HuggingFace 每日AI论文速递 - 节目列表

【赞助商】通勤路上就听AI每周谈。AI每周谈，每周带你回顾上周AI大事传送门 🔗https://www.xiaoyuzhoufm.com/podcast/688a34636f5a275f1cba40fd【目录】本期的 15 篇论文如下：[00:32] 🔍 UniG2U-Bench: Do Unified Models Advance Multimodal Understanding?（UniG2U-Bench：统一模型是否推动了多模态理解的发展？）[01:40] 🧩 Utonia: Toward One Encoder for All Point Clouds（Utonia：迈向适用于所有点云的统一编码器）[02:21] 🔍 BeyondSWE: Can Current Code Agent Survive Beyond Single-Repo Bug Fixing?（超越SWE：当前代码智能体能否在单仓库缺陷修复之外生存？）[03:00] 🔍 Beyond Language Modeling: An Exploration of Multimodal Pretraining（超越语言建模：多模态预训练的探索）[03:53] 🧠 Beyond Length Scaling: Synergizing Breadth and Depth for Generative Reward Models（超越长度缩放：融合广度与深度以优化生成式奖励模型）[04:40] 🎯 How Controllable Are Large Language Models? A Unified Evaluation across Behavioral Granularities（大型语言模型的可控性如何？跨行为粒度的统一评估）[05:16] 🎬 Kling-MotionControl Technical Report（Kling-MotionControl技术报告）[05:58] 🎬 Kiwi-Edit: Versatile Video Editing via Instruction and Reference Guidance（Kiwi-Edit：基于指令与参考引导的通用视频编辑）[07:01] 🤖 Qwen3-Coder-Next Technical Report（Qwen3-Coder-Next技术报告）[07:46] 🧠 PRISM: Pushing the Frontier of Deep Think via Process Reward Model-Guided Inference（PRISM：通过过程奖励模型引导的推理推动深度思考前沿）[08:30] 🔍 InfoPO: Information-Driven Policy Optimization for User-Centric Agents（InfoPO：面向用户中心智能体的信息驱动策略优化）[09:29] 🔬 Surgical Post-Training: Cutting Errors, Keeping Knowledge（手术式后训练：精准修正错误，稳固保留知识）[10:14] 🎛 CFG-Ctrl: Control-Based Classifier-Free Diffusion Guidance（CFG-Ctrl：基于控制的Classifier-Free扩散引导）[10:53] 🎬 NOVA: Sparse Control, Dense Synthesis for Pair-Free Video Editing（NOVA：稀疏控制与密集合成的无配对视频编辑框架）[11:58] ⚡ Spilled Energy in Large Language Models（大语言模型中的能量溢出）【关注我们】您还可以在以下平台找到我们，获得播客内容以外更多信息小红书: AI速递在小宇宙查看该单集文稿

【赞助商】通勤路上就听AI每周谈。AI每周谈，每周带你回顾上周AI大事传送门 🔗https://www.xiaoyuzhoufm.com/podcast/688a34636f5a275f1cba40fd【目录】本期的 15 篇论文如下：[00:30] ⚡ From Scale to Speed: Adaptive Test-Time Scaling for Image Editing（从规模到速度：图像编辑的自适应测试时扩展）[01:16] 🎨 OmniLottie: Generating Vector Animations via Parameterized Lottie Tokens（OmniLottie：通过参数化Lottie令牌生成矢量动画）[01:57] 🤖 OpenAutoNLU: Open Source AutoML Library for NLU（OpenAutoNLU：面向自然语言理解的开源自动机器学习库）[02:37] 🧩 MMR-Life: Piecing Together Real-life Scenes for Multimodal Multi-image Reasoning（MMR-Life：拼凑真实生活场景以实现多模态多图像推理）[03:32] 📊 RubricBench: Aligning Model-Generated Rubrics with Human Standards（RubricBench：对齐模型生成的评分标准与人类标准）[04:16] 🧠 CHIMERA: Compact Synthetic Data for Generalizable LLM Reasoning（CHIMERA：用于通用大语言模型推理的紧凑合成数据集）[05:04] 🔍 VGGT-Det: Mining VGGT Internal Priors for Sensor-Geometry-Free Multi-View Indoor 3D Object Detection（VGGT-Det：挖掘VGGT内部先验实现无需传感器几何的多视角室内3D目标检测）[06:08] 🤖 CoVe: Training Interactive Tool-Use Agents via Constraint-Guided Verification（CoVe：通过约束引导验证训练交互式工具使用智能体）[06:50] ⚙ SWE-rebench V2: Language-Agnostic SWE Task Collection at Scale（SWE-rebench V2：大规模语言无关的软件工程任务集合）[07:37] 📊 Spectral Condition for $μ$P under Width-Depth Scaling（宽度-深度缩放下 $μ$P 的光谱条件）[08:21] 🎬 WorldStereo: Bridging Camera-Guided Video Generation and Scene Reconstruction via 3D Geometric Memories（WorldStereo：通过3D几何记忆桥接相机引导视频生成与场景重建）[09:08] 🧠 LLaDA-o: An Effective and Length-Adaptive Omni Diffusion Model（LLaDA-o：一种高效且长度自适应的全能扩散模型）[10:11] 🧠 Efficient RLVR Training via Weighted Mutual Information Data Selection（基于加权互信息数据选择的高效RLVR训练方法）[10:48] 🧠 Learn Hard Problems During RL with Reference Guided Fine-tuning（通过参考引导微调在强化学习中学习难题）[11:51] 🔬 When Does RL Help Medical VLMs? Disentangling Vision, SFT, and RL Gains（强化学习何时助力医学视觉语言模型？解构视觉、监督微调与强化学习的增益）【关注我们】您还可以在以下平台找到我们，获得播客内容以外更多信息小红书: AI速递在小宇宙查看该单集文稿

【赞助商】通勤路上就听AI每周谈。AI每周谈，每周带你回顾上周AI大事传送门 🔗https://www.xiaoyuzhoufm.com/podcast/688a34636f5a275f1cba40fd【目录】本期的 15 篇论文如下：[00:29] 🛠 dLLM: Simple Diffusion Language Modeling（dLLM：简单的扩散语言建模）[01:15] 🧠 Enhancing Spatial Understanding in Image Generation via Reward Modeling（通过奖励建模增强图像生成中的空间理解）[02:11] 🌍 Recovered in Translation: Efficient Pipeline for Automated Translation of Benchmarks and Datasets（在翻译中恢复：自动化基准测试与数据集翻译的高效流程）[03:08] ⚡ CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation（CUDA Agent：用于高性能CUDA内核生成的大规模智能体强化学习系统）[03:59] 🎬 Mode Seeking meets Mean Seeking for Fast Long Video Generation（模式寻求与均值寻求相遇：实现快速长视频生成）[04:44] 🧩 Compositional Generalization Requires Linear, Orthogonal Representations in Vision Embedding Models（组合泛化要求视觉嵌入模型具备线性正交表示）[05:31] ⚡ LK Losses: Direct Acceptance Rate Optimization for Speculative Decoding（LK损失函数：用于推测解码的直接接受率优化）[06:21] 🔍 CiteAudit: You Cited It, But Did You Read It? A Benchmark for Verifying Scientific References in the LLM Era（CiteAudit：你引用了它，但你读过吗？大语言模型时代科学参考文献验证基准）[07:16] ⚡ Accelerating Masked Image Generation by Learning Latent Controlled Dynamics（通过学习潜在控制动力学加速掩码图像生成）[08:00] 🧠 Memory Caching: RNNs with Growing Memory（记忆缓存：具有增长记忆能力的循环神经网络）[08:38] 📊 InfoNCE Induces Gaussian Distribution（InfoNCE诱导高斯分布）[09:28] 🧠 Ref-Adv: Exploring MLLM Visual Reasoning in Referring Expression Tasks（Ref-Adv：探索多模态大语言模型在指代表达任务中的视觉推理能力）[10:28] ⚡ SenCache: Accelerating Diffusion Model Inference via Sensitivity-Aware Caching（SenCache：基于敏感度感知的缓存加速扩散模型推理）[11:15] 🎬 LongVideo-R1: Smart Navigation for Low-cost Long Video Understanding（LongVideo-R1：面向低成本长视频理解的智能导航）[11:53] ⚡ Vectorizing the Trie: Efficient Constrained Decoding for LLM-based Generative Retrieval on Accelerators（向量化字典树：面向加速器的高效约束解码用于基于LLM的生成式检索）【关注我们】您还可以在以下平台找到我们，获得播客内容以外更多信息小红书: AI速递在小宇宙查看该单集文稿

【赞助商】通勤路上就听AI每周谈。AI每周谈，每周带你回顾上周AI大事传送门 🔗https://www.xiaoyuzhoufm.com/podcast/688a34636f5a275f1cba40fd【目录】本期的 5 篇论文如下：[00:39] TOP1(🔥491) | 🧠 A Very Big Video Reasoning Suite（一个超大规模视频推理套件）[02:33] TOP2(🔥246) | 💭 Does Your Reasoning Model Implicitly Know When to Stop Thinking?（你的推理模型是否隐含地知道何时停止思考？）[04:48] TOP3(🔥215) | ⚖ VESPO: Variational Sequence-Level Soft Policy Optimization for Stable Off-Policy LLM Training（VESPO：用于稳定离策略LLM训练的变分序列级软策略优化）[07:29] TOP4(🔥187) | 🌍 The Trinity of Consistency as a Defining Principle for General World Models（一致性三位一体：作为通用世界模型定义原则）[09:43] TOP5(🔥146) | 🔍 From Blind Spots to Gains: Diagnostic-Driven Iterative Training for Large Multimodal Models（从盲点到增益：诊断驱动的迭代训练用于大型多模态模型）【关注我们】您还可以在以下平台找到我们，获得播客内容以外更多信息小红书: AI速递在小宇宙查看该单集文稿

【赞助商】通勤路上就听AI每周谈。AI每周谈，每周带你回顾上周AI大事传送门 🔗https://www.xiaoyuzhoufm.com/podcast/688a34636f5a275f1cba40fd【目录】本期的 15 篇论文如下：[00:31] 🔍 From Blind Spots to Gains: Diagnostic-Driven Iterative Training for Large Multimodal Models（从盲点到增益：诊断驱动的迭代训练用于大型多模态模型）[01:16] 🌍 The Trinity of Consistency as a Defining Principle for General World Models（一致性三位一体：作为通用世界模型定义原则）[01:49] 🧭 MobilityBench: A Benchmark for Evaluating Route-Planning Agents in Real-World Mobility Scenarios（MobilityBench：一个用于评估现实世界移动场景中路径规划智能体的基准）[02:52] 🧠 OmniGAIA: Towards Native Omni-Modal AI Agents（OmniGAIA：迈向原生全模态人工智能体）[03:44] 🔍 Imagination Helps Visual Reasoning, But Not Yet in Latent Space（想象力助力视觉推理，但尚未在潜在空间中实现）[04:26] 🧠 Exploratory Memory-Augmented LLM Agent via Hybrid On- and Off-Policy Optimization（基于混合在线与离线策略优化的探索性记忆增强大语言模型智能体）[05:26] 🛡 AgentDropoutV2: Optimizing Information Flow in Multi-Agent Systems via Test-Time Rectify-or-Reject Pruning（AgentDropoutV2：通过测试时修正或拒绝剪枝优化多智能体系统中的信息流）[06:18] 🔍 Search More, Think Less: Rethinking Long-Horizon Agentic Search for Efficiency and Generalization（多搜索，少思考：重新思考长视野智能搜索的效率与泛化性）[06:54] 🩺 MediX-R1: Open Ended Medical Reinforcement Learning（MediX-R1：开放式医学强化学习框架）[07:42] ⚡ Accelerating Diffusion via Hybrid Data-Pipeline Parallelism Based on Conditional Guidance Scheduling（基于条件引导调度的混合数据-流水线并行加速扩散模型）[08:43] 🤖 EmbodMocap: In-the-Wild 4D Human-Scene Reconstruction for Embodied Agents（EmbodMocap：面向具身智能体的野外4D人-场景重建）[09:41] 🎮 AI Gamestore: Scalable, Open-Ended Evaluation of Machine General Intelligence with Human Games（AI游戏商店：通过人类游戏对机器通用智能进行可扩展、开放式评估）[10:26] 🚶 Causal Motion Diffusion Models for Autoregressive Motion Generation（因果运动扩散模型用于自回归运动生成）[11:09] ⚡ veScale-FSDP: Flexible and High-Performance FSDP at Scale（veScale-FSDP：大规模灵活且高性能的FSDP）[11:51] 🚗 Risk-Aware World Model Predictive Control for Generalizable End-to-End Autonomous Driving（面向可泛化端到端自动驾驶的风险感知世界模型预测控制）【关注我们】您还可以在以下平台找到我们，获得播客内容以外更多信息小红书: AI速递在小宇宙查看该单集文稿

【赞助商】通勤路上就听AI每周谈。AI每周谈，每周带你回顾上周AI大事传送门 🔗https://www.xiaoyuzhoufm.com/podcast/688a34636f5a275f1cba40fd【目录】本期的 15 篇论文如下：[00:31] ⚗ MolHIT: Advancing Molecular-Graph Generation with Hierarchical Discrete Diffusion Models（MolHIT：基于分层离散扩散模型推进分子图生成）[01:08] 🎭 DreamID-Omni: Unified Framework for Controllable Human-Centric Audio-Video Generation（DreamID-Omni：可控人本音视频生成统一框架）[01:49] 🧪 ARLArena: A Unified Framework for Stable Agentic Reinforcement Learning（ARLArena：一个用于稳定智能体强化学习的统一框架）[02:40] ⚡ HyTRec: A Hybrid Temporal-Aware Attention Architecture for Long Behavior Sequential Recommendation（HyTRec：一种用于长行为序列推荐的混合时序感知注意力架构）[03:22] 🎬 SkyReels-V4: Multi-modal Video-Audio Generation, Inpainting and Editing model（SkyReels-V4：多模态视频-音频生成、修复与编辑模型）[04:10] 🎮 Solaris: Building a Multiplayer Video World Model in Minecraft（Solaris：在《我的世界》中构建多人视频世界模型）[05:20] 🤖 GUI-Libra: Training Native GUI Agents to Reason and Act with Action-aware Supervision and Partially Verifiable RL（GUI-Libra：通过动作感知监督和部分可验证强化学习训练原生GUI智能体进行推理与行动）[06:19] 🎬 JavisDiT++: Unified Modeling and Optimization for Joint Audio-Video Generation（JavisDiT++：面向联合音视频生成的统一建模与优化）[07:11] 🌐 Image Generation with a Sphere Encoder（使用球面编码器的图像生成）[07:51] 🧭 World Guidance: World Modeling in Condition Space for Action Generation（世界引导：基于条件空间的世界建模用于动作生成）[08:31] 🔍 NanoKnow: How to Know What Your Language Model Knows（NanoKnow：如何知晓你的语言模型知道什么）[09:10] ⚡ DualPath: Breaking the Storage Bandwidth Bottleneck in Agentic LLM Inference（DualPath：打破智能体化大语言模型推理中的存储带宽瓶颈）[10:11] 🧠 The Design Space of Tri-Modal Masked Diffusion Models（三模态掩码扩散模型的设计空间研究）[10:46] 🔤 VecGlypher: Unified Vector Glyph Generation with Language Models（VecGlypher：基于语言模型的统一矢量字形生成）[11:20] ⚡ SeaCache: Spectral-Evolution-Aware Cache for Accelerating Diffusion Models（SeaCache：一种用于加速扩散模型的频谱演化感知缓存）【关注我们】您还可以在以下平台找到我们，获得播客内容以外更多信息小红书: AI速递在小宇宙查看该单集文稿

【赞助商】通勤路上就听AI每周谈。AI每周谈，每周带你回顾上周AI大事传送门 🔗https://www.xiaoyuzhoufm.com/podcast/688a34636f5a275f1cba40fd【目录】本期的 15 篇论文如下：[00:29] 🖥 On Data Engineering for Scaling LLM Terminal Capabilities（论扩展大型语言模型终端能力的数据工程）[01:20] 🧠 Query-focused and Memory-aware Reranker for Long Context Processing（面向长文本处理的查询聚焦与记忆感知重排序器）[02:12] 🔗 From Perception to Action: An Interactive Benchmark for Vision Reasoning（从感知到行动：视觉推理的交互式基准）[03:04] 🤖 PyVision-RL: Forging Open Agentic Vision Models via RL（PyVision-RL：通过强化学习锻造开放的智能体视觉模型）[03:52] 📊 LongCLI-Bench: A Preliminary Benchmark and Study for Long-horizon Agentic Programming in Command-Line Interfaces（LongCLI-Bench：命令行界面中长视野智能体编程的初步基准与研究）[04:41] 🔍 DREAM: Deep Research Evaluation with Agentic Metrics（DREAM：基于智能体指标的深度研究评估）[05:39] 📈 Conv-FinRe: A Conversational and Longitudinal Benchmark for Utility-Grounded Financial Recommendation（Conv-FinRe：面向效用驱动的金融推荐对话式与长期性基准）[06:49] ⚙ QuantVLA: Scale-Calibrated Post-Training Quantization for Vision-Language-Action Models（QuantVLA：面向视觉-语言-动作模型的尺度校准后训练量化）[07:35] 🤖 Learning from Trials and Errors: Reflective Test-Time Planning for Embodied LLMs（从试错中学习：具身大语言模型的反思性测试时规划）[08:20] 🚀 The Diffusion Duality, Chapter II: $Ψ$-Samplers and Efficient Curriculum（扩散对偶性第二章：Ψ采样器与高效课程学习）[09:05] 🧩 Communication-Inspired Tokenization for Structured Image Representations（面向结构化图像表征的通信启发式分词方法）[10:02] 🤖 Aletheia tackles FirstProof autonomously（Aletheia自主攻克首届FirstProof挑战）[10:42] ⚡ Untied Ulysses: Memory-Efficient Context Parallelism via Headwise Chunking（解绑的尤利西斯：通过注意力头分块实现内存高效上下文并行）[11:34] ⚡ The Art of Efficient Reasoning: Data, Reward, and Optimization（高效推理的艺术：数据、奖励与优化）[12:13] 🔒 Adaptive Text Anonymization: Learning Privacy-Utility Trade-offs via Prompt Optimization（自适应文本匿名化：通过提示优化学习隐私与效用的权衡）【关注我们】您还可以在以下平台找到我们，获得播客内容以外更多信息小红书: AI速递在小宇宙查看该单集文稿

【赞助商】通勤路上就听AI每周谈。AI每周谈，每周带你回顾上周AI大事传送门 🔗https://www.xiaoyuzhoufm.com/podcast/688a34636f5a275f1cba40fd【目录】本期的 14 篇论文如下：[00:31] 🧠 A Very Big Video Reasoning Suite（一个超大规模视频推理套件）[01:16] 🧪 VLANeXt: Recipes for Building Strong VLA Models（VLANeXt：构建强大视觉-语言-动作模型的实践指南）[02:06] 🧭 ManCAR: Manifold-Constrained Latent Reasoning with Adaptive Test-Time Computation for Sequential Recommendation（ManCAR：用于序列推荐的具有自适应测试时计算的流形约束潜在推理）[02:54] 🤖 TOPReward: Token Probabilities as Hidden Zero-Shot Rewards for Robotics（TOPReward：将标记概率作为机器人学的隐藏零样本奖励）[03:45] 📱 Mobile-O: Unified Multimodal Understanding and Generation on Mobile Device（Mobile-O：移动设备上的统一多模态理解与生成）[04:40] 🧠 DSDR: Dual-Scale Diversity Regularization for Exploration in LLM Reasoning（DSDR：用于大语言模型推理探索的双尺度多样性正则化）[05:54] 🎯 Learning Cross-View Object Correspondence via Cycle-Consistent Mask Prediction（通过循环一致掩码预测学习跨视角物体对应关系）[06:44] 🎻 SkillOrchestra: Learning to Route Agents via Skill Transfer（SkillOrchestra：通过技能迁移学习路由智能体）[07:28] 🤖 RoboCurate: Harnessing Diversity with Action-Verified Neural Trajectory for Robot Learning（RoboCurate：利用动作验证神经轨迹的多样性进行机器人学习）[08:02] 🚀 K-Search: LLM Kernel Generation via Co-Evolving Intrinsic World Model（K-Search：通过协同演化内在世界模型进行LLM内核生成）[08:43] 🤖 SimVLA: A Simple VLA Baseline for Robotic Manipulation（SimVLA：用于机器人操作的简单视觉-语言-动作基线）[09:29] 🧠 tttLRM: Test-Time Training for Long Context and Autoregressive 3D Reconstruction（tttLRM：基于测试时训练的长上下文自回归三维重建）[10:23] 🗜 Nacrith: Neural Lossless Compression via Ensemble Context Modeling and High-Precision CDF Coding（Nacrith：基于集成上下文建模与高精度CDF编码的神经无损压缩）[11:08] 🧬 AAVGen: Precision Engineering of Adeno-associated Viral Capsids for Renal Selective Targeting（AAVGen：用于肾脏选择性靶向的腺相关病毒衣壳精准工程）【关注我们】您还可以在以下平台找到我们，获得播客内容以外更多信息小红书: AI速递在小宇宙查看该单集文稿

【赞助商】通勤路上就听AI每周谈。AI每周谈，每周带你回顾上周AI大事传送门 🔗https://www.xiaoyuzhoufm.com/podcast/688a34636f5a275f1cba40fd【目录】本期的 10 篇论文如下：[00:40] ⚖ VESPO: Variational Sequence-Level Soft Policy Optimization for Stable Off-Policy LLM Training（VESPO：用于稳定离策略LLM训练的变分序列级软策略优化）[01:45] 💭 Does Your Reasoning Model Implicitly Know When to Stop Thinking?（你的推理模型是否隐含地知道何时停止思考？）[02:44] 🎮 Generated Reality: Human-centric World Simulation using Interactive Video Generation with Hand and Camera Control（生成现实：基于交互式视频生成与手部和相机控制的人本世界模拟）[03:24] 🤖 EgoPush: Learning End-to-End Egocentric Multi-Object Rearrangement for Mobile Robots（EgoPush：面向移动机器人的端到端自我中心多物体重排学习）[04:11] 🤖 SARAH: Spatially Aware Real-time Agentic Humans（SARAH：具备空间感知能力的实时拟人化智能体）[05:05] 🎬 VidEoMT: Your ViT is Secretly Also a Video Segmentation Model（VidEoMT：你的ViT模型暗中也是一个视频分割模型）[05:51] ✂ Sink-Aware Pruning for Diffusion Language Models（面向扩散语言模型的汇点感知剪枝）[06:36] 🎯 Selective Training for Large Vision Language Models via Visual Information Gain（基于视觉信息增益的大型视觉语言模型选择性训练）[07:18] 🧮 DeepVision-103K: A Visually Diverse, Broad-Coverage, and Verifiable Mathematical Dataset for Multimodal Reasoning（DeepVision-103K：一个视觉多样、覆盖广泛且可验证的多模态推理数学数据集）[08:16] 🤖 Learning Smooth Time-Varying Linear Policies with an Action Jacobian Penalty（通过动作雅可比惩罚学习平滑时变线性策略）【关注我们】您还可以在以下平台找到我们，获得播客内容以外更多信息小红书: AI速递在小宇宙查看该单集文稿

【赞助商】通勤路上就听AI每周谈。AI每周谈，每周带你回顾上周AI大事传送门 🔗https://www.xiaoyuzhoufm.com/podcast/688a34636f5a275f1cba40fd【目录】本期的 5 篇论文如下：[00:45] TOP1(🔥219) | 🧠 Less is Enough: Synthesizing Diverse Data in Feature Space of LLMs（少即是够：在大型语言模型特征空间中合成多样化数据）[03:23] TOP2(🔥140) | 🔊 SQuTR: A Robustness Benchmark for Spoken Query to Text Retrieval under Acoustic Noise（SQuTR：声学噪声下口语查询文本检索的鲁棒性基准）[05:03] TOP3(🔥71) | 🤖 GLM-5: from Vibe Coding to Agentic Engineering（GLM-5：从氛围编码到智能体工程）[06:53] TOP4(🔥61) | 🧠 Experiential Reinforcement Learning（经验性强化学习）[08:50] TOP5(🔥58) | 🏥 MedXIAOHE: A Comprehensive Recipe for Building Medical MLLMs（MedXIAOHE：构建医疗多模态大语言模型的完整方案）【关注我们】您还可以在以下平台找到我们，获得播客内容以外更多信息小红书: AI速递在小宇宙查看该单集文稿

【赞助商】通勤路上就听AI每周谈。AI每周谈，每周带你回顾上周AI大事传送门 🔗https://www.xiaoyuzhoufm.com/podcast/688a34636f5a275f1cba40fd【目录】本期的 15 篇论文如下：[00:31] ⚡ SpargeAttention2: Trainable Sparse Attention via Hybrid Top-k+Top-p Masking and Distillation Fine-Tuning（SpargeAttention2：通过混合Top-k+Top-p掩码与蒸馏微调实现可训练的稀疏注意力）[01:27] 🧠 Unified Latents (UL): How to train your latents（统一隐变量（UL）：如何训练你的隐变量）[02:05] 🤖 Mobile-Agent-v3.5: Multi-platform Fundamental GUI Agents（Mobile-Agent-v3.5：多平台基础图形用户界面智能体）[02:58] 🚗 "What Are You Doing?": Effects of Intermediate Feedback from Agentic LLM In-Car Assistants During Multi-Step Processing（“你在做什么？”：多步骤处理过程中来自具身化LLM车载助手的中间反馈效果研究）[03:45] ⚠ Frontier AI Risk Management Framework in Practice: A Risk Analysis Technical Report v1.5（前沿人工智能风险管理框架实践：风险分析技术报告 v1.5）[04:40] ⚡ DDiT: Dynamic Patch Scheduling for Efficient Diffusion Transformers（DDiT：面向高效扩散变换器的动态补丁调度）[05:39] 🧠 Arcee Trinity Large Technical Report（Arcee Trinity 大型技术报告）[06:23] 🖥 Computer-Using World Model（计算机使用世界模型）[07:20] 🔬 ArXiv-to-Model: A Practical Study of Scientific LM Training（ArXiv到模型：科学语言模型训练的实践研究）[07:59] 🧬 Discovering Multiagent Learning Algorithms with Large Language Models（利用大语言模型发现多智能体学习算法）[08:42] 🖐 TactAlign: Human-to-Robot Policy Transfer via Tactile Alignment（TactAlign：通过触觉对齐实现人机策略迁移）[09:24] 🤖 FRAPPE: Infusing World Modeling into Generalist Policies via Multiple Future Representation Alignment（FRAPPE：通过多未来表示对齐将世界建模注入通用策略）[10:08] 🧠 World Models for Policy Refinement in StarCraft II（用于《星际争霸II》策略优化的世界模型）[10:46] ⚡ 2Mamba2Furious: Linear in Complexity, Competitive in Accuracy（2Mamba2Furious：线性复杂度，媲美准确度）[11:19] 🤿 StereoAdapter-2: Globally Structure-Consistent Underwater Stereo Depth Estimation（StereoAdapter-2：全局结构一致的水下立体深度估计）【关注我们】您还可以在以下平台找到我们，获得播客内容以外更多信息小红书: AI速递在小宇宙查看该单集文稿

【赞助商】通勤路上就听AI每周谈。AI每周谈，每周带你回顾上周AI大事传送门 🔗https://www.xiaoyuzhoufm.com/podcast/688a34636f5a275f1cba40fd【目录】本期的 14 篇论文如下：[00:30] ⚡ SLA2: Sparse-Linear Attention with Learnable Routing and QAT（SLA2：具有可学习路由和量化感知训练的稀疏线性注意力）[01:16] 🤖 Learning Humanoid End-Effector Control for Open-Vocabulary Visual Loco-Manipulation（面向开放词汇视觉移动操作的人形机器人末端执行器控制学习）[02:02] 🧠 RynnBrain: Open Embodied Foundation Models（RynnBrain：开放式具身基础模型）[02:46] 🔑 Empty Shelves or Lost Keys? Recall Is the Bottleneck for Parametric Factuality（空书架还是丢钥匙？回忆是参数化事实性的瓶颈）[03:33] 🕺 SAM 3D Body: Robust Full-Body Human Mesh Recovery（SAM 3D 人体：鲁棒的全身体三维人体网格重建）[04:41] 🤝 Multi-agent cooperation through in-context co-player inference（通过上下文共玩家推断实现多智能体合作）[05:28] 📊 MAEB: Massive Audio Embedding Benchmark（MAEB：大规模音频嵌入基准测试）[06:04] 🤖 World Action Models are Zero-shot Policies（世界行动模型是零样本策略）[06:44] 🔬 Towards a Science of AI Agent Reliability（迈向AI智能体可靠性的科学）[07:20] 🧠 MMA: Multimodal Memory Agent（MMA：多模态记忆智能体）[08:09] 🚀 Optimizing Few-Step Generation with Adaptive Matching Distillation（通过自适应匹配蒸馏优化少步生成）[08:56] 🧭 Learning Situated Awareness in the Real World（在现实世界中学习情境感知）[09:28] ⚠ Visual Memory Injection Attacks for Multi-Turn Conversations（面向多轮对话的视觉记忆注入攻击）[10:10] 🤖 BiManiBench: A Hierarchical Benchmark for Evaluating Bimanual Coordination of Multimodal Large Language Models（BiManiBench：用于评估多模态大语言模型双手协调能力的层次化基准）【关注我们】您还可以在以下平台找到我们，获得播客内容以外更多信息小红书: AI速递在小宇宙查看该单集文稿