用于智能体强化学习(Agentic RL),解决长时域任务中同步批处理低效问题,加速 LLM 后训练过程。
👍 6
07/07 08:00
Generalist robot manipulation policies have advanced rapidly, yet existing benchmarks remain limited in systematically evaluating their capabilities. Many rely on simple, short-horizon, or skill-narrow tasks with limited capability coverage, and are often conducted only in simulation or only in the
中文介绍 提出 RoboDojo,一个统一的仿真与真实机器人操作策略基准,覆盖多样化长时域任务,支持对通用操作策略进行系统评估,弥补现有基准在任务复杂度和环境多样性上的不足。
👍 6
07/04 08:00
Multimodal large language models (MLLMs) generate responses autoregressively, integrating visual and linguistic information in an evolving context. Prior work on interpretability has focused on individual layers and circuits (where), leaving the token-level dynamics of multimodal computation during
中文介绍 研究多模态大语言模型在自回归生成过程中 token 级的注意力动态,揭示视觉与语言信息交互的细粒度机制,为模型可解释性提供新视角。
👍 6
07/07 08:00
Late-interaction retrieval models that use the MaxSim similarity function have shown strong empirical performance, often outperforming single-vector dense and sparse retrieval models. Despite these empirical findings, little is known about the theoretical representation power of MaxSim and how it co
中文介绍 量化并扩展基于 MaxSim 的晚期交互检索模型的理论表示能力,证明其相比单向量稠密和稀疏模型的优势,并提出改进方向以进一步提升检索性能。