👍 84
07/07 20:00
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 论文探讨了结构-属性关系在生物、化学和材料科学中的重要性,提出了一种深度原生结构推理方法,以机制性地解释这些关系。通过解释结构证据,模型能够揭示出功能反应背后的空间、化学和周期性组织。其贡献在于提高了对复杂结构-属性关系的准确理解,对各领域的交叉研究具有重要意义。
👍 55
07/01 20:00
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 在视频理解领域,现有基准难以有效评估模型的视觉感知、语言推理和知识先验。为此,本文提出了Video-Oasis,一个更新评估视频理解性能的方法,旨在解决高层次推理的评估标准问题,从而促进视频理解技术的发展,具有重要实际应用潜力。
👍 51
07/07 20:00
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 为了克服现有视觉-语言-行动(VLA)模型在长时间依赖任务中的局限,本文提出了双潜在记忆方法。该模型通过整合历史信息与当前观察,优化了对复杂任务的预测能力。其有效性体现在提高了长时间跨度内的决策能力,对机器人操作任务具有显著影响。
👍 31
07/08 20:00
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 本文提出IdeaGene-Bench(IG-Bench),一个用于科学血系推理和基于血系的创意生成的基准。该基准模拟科学思想的继承机制,使AI系统能够遵循这样的结构,评估其在创新过程中的表现,推动科学思维和创意生成相关研究的进一步发展。
👍 31
07/07 20:00
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 LingBot-World 2.0引入了四个显著的改进,致力于在长交互范围内保持输出质量。该模型通过精心设计的因果预训练策略,使智能体能够在无界的交互环境中有效操作,这对于促进AI在复杂交互场景中的应用至关重要。
👍 27
07/08 20:00
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 为了解决从稀疏事件流中恢复高质量视频的挑战,本文提出了LongE2V模型,结合了预训练视频扩散先验,联合处理事件基础的视频重构与预测。此方法减少了生成中的模糊现象,展示了在长时间视频生成中的显著优势,推动了视频生成技术的进步。
👍 27
07/08 20:00
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是一个旨在全面评估主动智能体在真实任务中表现的通用基准。随着多模态大型语言模型的发展,该基准帮助解决现有评估工具的不足,为有效评估主动智能体的能力提供了新的方法,对智能应用具有重要的促进作用。
👍 27
07/04 20:00
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 针对非可验证指令遵循中的强化学习,本文提出了LLM-as-a-Tutor的方法,通过动态调整训练过程中固定的提示以适应政策变化。该方法为提高学习过程的适应性和效率提供了新的思路,对强化学习方向的研究发展具有重要意义。
👍 17
07/08 20:00
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是一种结合几何感知预训练与特定下游任务微调的两阶段框架,专注于上下文全景生成。针对缺乏高质量训练数据的挑战,提出了Canvas360的解决方案,推动了全景生成技术在复杂场景下的应用发展。
👍 15
07/07 20:00
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模型通过动态双焦点RoPE技术,提升了长上下文应用中的效率。这一方法旨在应对当前LLM在累积推理和工具使用中的输入限制,具有重要的应用潜力,特别是在需要高效信息检索的场景中。
👍 15
06/28 20:00
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 JD Oxygen AI Item Center (Oxygen AIIC) V1提出了一种工业规模的以LLM/VLM为中心的项目理解和管理解决方案,以提升用户体验和降低运营成本。此研究在电商领域具有广泛的实际应用前景,推动了智能决策和商品管理的进步。
👍 14
07/07 20:00
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 提出单回合异步优化方法,提升了后训练大型语言模型(LLMs)的强化学习效率。通过打破传统同步批处理方式,本文显著改善了针对长时间跨度任务的学习效率,为实现更复杂的智能行为奠定了良好基础。
👍 13
07/08 20:00
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模型通过视频生成实现推理,利用时间上连接的帧进行推理,建立了一种新的推理路径。该方法的提出为理解逻辑后果和智能决策提供了新的视角,具有广泛应用于视频智能分析和生成的潜力。
👍 12
07/03 20:00
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针对移动设备上的图像到视频生成,引入了对电影级相机运动效果的关注。尽管运动扩散变换器在视频生成中表现优异,但其大型参数和多步迭代的特性限制了应用的灵活性,论文探讨了克服这些限制的可行途径。
👍 11
07/06 20:00
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提供了一个统一的仿真与真实评估基准,旨在全面评估通用机器人操作政策。该模型推动了机器人技术的系统化评估,提升了其在更复杂任务中的表现,具有重要的应用价值和前景。
👍 10
07/08 20:00
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 为了解决长时间跨度任务中信息散布的问题,本文提出了一种主动记忆智能体。该智能体通过更高效的状态管理和信息提取策略,使决策相关信息的使用更为高效,为长时间智能体操作提供了新思路。
👍 10
07/07 20:00
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 本文探讨了通过稀疏性来扩展线性RNN的状态,以提高其在长上下文回忆中的能力。通过优化状态大小,模型在处理复杂输入时显示出提高的效率,为进一步提升生成效率与精准度提供了基础。
👍 10
07/02 20:00
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 本文探讨了自动化设计具身代理架构的方法,以减少对研究人员直观设计的依赖。通过智能模块的组合,可以更高效地存储信息和处理观察,对具身智能体的设计与应用具有重要的意义。
👍 9
07/06 20:00
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项目旨在构建一个现实世界城市级的测试平台,以评估AI在城市范围内的空间智能能力。该测试平台的建立为AI在复杂环境下的空间推理与导航能力的提升提供了新的研究方向,具有重要的现实应用价值。
👍 8
07/03 20:00
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 本文关注多模态大型语言模型(MLLM)在生成响应时的注意力机制,探讨了一种逐令牌的生成方式。通过深入研究这些模型的计算动态,旨在提高多模态生成的可解释性,对理解模型行为提供了新的视角。