👍 65
07/06 20:00
Scaling robot learning requires massive, diverse trajectory data, yet collection is currently bottlenecked by physical teleoperation, where every demonstration binds operator time to specific hardware and workspaces. We introduce digital teleoperation, a paradigm that decouples data collection from
中文介绍 论文提出了一种数字化远程操作的新范式,以解决物理远程操作对数据收集造成的瓶颈。该方法通过将数据收集与操作控制解耦,支持大规模、多样化的轨迹数据收集。使用新模型进行实验,表明该方法在数据效率上有显著提升,可能推动机器人学习和自动化领域的发展。意义:为Agent和机器人学习提供新的数据收集方法。
👍 31
07/02 20:00
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 为了解决现代大语言模型在长文本处理中的计算成本问题,本文提出了一种分层稀疏注意力机制,通过对现有方法的改进,达到更好的上下文建模效果。实验结果显示,优化后的模型在处理长上下文时表现出更高的效率,能够支持无限长的上下文建模。意义:推动长文生成和理解的研究趋势。
👍 28
07/06 20:00
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 本研究将计算机视觉视为统一的多模态生成,提出了一种无任务特定架构的通用多模态模型SenseNova-Vision。该模型通过自然语言指令执行多样的视觉任务,表现出较强的跨模态生成能力。实验结果表明,该方法提升了多模态任务的处理效率,可能在视觉理解和生成领域产生广泛影响。意义:促进多模态生成和理解的研究进展。
👍 24
07/03 20:00
Unified multi-modal models (UMMs) have shown promising interleaved text-image reasoning capabilities, yet effectively optimizing such multi-turn generation via reinforcement learning (RL) remains an open challenge. Existing approaches apply RL exclusively to text steps, relegating image generation t
中文介绍 为了解决统一多模态模型在多轮生成中的优化问题,本文提出了一种强化学习框架,优化文本和图像生成过程。通过将强化学习扩展到图像生成步骤,模型在多模态推理上取得显著进展,提升了决策过程的有效性。结果表明,该方法在多模态推理中具有更好的生成效果,可能推动交互式生成系统的发展。意义:对多模态推理和决策支持系统有积极影响。
👍 23
06/30 20:00
Scientific literature search often requires more than retrieving papers from a single query: users' intents are underspecified, preference-dependent, and evolve through interaction. Existing search agents typically rely on fixed pipelines or implicit language-only reasoning, making their search stra
中文介绍 针对科学文献搜索的复杂性,本文提出了一种基于工作流归纳的多轮智能文献搜索方法。该方法通过用户交互实时调整搜索意图,克服了固定管道和语言推理的限制。实验表明,相比传统方法,该系统在用户满意度和搜索效率上显著提升,推动了文献检索系统的智能化发展。意义:提升文献搜索和信息检索领域的智能化水平。
👍 20
07/01 20:00
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 论文介绍了Gemma 4,一个新一代的开放权重多模态语言模型。Gemma 4在计算效率和推理能力上进行了优化,采用了密集以及混合专家架构,参数范围从2.3B到31B。实验结果显示,该模型在多模态任务上表现出优越的能力和效率,推动了多模态AI的发展。意义:促进大规模多模态模型的应用与研究进展。
👍 19
07/05 20:00
Agentic video understanding equips models with long-term memory to autonomously process and respond to continuous, long-horizon multimodal streams. However, advanced video agents often rely on ``detective-style'' iterative reasoning for action control (e.g., search) and evidence aggregation, incurri
中文介绍 研究提出了一种针对长时程视频理解的智能模型Light-Omni,具备长时记忆,能够自主处理和响应复杂的多模态视频流。该模型减少了传统推理模式的依赖,提高了视频操作的准确性和速度。实验结果显示,该方法在复杂视频理解任务中表现突出,为未来的智能视频处理提供了新思路。意义:推动智能视频理解和处理的研究方向。
👍 17
07/05 20:00
Key-value (KV) cache growth is a major bottleneck in autoregressive decoding, as memory and bandwidth scale linearly with context length. Existing KV eviction methods often rely on static heuristics or proxy scores, which poorly track future token utility and cause brittle eviction as relevance shif
中文介绍 本文提出了一种新的Key-Value缓存压缩方法KVpop,通过预测在线修剪来优化自回归解码过程。通过动态跟踪未来标记的实用性,该方法显著提高了缓存管理的效率并减少了资源消耗,提升了解码速度。实验结果显示,相较于静态方法,效率提高了显著水平。意义:对高效自回归生成任务的研究具有重要贡献。
👍 16
07/02 20:00
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 针对密集视频字幕生成任务,研究提出了一种并行自回归解码方法。此方法结合了事件级视频理解和生成,利用大语言模型的强大生成能力有效提升了字幕的时效性和准确性。实验结果表明,该方法在视频描述任务上具有更强的性能,推动了视频自动生成和理解领域的发展。意义:为视频字幕生成和理解提供了新的技术支持。
👍 15
07/02 20:00
While skill optimization for autonomous agents has gained traction, existing methods rely on complex pipelines. This leaves a fundamental question unaddressed: What constitutes a minimal viable pipeline for skill optimization, where every component is justified by theory or empirical necessity? We f
中文介绍 论文提出了一种名为SkillOpt-Lite的新方法,用于简化自主体的技能优化过程,强调理论和实证必要性。在所建立的框架下,验证了优化过程中各组件的有效性和必要性,得出较传统方法更优的技能发展路径。实验结果显示,该流程在效率和效果上有所提升,有助于进一步发展自主体的技能学习。意义:为自主体优化算法的研究提供了理论基础。
👍 13
07/05 20:00
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 文中提出了DSpark,一种信心调度的投机解码方法,结合半自回归生成框架,旨在加速大语言模型的推理过程。通过将草稿生成与目标验证解耦,该方法显著改善了长序列的生成效果。实验结果表明,DSpark在多种场景下有效提升了推理速度和准确性,推动快速解码技术的发展。意义:对大语言模型的推理效率提升具有重要影响。
👍 10
07/06 20:00
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 为了解决VLA模型在实际应用中的差距,本文提出了LingBot-VLA 2.0,改进了模型在功能性方面的表现。通过在三个功能领域的提升,该模型降低了实验室和现实世界之间的差异。实验结果显示,改进后模型在实际应用中表现卓越,推动了VLA模型的实际应用发展。意义:提升VLA模型对实际应用的适应性。
👍 10
07/02 20:00
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 论文介绍了MentalThink,一个视觉符号推理的框架,使多模态大语言模型具备了可执行的“心理”可视化机制。核心为think-with-SVG管线,模型学习生成、渲染和解释可缩放矢量图形。实验表明该方法在推理准确性和复杂性处理方面有显著优势,为多模态体裁的推理研究提供了新方向。意义:促进视觉符号推理和创作的研究。
👍 10
07/03 20:00
Vision-Language-Action (VLA) models acquire broad embodied capabilities through large-scale pretraining, yet their generalization remains far more fragile than that of LLMs and VLMs. The prevailing remedy, post-training via supervised fine-tuning or reinforcement learning, improves task-specific per
中文介绍 为提升Vision-Language-Action(VLA)模型的泛化能力,本文提出了一种新方法,通过提炼树搜索来优化动作评估。通过树搜索的数据精炼,模型在特定任务上的表现显著提升,验证了后训练的有效性。实验结果显示,该方法在多项任务中表现突出,有助于推动VLA模型的改进和应用。意义:为VLA模型的优化提供新思路。
👍 10
07/05 20:00
Audio intelligence involves understanding, reasoning about, and generating both audio and speech. In this work, we introduce Nemotron-Labs-Audex-30B-A3B (Audex), a unified audio-text LLM built on Nemotron-Cascade-2-30B-A3B, a strong text-only MoE LLM. Audex adopts a simple unified design with a sing
中文介绍 这项研究提出了Audio Intelligence的统一模型Nemotron-Labs-Audex,以处理音频和文本指令。Audex在设计上简化了框架,以提高音频和语言的理解与生成能力。实验结果表明,该模型在音频处理领域表现出色,造成了音频智能技术的进一步发展。意义:推动音频处理与理解的结合发展。
👍 9
07/06 20:00
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 本文提出了一种面向长时程代理训练的On-policy Distillation方法TurnOPD。通过识别代理任务中的两大关键效率问题,优化了该方法在多轮任务中的应用,提升了训练效率。实验结果表明,新方法在长时程任务上有效减少了训练时间和计算资源消耗,对代理训练的优化研究具有重要意义。意义:提升代理训练效率和有效性。
👍 8
07/05 20:00
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 文中介绍了CanvasAgent,该模型通过视觉工具编排实现复杂图像创作与编辑。该方法整合了图像合成、对象定位、区域分割和内容编辑等功能,明显提升了多功能图像处理的能力。实验表明,该系统在执行复杂图像任务时表现突出,为未来图像生成和编辑技术的融合提供了新的途径。意义:推动图像生成与编辑技术的进步。
👍 7
06/29 20:00
Large language models increasingly operate over long contexts, where the KV cache becomes a dominant memory bottleneck: its size grows linearly with sequence length and must be retained throughout decoding, making full GPU caching prohibitively expensive without compression. Existing KV cache compre
中文介绍 为了解决大语言模型在长上下文推理中的KV缓存瓶颈,本文提出了一种分辨率自适应的KV缓存管理方案SeKV。该方法通过层次语义记忆提高缓存效能,显著优化了长序列下的解码过程。实验结果显示,该方法在长上下文任务中取得了更好的推理效果,推动了大语言模型的进阶应用。意义:助力长上下文推理和优化的研究。
👍 6
07/01 20:00
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 本文介绍了PointDiT,一种基于像素空间扩散的新方法,旨在提升单图像3D重建的精度和效率。通过简化模型结构和损失函数,该方法减轻了现有技术的复杂性,优化了重建结果。实验统计表示,PointDiT在重建精度和处理速度上都较前沿方法有显著提升,推动了3D计算机视觉的发展。意义:促进3D重建技术的创新与应用。
👍 6
07/05 20:00
Group Relative Policy Optimization (GRPO) is effective when the current policy already samples useful reasoning trajectories, but it stalls on hard prompts whose correct solution modes lie outside the student's on-policy support. We propose TREK (Teacher-Routed Exploration via Forward KL), a simple
中文介绍 本研究提出了TREK,一种通过教师引导的探索与强化学习方法。该方法针对群体相对策略优化(GRPO)中的困难任务,显著改善了样本效率和推理能力,使得模型能够更好地应对复杂提示。实验结果验证了TREK在多任务学习上的优越性,有助于推动智能系统的自主学习能力。意义:提升智能体在复杂任务中的适应能力与探索能力。