👍 84
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方法,精确、跨学科且透明地理解结构-性质关系。利用深层结构推理机制直接从空间、化学和周期排列解释结构证据,无需手工特征。适用于生物学、化学和材料科学。
👍 54
07/02 08:00
The inherent complexity of video understanding makes it difficult to determine whether Video-LLM benchmark performance stems from visual perception, linguistic reasoning, or knowledge priors. While many benchmarks have emerged to assess high-level reasoning, shared criteria for evaluating video unde
中文介绍 Video-Oasis重新思考视频理解评估,提出共享评估标准以区分基准性能来自视觉感知、语言推理还是知识先验。通过系统设计确保视频-LLM评估的公平性和可控性,避免现有基准的混淆因素。
👍 51
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 针对VLA模型在长时程任务中依赖马尔可夫假设的局限,提出Dual Latent Memory机制,引入显式和隐式双潜记忆模块以利用历史信息,实现长期依赖的机器人操作中稳定高效的动作预测。
👍 31
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(Infinity),实现无限交互时程且保持输出一致。通过因果预训练、可扩展世界引擎和多阶段训练,生成持续稳定的虚拟世界,支持丰富用户交互。
👍 30
07/09 08:00
Scientific ideas rarely start from a blank page. They inherit mechanisms, repair known limitations, and recombine pieces of earlier work, much like biological genomes. Current benchmarks still say little about whether AI systems can follow this inheritance structure. We present IdeaGene-Bench (IG-Be
中文介绍 提出IdeaGene-Bench基准,评估AI系统对科学思想谱系推理和基于谱系的新思想生成能力。模拟思想继承、修复和重组先前工作的结构,要求模型遵循类似基因组的传承结构,填补现有评测空白。
👍 27
07/09 08:00
Recovering high-quality video from sparse event streams is a challenging task. Regression methods often blur textures, while existing generative models struggle with long-term stability. We propose LongE2V, a novel approach that leverages pre-trained video diffusion priors to jointly handle event-ba
中文介绍 LongE2V利用预训练视频扩散先验,联合处理基于事件的视频重建、预测和帧插值。通过扩散模型克服传统回归纹理模糊和生成模型长时不稳定性,在稀疏事件流输入下恢复高质量长视频。
👍 27
07/09 08:00
The rapid development of large language models and multimodal large language models has accelerated the emergence of proactive agents capable of operating everyday tools and assisting users in real-world environments. However, existing benchmarks struggle to evaluate such agents effectively, as they
中文介绍 UniClawBench是面向主动智能体在真实世界任务中的通用基准。覆盖多样化任务,系统评测智能体的规划、工具使用和交互能力,弥补现有基准难以评估主动操作日常工具能力的不足。
👍 27
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 针对非可验证指令跟随的RL,提出LLM-as-a-Tutor方法,实现策略感知的提示自适应。训练中动态调整LLM裁判的评分提示,使其与演化策略对齐,提高奖励信号准确性和训练效率。
👍 17
07/09 08:00
In this work, we present Canvas360, a two-stage framework for in-context panoramic generation that combines geometry-aware pretraining with downstream task-specific fine-tuning. To address the lack of large-scale, high-quality training data tailored to in-context panoramic tasks, we propose Canvas36
中文介绍 Canvas360是两阶段框架:先几何感知预训练解决全景生成数据不足,再微调下游任务。通过显式编码球面几何结构,使扩散模型在上下文全景生成中保持场景一致性。
👍 15
07/08 08:00
Modern LLMs are increasingly deployed in long-context applications such as retrieval-augmented generation, repository-level coding, and agentic workflows whose accumulated reasoning and tool traces routinely push the input an order of magnitude past the pretraining window, making zero-shot context e
中文介绍 Jet-Long提出动态双焦点RoPE,用于高效扩展LLM长上下文能力。通过位置编码的焦点动态调整,使模型零样本适应远超预训练窗口的输入长度,适用于RAG、代码理解和智能体工作流。
👍 15
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 京东推出Oxygen AIIC V1,以LLM/VLM为核心的工业级商品理解、管理与应用方案。面向超7亿用户和百亿级SKU,通过多模态大模型自动提取结构化商品知识,提升体验并降低成本。
👍 14
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 针对智能体RL中同步批处理的低效问题,提出单轨迹异步优化方法。在单次rollout内实时更新策略,无需等待批次完成,显著加速长时程代理任务的训练效率。
👍 13
07/09 08:00
Reasoning has become a core capability for large models, especially when reliable decisions require understanding logical consequences. Recent video generation models offer a reasoning path distinct from previous Chain-of-Thought (CoT): reasoning can unfold through temporally connected frames, known
中文介绍 OpenCoF通过视频生成进行推理,与CoT不同,推理在时序连接的视频帧中展开(temporal CoT)。利用扩散模型生成连贯视频序列,使模型在视觉动态中学习逻辑因果和时序推理。
👍 12
07/04 08:00
The growing demand for image-to-video creation on mobile devices has increasingly focused on cinematic motion effects like bullet time, dolly zoom, slow motion, etc. While Diffusion Transformers (DiTs) exhibit strong performance in video generation, their large parameter sizes and multi-step iterati
中文介绍 CineMobile面向移动设备,通过轻量化Diffusion Transformer实现图像到视频,生成子弹时间、推拉镜头等电影级相机运动。采用多步迭代压缩和知识蒸馏,适配端侧算力。
👍 11
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 RoboDojo是统一的仿真与真实机器人操控策略评估基准,覆盖多样化长时程任务,系统评测通用操控策略的泛化能力和鲁棒性,弥补现有基准在任务广度和跨平台评估上的不足。
👍 10
07/09 08:00
In long-horizon tasks, decision-relevant state is often scattered across an expanding trajectory, while the action agent must surface it and act. As trajectories grow, task requirements, environment facts, prior attempts, diagnoses, and open subgoals can be buried in the context window or pushed bey
中文介绍 提出主动记忆智能体,在长时程任务中主动检索并注入决策相关的历史信息(环境事实、先前尝试、未完成子目标等),避免关键信息被上下文窗口淹没,提升长期依赖任务完成率。
👍 10
07/08 08:00
Linear attention models allow a fixed state size and a fixed amount of compute per token. However, due to their limited state size, linear attention models fall behind in long-context recall compared to softmax-attention-based transformer architectures. Increasing the state size of linear attention
中文介绍 Sparse Delta Memory通过稀疏化增量记忆扩展线性RNN状态容量,在不增加每步计算量前提下大幅提升长上下文召回能力,弥合与softmax注意力模型在长程依赖上的差距。
👍 10
07/03 08:00
Embodied agents are typically built as hand-designed compositions of perception, memory, planning, and action modules. This modularity exposes a large architectural design space, but current systems still rely on researcher intuition to choose where information is stored, how observations are proces
中文介绍 提出自动化设计具身智能体架构的方法,将感知、记忆、规划和动作模块组合视为可搜索空间。通过演化或可微分搜索自动发现给定任务下最优模块连接与信息流,减少人工设计依赖。
👍 9
07/07 08:00
Humans can navigate an unfamiliar city and gradually form a coherent spatial mental map spanning tens of square kilometers. Can AI build spatial representations at a comparable scale? Although recent foundation models have advanced scene reconstruction and embodied intelligence, scaling to entire ci
中文介绍 WildCity是真实城市规模的渲染、仿真与空间智能测试床,提供数平方公里城市的照片级可交互数字孪生。用于评估大规模场景重建、空间导航和多模态空间表征,推动城市级空间智能研究。
👍 8
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 探索多模态大模型在令牌级动态中的注意力机制,分析生成过程中视觉与语言信息的交互模式。通过细粒度注意力分析揭示跨模态整合的阶段和策略,提升MLLM生成过程的可解释性。