👍 105
06/23 08:00
Memory for large language model (LLM) agents has rapidly evolved from simple retrieval-augmented mechanisms into a data management system that supports persistent information storage, retrieval, update, consolidation, and dynamic lifecycle governance throughout agent execution. Despite this evolutio
中文介绍 探讨LLM智能体记忆系统从简单检索增强到支持持久化存储、更新、合并和生命周期管理的数据管理系统的演进。分析当前能力与挑战,评估「智能体原生」记忆系统的可行性。
👍 50
06/25 08:00
Modern Vision-Language-Action (VLA) models often fail to generalize to novel setups, such as altered camera viewpoints or robot morphologies, because they are typically conditioned only on current observations and language instructions. By ignoring the underlying system configuration as a variable,
中文介绍 针对VLA模型难以泛化到新视角或机器人形态的问题,提出上下文世界模型,将系统配置作为隐变量,使模型在推理时自适应调整,提升对未知场景的零样本泛化能力。
👍 46
06/25 08:00
Outcome-based reinforcement learning provides a stable optimization backbone for language agents, but its sparse trajectory-level rewards provide little guidance on which intermediate decisions should be reinforced or suppressed. On-policy self-distillation offers dense token-level supervision, yet
中文介绍 提出OPID在策略技能蒸馏方法,解决基于结果的RL对中间决策监督不足的问题。结合稀疏轨迹奖励与密集token级监督,实现语言智能体的稳定高效训练,避免自蒸馏偏差。
👍 45
06/24 08:00
Real-world photography requires capture-time guidance for both camera framing and subject pose. Yet existing aesthetic cropping benchmarks mainly evaluate post-hoc crop prediction and overlook subject-side recommendations, leaving the capture-time guidance capabilities of multimodal large language m
中文介绍 ShutterMuse利用多模态大语言模型为拍摄提供实时构图和主体姿势指导。现有基准多评估后期裁剪而忽略拍摄时建议,ShutterMuse填补这一空白,实现拍摄时刻的智能引导。
👍 42
06/25 08:00
While text-to-image (T2I) models have achieved remarkable progress, they struggle with real-world requests that are often underspecified, implicit, or dependent on up-to-date knowledge. We identify this challenge as the Context Gap: the mismatch between the user context and the sufficient generation
中文介绍 提出Qwen-Image-Agent,弥合真实图像生成中的「语境差距」——用户请求往往模糊、隐含或依赖最新知识。通过智能体框架融合外部知识检索与推理,生成更符合用户意图的图像。
👍 41
06/24 08:00
A classical intuition holds that verifying a solution is easier than producing one. For today's coding agents, this intuition is being inverted: as foundation models develop stronger reasoning capabilities and engineering harnesses grow more sophisticated, generating complex candidate solutions is n
中文介绍 挑战「验证比生成容易」的传统直觉。随着编码智能体能力增强,生成复杂解变得相对容易,验证正确性成为瓶颈。揭示「验证地平线」现象,指出无银弹式奖励设计。
👍 38
06/25 08:00
A unified representation for text and vision is a natural pursuit, as it enables simpler multimodal modeling and more efficient training. However, representing images as discrete signals in the same way as text inevitably introduces severe information loss. Existing work struggles to balance low-lev
中文介绍 ViQ提出任意分辨率下文本对齐的视觉量化表示。通过可学习量化器和分辨率自适应机制,在保持离散化高效性的同时减少信息损失,实现文本与视觉的统一表征。
👍 34
06/24 08:00
Synthesizing a novel-view video from a monocular reference video along a target camera trajectory requires both geometric consistency and motion fidelity with respect to the reference video. Existing methods based on explicit 3D representations are limited by the accuracy of off-the-shelf reconstruc
中文介绍 MVTrack4Gen利用多视角点追踪作为几何监督,从单目参考视频生成新视角视频。通过隐式3D表示和点轨迹约束,同时保证几何一致性和运动保真度,避免显式重建的精度瓶颈。
👍 31
06/25 08:00
Speculative decoding (SD) accelerates autoregressive Large Language Models (LLMs) by drafting multiple tokens and verifying them in parallel, but it faces a scaling limitation: increasing the draft budget improves speed only when acceptance remains high and drafting overhead stays low. This ceiling
中文介绍 JetSpec通过并行树草稿生成突破推测解码的缩放天花板。以树状结构替代线性草稿,在保持高接受率的同时降低草稿开销,显著加速LLM推理。
👍 28
06/22 08:00
Computer-use agents can execute software tasks through either graphical interfaces or programmatic command interfaces, but existing evaluations confound interaction modality with differences in tasks, initial states, verifiers, and permitted actions. We introduce a matched execution-layer benchmark
中文介绍 构建匹配的执行层基准,系统比较GUI与CLI两种计算机使用智能体的执行瓶颈。发现屏幕智能体受限于视觉感知,技能中介智能体受限于API覆盖,揭示不同交互模态的优劣。
👍 26
06/24 08:00
Fine-grained visual reasoning requires multimodal large language models (MLLMs) to identify task-relevant visual evidence and ground their reasoning in local image regions. Existing agentic methods typically rely on reinforcement learning with verifiable rewards or supervised fine-tuning on large-sc
中文介绍 V-Zero提出免答案标签的在策略蒸馏方法,结合对比证据门控机制,用于细粒度视觉推理。通过智能体自探索和证据筛选,提升MLLM定位局部视觉证据的能力。
👍 24
06/19 08:00
Generating a coherent multi-shot video requires structured cross-shot memory. Subject appearance, scene context, and speaker identity must persist across cuts. Existing approaches either train end-to-end over fixed-length sequences and cannot scale, generate shot-by-shot with memory banks that grow
中文介绍 UnityShots提出记忆驱动的多镜头音视频生成方法,通过边界感知门控机制维护跨镜头连贯性。解决现有方法难以扩展或记忆膨胀的问题,实现可变长度多镜头生成。
👍 23
06/24 08:00
Joint-Embedding Predictive Architectures (JEPAs), including recent LeWorldModel (LeWM), have become a promising foundation for reconstruction-free visual world models. For visual planning, however, LeWM evaluates candidate action sequences by repeatedly applying a local one-step latent transition mo
中文介绍 Fast LeWorldModel加速联合嵌入预测架构(JEPA)在视觉规划中的应用。通过优化潜在过渡模型的推理方式,避免逐步骤重复计算,大幅提升动作序列评估效率。
👍 17
06/25 08:00
As agentic systems continue to evolve and are widely deployed in real-world scenarios, there is a growing demand to faithfully evaluate their capabilities. However, current benchmarks are typically built on popular applications with relatively simple tasks and focus on a narrow set of capabilities w
中文介绍 重新评估智能体在陌生环境中的能力。指出现有基准局限于流行应用和简单任务,构建更具挑战性的评估套件,涵盖复杂、多步骤、跨域任务,揭示智能体在非熟悉场景下的性能退化。
👍 16
06/24 08:00
Tool use enables large language models (LLMs) to perform complex tasks, and recent agentic reinforcement learning (RL) methods show promise for enhancing model capabilities. However, RL alone often leads to instability or limited gains in tool-use tasks. In our experiments, some models exhibit catas
中文介绍 分析多步工具使用RL训练崩溃的原因:稀疏奖励导致策略退化。提出引入辅助监督信号(如工具使用步骤正确性)稳定训练,显著提升模型在复杂工具调用任务上的表现。
👍 13
06/25 08:00
The prevalent dual-branch paradigm, i.e., training a side network to encode visual conditions and fusing its intermediate-layer features to a frozen pretrained main network, has shown remarkable success in visual-condition controllable generation. Despite its widespread adoption, the role of the sid
中文介绍 LISA通过似然分数对齐优化视觉条件可控生成。揭示现有双分支框架中侧网络特征融合的偏差,提出对齐主网络与侧网络的似然分布,提升生成质量与条件控制精度。
👍 13
06/24 08:00
We introduce Autodata, a general method that enables AI agents to act as data scientists who build high quality training and evaluation data. We show how to train (meta-optimize) such a data scientist agent, so that it learns to create even stronger data. We describe the overall formulation, and a s
中文介绍 Autodata让AI智能体扮演数据科学家,自动生成高质量训练和评估数据。通过元优化训练数据生成策略,使其学会创建更优数据,提升下游模型性能。
👍 9
06/25 08:00
Reasoning capability has advanced rapidly in large language models (LLMs), leading to an increasing size of key-value (KV) cache in both prefilling and decoding stages. Existing KV cache compression methods mainly rely on attention weights to estimate token importance. While attention effectively ca
中文介绍 提出信息感知的KV缓存压缩方法,用于长序列推理。超越单纯依赖注意力权重,结合信息论度量评估token重要性,在保持推理质量的同时显著减少缓存大小,降低显存占用。
👍 9
06/25 08:00
Video reasoning language models implicitly assume that every input frame is equally reliable. This leads to what we term the Blind Trust Problem: under realistic perturbations such as motion blur, glare, or occlusion, frontier video reasoning models can suffer 15-30%p accuracy drops on real-world em
中文介绍 提出置信度感知的工具编排方案,解决视频推理中的「盲目信任」问题。动态评估每帧可靠性,选择性调用不同分析工具,在运动模糊、遮挡等扰动下准确率提升15–30%。
👍 9
06/25 08:00
We present PhysiFormer, a diffusion transformer for physically-plausible 3D object motion. Unlike video world models that operate in view-dependent pixel space, PhysiFormer represents objects as 3D meshes expressed in world coordinates. Given the initial vertex positions and velocities, as well as o
中文介绍 PhysiFormer是一种扩散Transformer,用于学习物理合理的3D物体运动。与世界空间中的3D网格表示交互,避免视点依赖的像素空间限制,从初始顶点位置和速度预测未来运动。