👍 148
07/15 20:00
A growing gap separates inference context lengths from RL post-training: inference systems are approaching million-token contexts, while post-training workloads often remain at 256K tokens or below and rely on length generalization at deployment. The gap is especially important for AI agents, whose
中文介绍 针对推理上下文长度与强化学习后训练之间的差距,论文提出了 LongStraw 方法,旨在通过固定的 GPU 预算实现超过 2M Tokens 的长上下文强化学习。该方法特别适用于 AI 代理,能够在较大的上下文中进行有效的部署和推理。
👍 124
07/13 20:00
We introduce Boogu-Image-0.1, an open-source unified multimodal understanding and generation model family, comprising Base, Turbo, Edit, and Edit-Turbo variants. It delivers competitive performance in high-quality text-to-image generation, fast inference, instruction-based editing, and bilingual (Ch
中文介绍 Boogu-Image-0.1 是一种开放源代码的统一多模态理解与生成模型,包含多种变体(Base、Turbo、Edit、Edit-Turbo)。该模型在高质量文本生成图像、快速推理、基于指令的编辑以及双语能力等方面表现优异,推动了多模态生成研究的发展。
👍 116
07/15 20:00
Recent advances in video understanding have spanned motion, long video, and streaming interaction, driving this field toward real-world applications. Despite this progress, current open-source models remain limited in several ways. They often struggle to generalize across diverse video types, making
中文介绍 VideoChat3 是一款全面开放的视频多模态大语言模型,专注于高效的真实世界视频理解。尽管当前模型在视频类型的泛化能力上存在不足,VideoChat3 旨在克服这些局限,使之更适用于多样化的视频分析应用,推动视频智能的发展。
👍 86
07/13 20:00
Reinforcement learning with verifiable rewards without human-annotated data, often referred to as zero RL, has emerged as a powerful paradigm for eliciting chain-of-thought reasoning. However, due to computational constraints, existing studies are largely restricted to small models, leaving the trai
中文介绍 Ring-Zero 方法将强化学习与可验证奖励结合,推动了无人工注释数据的链式推理能力。然而,当前研究多限于小模型,训练效率低下。该工作旨在扩展到更大规模的模型,促进自我推理的多样性和深入性,具有重要的应用潜力。
👍 78
07/15 20:00
Large language models are increasingly trained as interactive agents for long-horizon tasks involving multi-turn interaction, tool use, and environment feedback. Outcome-based reinforcement learning (RL) provides a practical optimization paradigm, but its sparse trajectory-level rewards offer limite
中文介绍 SEED 提出了一种自我演化的在线策略蒸馏方法,专注于增强长时任务中的强化学习。尽管传统的基于结果的奖励机制在稀疏性上有局限性,SEED 通过动态更新策略,提升了多轮交互能力,为智能代理的自适应性提供了新的解决方案。
👍 54
07/15 20:00
Recent advances in Tool-Integrated Large Language Models have made web search a core capability of information-seeking agents. However, as interaction histories grow, agents increasingly struggle to track task progress. When search attempts fail to yield useful evidence, current single- and multi-ag
中文介绍 SearchOS-V1 旨在应对信息获取代理在庞大交互历史中的任务跟踪挑战,通过设计可协作的开放领域信息获取机制,提高搜索成效。其研究促进了智能代理在信息检索和处理方面的能力提升,为信息获取应用奠定了基础。
👍 39
07/15 20:00
World-action models (WAMs) are emerging as a promising foundation for embodied control: rather than predicting actions alone, they learn representations that couple action generation with future world prediction. This coupling is often viewed as a source of robustness, interpretability, and safety,
中文介绍 BadWAM 研究了世界-动作模型(WAMs)在预测未来世界与生成动作时的耦合性,强调其在稳健性、可解释性与安全性方面的潜力。该模型为发展更强大的控制策略提供了理论支持,推动了动态控制领域的研究进展。
👍 36
07/14 20:00
As Large Language Models (LLMs) evolve into autonomous agents, the need for unified evaluation infrastructure becomes critical. However, current evaluation pipelines remain highly fragmented and tightly coupled, hindering reproducibility and causing redundant engineering. To address this, we introdu
中文介绍 AgentCompass 展示了一种统一的评估基础设施,支持自适应代理的能力评估。现有评估流程的碎片化问题严重影響可重复性与工程效率,该工作通过标准化评估进程,为智能代理的性能比较提供了系统化支持。
👍 35
07/06 20:00
Image guardrails are typically trained and evaluated under a fixed safety policy, implicitly treating safety as an intrinsic property of an image. Real deployments are different: the same image may be allowed in one product, restricted in another, and newly disallowed when a policy boundary changes.
中文介绍 PolicyShiftGuard 针对基于政策自适应的图像保护机制进行评测和改进。通过优化安全政策的适应性,提升了图像的可接受性与实时部署的灵活性,为图像处理中的安全性保障提供了新思路。
👍 33
07/14 20:00
Video generation increasingly relies on keyframe-based workflows, where creators specify a sequence of reference images to guide generation. Although recent models support multi-keyframe conditioning, it remains unclear whether they can faithfully reproduce the prescribed keyframes while maintaining
中文介绍 KeyFrame-Compass 旨在全面评估基于关键帧的视频生成能力。尽管现有模型支持多关键帧的条件生成,但对目标关键帧的忠实再现能力仍然不足,从而限制了生成效果的真实性。该研究助力下一代视频生成的准确性,提高用户体验。
👍 30
07/14 20:00
Multi-reference-to-audio-video (MR2AV) generation aims to generate coherent audio-video content conditioned on multiple references and textual instructions. Existing benchmarks mainly focus on text-driven generation, single-reference subject preservation, or isolated audio-video alignment, leaving t
中文介绍 MultiRef-Compass 专注于对多参考音视频生成的综合评估,推动生成技术从单一文本驱动演变为多维参考协同。此研究填补了现有基准在多媒介联动生成方面的空白,是音视频创作领域的一个重要进展。
👍 30
07/12 20:00
Current visual generation models are capable of producing high-quality content, yet they lack a coherent perception of the spatial structure. Existing generative novel view synthesis methods typically introduce explicit geometry priors, which enforce spatial consistency but inherently restrict gener
中文介绍 MetaView 提出了基于单目视图合成的新方法,结合尺度感知的隐式几何先验,实现高质量视觉生成。然而,现有方法对空间结构的理解不足,该研究试图以更灵活的几何表示提升生成结果的连贯性与真实感。
👍 25
07/13 20:00
Learning broad world knowledge directly from raw visual data is a fundamental capability of intelligence. We introduce UniVR, the first investigation into simultaneously learning complex reasoning, fine-grained physical dynamics, and long-term planning from pure visual demonstrations. At its core, U
中文介绍 UniVR 是针对纯视觉数据学习广泛世界知识的重要研究,首度实现复杂推理、精细物理动态及长期规划的综合学习。该模型的核心在于探索视觉学习的潜能,为智能体的发展打开新的思路。
👍 25
07/13 20:00
Human cognition does not separate understanding and generation. A teacher at a whiteboard speaks and draws together, each modality reshapes the other. In this paper, we bring this coupled loop to artificial systems. Masked Diffusion Models (MDMs) are ideally suited to this task, yet existing sampler
中文介绍 该研究以人为认知为基础,提出了理解与生成的自我纠正耦合马尔可夫跳跃过程,推动了人工系统在这两方面的互动。Masked Diffusion Models(MDMs)被应用于此,展示了跨模态生成中的创新性。
👍 25
07/14 20:00
World Action Models (WAMs) improve robot policy learning by jointly modeling actions and future visual observations, using future scene evolution as dense supervision for physically grounded action generation. However, a common design in existing WAMs is to explicitly generate future videos at infer
中文介绍 GigaWorld-Policy-0.5 是一款更加快速且强大的世界动作模型,借助 AutoResearch 的助力,提升了机器人政策学习的质量。该模型通过未来视觉观察的联合建模,为物理基础的动作生成提供了密集监督,推动了机器人智能的发展。
👍 22
07/13 20:00
Self-improving autonomous agents are moving from research prototypes to deployed systems. The primary goal is controllable evolution, or adaptation, from experience with minimal or even no human input. This survey frames modern self-improving agents as adaptive systems that convert experience into a
中文介绍 文献综述了现代自我改进代理的发展,从研究原型转向实际应用,重点讨论如何在几乎无人工输入的情况下实现可控演变。该研究为自我学习和适应性系统的构建提供了框架,启发了智能代理研究的未来方向。
👍 20
07/02 20:00
Reinforcement learning has become a standard post-training recipe for large language models, but dense full-parameter updates create two deployment-relevant bottlenecks: suppressed reasoning performance, often reflected by premature saturation of test-time scaling, and interference when consolidatin
中文介绍 Spectral Rewiring 方法通过优化重构过程,改进强化学习在大语言模型中的应用,缓解了推理性能衰退和干扰问题。该研究为后训练流程中的模型优化提供新思路,具备较高的实用价值。
👍 19
07/14 20:00
Embodied cognition requires agents to connect high-level task reasoning with the physical states to be achieved. We introduce Hy-Embodied-RxBrain, an embodied cognition foundation model with joint language-visual reasoning and imagination. Unlike vision-language models that emphasize scene understan
中文介绍 RxBrain 提出了一个结合语言与视觉推理的体现认知基础模型,强调了高层次任务推理与实现物理状态之间的关联。该模型不仅提升了任务执行的效果,也为智能体在复杂环境下的表现提供了新的研究视角。
👍 16
07/15 20:00
We present Wan-Streamer v0.3, which reframes our native-streaming interaction model under a single organizing view: a video is a world plus an event stream. The world is the persistent context in which a video unfolds, including the environment, scene, subjects, ambient acoustic conditions, voice ch
中文介绍 论文提出了 Wan-Streamer v0.3 的新架构,重新定义了视频的概念为一个世界加上一个事件流,旨在提升视频内容的生成和理解能力。这种新的视角可能会为视频处理和智能代理的应用带来深远影响。
👍 16
07/13 20:00
Structured pruning is a hardware-friendly way to compress LLMs, but it is mostly validated on multiple-choice recognition tasks, while the same compressed checkpoints can collapse on the free-form generation that deployment actually requires. Two observations trace this gap. First, greedy pass@1 nea
中文介绍 ShortOPD 提出了短到长策略蒸馏方法,以恢复结构化剪枝的 LLMs。研究指出现有剪枝方法在自由表单生成中表现不佳,创新性地提出新策略,使压缩检查点能够保持其性能,有助于提升模型在实际部署中的有效性。