HuggingFace 每日AI论文速递 - 节目列表

本期的 8 篇论文如下：[00:33] 🧠 CLEAR: Character Unlearning in Textual and Visual Modalities（CLEAR：文本与视觉模态中的字符遗忘）[01:10] 🤖 AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions（AutoKaggle：一种用于自主数据科学竞赛的多智能体框架）[01:46] 🤖 SocialGPT: Prompting LLMs for Social Relation Reasoning via Greedy Segment Optimization（社交GPT：通过贪婪段优化提示LLMs进行社交关系推理）[02:26] 🌐 OpenWebVoyager: Building Multimodal Web Agents via Iterative Real-World Exploration, Feedback and Optimization（开放式网络航海者：通过迭代现实世界探索、反馈和优化构建多模态网络代理）[03:13] 🧠 Flow-DPO: Improving LLM Mathematical Reasoning through Online Multi-Agent Learning（Flow-DPO：通过在线多智能体学习提升LLM数学推理能力）[03:52] 🚀 ShadowKV: KV Cache in Shadows for High-Throughput Long-Context LLM Inference（ShadowKV：高吞吐量长上下文LLM推理的KV缓存优化）[04:31] 🤖 Robots Pre-train Robots: Manipulation-Centric Robotic Representation from Large-Scale Robot Dataset（机器人预训练机器人：基于大规模机器人数据集的以操作为中心的机器人表示）[05:17] 🤖 Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning（基于人机协作强化学习的精确灵巧机器人操作）【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

本期的 17 篇论文如下：[00:24] 🇵 Bielik 7B v0.1: A Polish Language Model -- Development, Insights, and Evaluation（Bielik 7B v0.1：波兰语言模型——开发、洞察与评估）[01:00] 🤖 AgentStore: Scalable Integration of Heterogeneous Agents As Specialized Generalist Computer Assistant（AgentStore：可扩展的异构代理作为专业化通才计算机助手集成）[01:39] 🤖 GPT-4o System Card（GPT-4o系统卡片）[02:21] 📄 Document Parsing Unveiled: Techniques, Challenges, and Prospects for Structured Information Extraction（文档解析揭秘：结构化信息提取的技术、挑战与前景）[03:08] 🤖 LongReward: Improving Long-context Large Language Models with AI Feedback（长奖励：通过AI反馈提升长上下文大语言模型）[03:43] 🎥 MarDini: Masked Autoregressive Diffusion for Video Generation at Scale（MarDini：大规模视频生成的掩码自回归扩散模型）[04:22] 🌟 DreamClear: High-Capacity Real-World Image Restoration with Privacy-Safe Dataset Curation（DreamClear：高容量真实世界图像修复与隐私安全数据集构建）[05:10] 🧩 GrounDiT: Grounding Diffusion Transformers via Noisy Patch Transplantation（GrounDiT：基于噪声补丁移植的扩散变换器空间定位）[05:49] 📚 A Survey of Small Language Models（小语言模型综述）[06:23] 💾 COAT: Compressing Optimizer states and Activation for Memory-Efficient FP8 Training（COAT：压缩优化器状态和激活以实现高效的FP8训练）[06:58] ⚡ Fast Best-of-N Decoding via Speculative Rejection（基于推测拒绝的快速最佳N解码）[07:36] 🔍 Vision Search Assistant: Empower Vision-Language Models as Multimodal Search Engines（视觉搜索助手：赋能视觉-语言模型作为多模态搜索引擎）[08:25] 🎥 LARP: Tokenizing Videos with a Learned Autoregressive Generative Prior（LARP：利用学习到的自回归生成先验进行视频标记化）[09:00] 🤖 Neural Fields in Robotics: A Survey（机器人学中的神经场：综述）[09:40] 🗣 Dialog2Flow: Pre-training Soft-Contrastive Action-Driven Sentence Embeddings for Automatic Dialog Flow Extraction（对话2流程：预训练软对比动作驱动句子嵌入用于自动对话流程提取）[10:15] 🩺 Language Models And A Second Opinion Use Case: The Pocket Professional（语言模型与第二意见应用案例：口袋专家）[10:55] 🤖 Leveraging Locality to Boost Sample Efficiency in Robotic Manipulation（利用局部性提升机器人操作的样本效率）【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

本期的 13 篇论文如下：[00:25] 🚀 ROCKET-1: Master Open-World Interaction with Visual-Temporal Context Prompting（ROCKET-1：利用视觉-时间上下文提示掌握开放世界交互）[01:14] 🗣 Continuous Speech Synthesis using per-token Latent Diffusion（基于每标记潜在扩散的连续语音合成）[01:55] ⚡ Teach Multimodal LLMs to Comprehend Electrocardiographic Images（教授多模态大语言模型理解心电图图像）[02:39] 🌐 Infinity-MM: Scaling Multimodal Performance with Large-Scale and High-Quality Instruction Data（无限多模态：通过大规模高质量指令数据扩展多模态性能）[03:23] ⚡ FasterCache: Training-Free Video Diffusion Model Acceleration with High Quality（FasterCache：无训练视频扩散模型加速与高质量生成）[03:56] 🎧 MMAU: A Massive Multi-Task Audio Understanding and Reasoning Benchmark（大规模多任务音频理解与推理基准）[04:34] 🧠 Counting Ability of Large Language Models and Impact of Tokenization（大型语言模型的计数能力及其对分词的影响）[05:08] 🧠 Fictitious Synthetic Data Can Improve LLM Factuality via Prerequisite Learning（通过先决学习利用虚构合成数据提升LLM事实性）[05:46] 🤖 Reflection-Bench: probing AI intelligence with reflection（反射-基准：通过反射探测AI智能）[06:23] 🤖 Hybrid Preferences: Learning to Route Instances for Human vs. AI Feedback（混合偏好：学习路由实例以进行人机反馈）[06:57] 🔍 Leveraging Skills from Unlabeled Prior Data for Efficient Online Exploration（利用未标注的先验数据进行高效在线探索）[07:35] 🔍 Are LLMs Better than Reported? Detecting Label Errors and Mitigating Their Effect on Model Performance（LLM是否优于报告？检测标签错误并减轻其对模型性能的影响）[08:15] 🤖 Dynamic 3D Gaussian Tracking for Graph-Based Neural Dynamics Modeling（基于图神经网络的动态三维高斯跟踪用于神经动力学建模）【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

本期的 5 篇论文如下：[00:44] TOP1(🔥79) | ⚡ FrugalNeRF: Fast Convergence for Few-shot Novel View Synthesis without Learned Priors（节俭NeRF：无学习先验的少样本新视角合成快速收敛）[02:42] TOP2(🔥60) | 🌳 SAM2Long: Enhancing SAM 2 for Long Video Segmentation with a Training-Free Memory Tree（SAM2Long：通过无训练记忆树增强SAM 2以实现长视频分割）[04:19] TOP3(🔥58) | 🚀 Breaking the Memory Barrier: Near Infinite Batch Size Scaling for Contrastive Loss（打破内存壁垒：对比损失的近无限批量规模扩展）[06:11] TOP4(🔥55) | 🤖 CompassJudger-1: All-in-one Judge Model Helps Model Evaluation and Evolution（指南针评判者-1：一体化评判模型助力模型评估与进化）[08:28] TOP5(🔥52) | 💼 UCFE: A User-Centric Financial Expertise Benchmark for Large Language Models（UCFE：面向用户的大语言模型金融专业能力基准）【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

本期的 21 篇论文如下：[00:26] 🚀 Breaking the Memory Barrier: Near Infinite Batch Size Scaling for Contrastive Loss（打破内存壁垒：对比损失的近无限批量规模扩展）[01:09] 🔄 LOGO -- Long cOntext aliGnment via efficient preference Optimization（LOGO -- 通过高效偏好优化实现长上下文对齐）[01:45] 🧠 Unleashing Reasoning Capability of LLMs via Scalable Question Synthesis from Scratch（从零开始释放LLMs的推理能力：可扩展的问题合成方法）[02:30] 🤔 Can Knowledge Editing Really Correct Hallucinations?（知识编辑真的能纠正幻觉吗？）[03:17] 🎮 Unbounded: A Generative Infinite Game of Character Life Simulation（无界：生成式无限角色生活模拟游戏）[04:02] 🎥 Framer: Interactive Frame Interpolation（Framer：交互式帧插值）[04:48] 📊 Distill Visual Chart Reasoning Ability from LLMs to MLLMs（从LLMs到MLLMs的视觉图表推理能力提炼）[05:35] 📉 Why Does the Effective Context Length of LLMs Fall Short?（为什么大型语言模型的有效上下文长度不足？）[06:14] 🔒 Robust Watermarking Using Generative Priors Against Image Editing: From Benchmarking to Advances（基于生成先验的鲁棒水印技术对抗图像编辑：从基准测试到进展）[06:52] 🔧 Skywork-Reward: Bag of Tricks for Reward Modeling in LLMs（天工奖励：LLM奖励建模的技巧包）[07:27] 🌍 CAMEL-Bench: A Comprehensive Arabic LMM Benchmark（CAMEL-Bench：一个全面的阿拉伯语大型多模态模型基准）[08:09] 📊 Should We Really Edit Language Models? On the Evaluation of Edited Language Models（我们真的应该编辑语言模型吗？关于编辑语言模型的评估）[08:43] 🌐 ADEM-VL: Adaptive and Embedded Fusion for Efficient Vision-Language Tuning（ADEM-VL：高效视觉语言调优的自适应嵌入融合方法）[09:20] 🌐 WAFFLE: Multi-Modal Model for Automated Front-End Development（WAFFLE：自动化前端开发的多模态模型）[09:52] 📚 CCI3.0-HQ: a large-scale Chinese dataset of high quality designed for pre-training large language models（CCI3.0-HQ：一个用于预训练大型语言模型的高质量大规模中文数据集）[10:30] 🔄 Stable Consistency Tuning: Understanding and Improving Consistency Models（稳定一致性调优：理解与改进一致性模型）[11:10] 🧮 Language Models are Symbolic Learners in Arithmetic（语言模型在算术中的符号学习者角色）[12:00] 🐍 Taipan: Efficient and Expressive State Space Language Models with Selective Attention（Taipan：高效且表达丰富的状态空间语言模型与选择性注意力）[12:44] 🔄 Value Residual Learning For Alleviating Attention Concentration In Transformers（残差值学习缓解Transformer中的注意力集中问题）[13:23] 📚 Multi-Draft Speculative Sampling: Canonical Architectures and Theoretical Limits（多草稿推测采样：典型架构与理论极限）[14:03] 🤖 Data Scaling Laws in Imitation Learning for Robotic Manipulation（机器人操作中的模仿学习数据缩放定律）【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

本期的 10 篇论文如下：[00:25] 🖼 MIA-DPO: Multi-Image Augmented Direct Preference Optimization For Large Vision-Language Models（多图像增强直接偏好优化：大型视觉语言模型）[01:09] 🌍 WorldSimBench: Towards Video Generation Models as World Simulators（世界模拟器：迈向视频生成模型作为世界模拟器）[01:47] 🌊 Scaling Diffusion Language Models via Adaptation from Autoregressive Models（通过自回归模型适应扩展扩散语言模型）[02:20] 📱 Lightweight Neural App Control（轻量级神经应用控制）[03:01] 🏠 ARKit LabelMaker: A New Scale for Indoor 3D Scene Understanding（ARKit标签制造者：室内3D场景理解的新尺度）[03:47] 🖼 Scalable Ranked Preference Optimization for Text-to-Image Generation（可扩展的文本到图像生成中的排序偏好优化）[04:23] 🌆 DynamicCity: Large-Scale LiDAR Generation from Dynamic Scenes（动态城市：动态场景的大规模LiDAR生成）[05:05] 🩺 MedINST: Meta Dataset of Biomedical Instructions（医学指令元数据集：MedINST）[05:52] 🌍 M-RewardBench: Evaluating Reward Models in Multilingual Settings（多语言环境下的奖励模型评估：M-RewardBench）[06:27] 📊 TP-Eval: Tap Multimodal LLMs' Potential in Evaluation by Customizing Prompts（TP-Eval：通过定制提示挖掘多模态大语言模型的评估潜力）【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

本期的 8 篇论文如下：[00:27] 🔍 PyramidDrop: Accelerating Your Large Vision-Language Models via Pyramid Visual Redundancy Reduction（金字塔式视觉冗余减少：通过金字塔视觉冗余减少加速大型视觉-语言模型）[01:09] 🌟 SpectroMotion: Dynamic 3D Reconstruction of Specular Scenes（光谱运动：镜面场景的动态三维重建）[01:48] 🤖 Aligning Large Language Models via Self-Steering Optimization（通过自引导优化对齐大型语言模型）[02:30] 🇯 JMMMU: A Japanese Massive Multi-discipline Multimodal Understanding Benchmark for Culture-aware Evaluation（JMMMU：一个用于文化意识评估的日本大规模多学科多模态理解基准）[03:11] 🧬 EvoPress: Towards Optimal Dynamic Model Compression via Evolutionary Search（EvoPress：通过进化搜索实现最优动态模型压缩）[03:53] 🧠 MiniPLM: Knowledge Distillation for Pre-Training Language Models（MiniPLM：预训练语言模型的知识蒸馏）[04:30] 🔍 Mitigating Object Hallucination via Concentric Causal Attention（通过同心因果注意力缓解对象幻觉）[05:19] 🧠 Math Neurosurgery: Isolating Language Models' Math Reasoning Abilities Using Only Forward Passes（数学神经外科：仅使用前向传递隔离语言模型的数学推理能力）【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

本期的 21 篇论文如下：[00:24] 🤖 CompassJudger-1: All-in-one Judge Model Helps Model Evaluation and Evolution（指南针评判者-1：一体化评判模型助力模型评估与进化）[01:11] 🌲 SAM2Long: Enhancing SAM 2 for Long Video Segmentation with a Training-Free Memory Tree（SAM2长：通过无需训练的记忆树增强SAM 2以实现长视频分割）[01:55] 🌐 PUMA: Empowering Unified MLLM with Multi-granular Visual Generation（PUMA：赋予统一多模态大语言模型多粒度视觉生成能力）[02:37] 🤖 AutoTrain: No-code training for state-of-the-art models（AutoTrain：无代码训练最先进的模型）[03:10] ⚡ FrugalNeRF: Fast Convergence for Few-shot Novel View Synthesis without Learned Priors（节俭NeRF：无学习先验的少样本新视角合成快速收敛）[03:56] 📊 Baichuan Alignment Technical Report（百川对齐技术报告）[04:39] 🌍 Pangea: A Fully Open Multilingual Multimodal LLM for 39 Languages（泛亚：一个完全开放的多语种多模态LLM，涵盖39种语言）[05:21] 🔍 RM-Bench: Benchmarking Reward Models of Language Models with Subtlety and Style（RM-Bench：评估语言模型奖励模型的细致性与风格敏感度）[06:05] 📚 Meta-Chunking: Learning Efficient Text Segmentation via Logical Perception（元分块：通过逻辑感知学习高效的文本分割）[06:41] 🔍 Pre-training Distillation for Large Language Models: A Design Space Exploration（大型语言模型预训练蒸馏：设计空间探索）[07:16] 🔬 Alchemy: Amplifying Theorem-Proving Capability through Symbolic Mutation（炼金术：通过符号变异增强定理证明能力）[07:55] 🔄 SemiEvol: Semi-supervised Fine-tuning for LLM Adaptation（半监督微调：LLM适应的半监督微调框架）[08:31] 📚 Selecting Influential Samples for Long Context Alignment via Homologous Models' Guidance and Contextual Awareness Measurement（通过同源模型引导和上下文意识测量选择长上下文对齐的关键样本）[09:11] 🤖 Zero-shot Model-based Reinforcement Learning using Large Language Models（基于大语言模型的零样本模型强化学习）[09:53] 🗣 Ichigo: Mixed-Modal Early-Fusion Realtime Voice Assistant（一护：混合模态早期融合实时语音助手）[10:28] 🧠 CBT-Bench: Evaluating Large Language Models on Assisting Cognitive Behavior Therapy（CBT-Bench：评估大型语言模型在辅助认知行为疗法中的应用）[11:12] 🛠 Router-Tuning: A Simple and Effective Approach for Enabling Dynamic-Depth in Transformers（路由器调优：一种简单有效的Transformer动态深度调整方法）[11:58] 🧠 Hallucination Detox: Sensitive Neuron Dropout (SeND) for Large Language Model Training（幻觉解毒：用于大型语言模型训练的敏感神经元丢弃方法）[12:45] 🌍 Cross-Lingual Auto Evaluation for Assessing Multilingual LLMs（多语言大语言模型的跨语言自动评估）[13:25] 🗣 DM-Codec: Distilling Multimodal Representations for Speech Tokenization（多模态表示蒸馏用于语音标记化）[14:17] 🧠 In-context learning and Occam's razor（上下文学习与奥卡姆剃刀）【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

本期的 12 篇论文如下：[00:27] 🌐 Web Agents with World Models: Learning and Leveraging Environment Dynamics in Web Navigation（拥有世界模型的网络代理：学习和利用环境动态进行网页导航）[01:11] 👗 MagicTailor: Component-Controllable Personalization in Text-to-Image Diffusion Models（魔法裁缝：文本到图像扩散模型中的组件可控个性化）[01:48] 💼 UCFE: A User-Centric Financial Expertise Benchmark for Large Language Models（UCFE：面向用户的大语言模型金融专业能力基准）[02:37] 🧠 NaturalBench: Evaluating Vision-Language Models on Natural Adversarial Samples（自然对抗样本：评估视觉语言模型）[03:12] 🧠 SeerAttention: Learning Intrinsic Sparse Attention in Your LLMs（SeerAttention：在LLMs中学习内在稀疏注意力）[03:54] 📊 Are AI Detectors Good Enough? A Survey on Quality of Datasets With Machine-Generated Texts（AI检测器足够好吗？机器生成文本数据集质量调查）[04:25] 🌐 Diffusion Curriculum: Synthetic-to-Real Generative Curriculum Learning via Image-Guided Diffusion（扩散课程：通过图像引导扩散实现合成到真实的生成课程学习）[05:08] 🎥 DAWN: Dynamic Frame Avatar with Non-autoregressive Diffusion Framework for Talking Head Video Generation（DAWN: 非自回归扩散框架动态帧头像的讲话头视频生成）[05:50] 🔄 A Common Pitfall of Margin-based Language Model Alignment: Gradient Entanglement（基于边际的语言模型对齐常见陷阱：梯度纠缠）[06:31] 🧬 DPLM-2: A Multimodal Diffusion Protein Language Model（DPLM-2: 一种多模态扩散蛋白质语言模型）[07:12] 📰 Context is Key(NMF): Modelling Topical Information Dynamics in Chinese Diaspora Media（关键在于上下文（NMF）：建模华人媒体中的主题信息动态）[07:56] 🧠 How Do Training Methods Influence the Utilization of Vision Models?（训练方法如何影响视觉模型的利用？）【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

本期的 5 篇论文如下：[00:45] TOP1(🔥80) | 🌐 Baichuan-Omni Technical Report（百川-Omni 技术报告）[02:20] TOP2(🔥58) | 📊 MixEval-X: Any-to-Any Evaluations from Real-World Data Mixtures（MixEval-X：从现实世界数据混合中进行任意到任意评估）[04:20] TOP3(🔥58) | 🎥 Movie Gen: A Cast of Media Foundation Models（电影生成：媒体基础模型集合）[06:27] TOP4(🔥53) | 🤖 LOKI: A Comprehensive Synthetic Data Detection Benchmark using Large Multimodal Models（LOKI：基于大型多模态模型的综合合成数据检测基准）[08:23] TOP5(🔥48) | 🌐 MMIE: Massive Multimodal Interleaved Comprehension Benchmark for Large Vision-Language Models（大规模多模态交错理解基准测试）【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

本期的 31 篇论文如下：[00:23] 📊 MixEval-X: Any-to-Any Evaluations from Real-World Data Mixtures（MixEval-X：从现实世界数据混合中进行任意到任意评估）[01:02] 🎥 Movie Gen: A Cast of Media Foundation Models（电影生成：媒体基础模型集合）[01:35] 📱 MobA: A Two-Level Agent System for Efficient Mobile Task Automation（MobA：一种高效移动任务自动化的两级代理系统）[02:18] 🌐 Harnessing Webpage UIs for Text-Rich Visual Understanding（利用网页UI进行丰富的视觉理解）[02:59] 🔄 Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation（雅努斯：解耦视觉编码以实现统一的多模态理解和生成）[03:29] 🩺 MMed-RAG: Versatile Multimodal RAG System for Medical Vision Language Models（多功能多模态RAG系统在医学视觉语言模型中的应用）[04:04] 📊 A Unified View of Delta Parameter Editing in Post-Trained Large-Scale Models（大规模模型后训练中Delta参数编辑的统一视角）[04:46] 🔄 PopAlign: Diversifying Contrasting Patterns for a More Comprehensive Alignment（PopAlign：多样化对比模式以实现更全面的模型对齐）[05:23] 🔍 BenTo: Benchmark Task Reduction with In-Context Transferability（BenTo: 基于上下文迁移性的基准任务缩减）[06:03] 🎥 DreamVideo-2: Zero-Shot Subject-Driven Video Customization with Precise Motion Control（DreamVideo-2：零样本主题驱动视频定制与精确运动控制）[06:49] 🧠 MoH: Multi-Head Attention as Mixture-of-Head Attention（MoH：多头部注意力机制作为混合头部注意力机制）[07:28] 🎥 VidPanos: Generative Panoramic Videos from Casual Panning Videos（VidPanos：从随意拍摄的平移视频生成全景视频）[08:03] 📉 FlatQuant: Flatness Matters for LLM Quantization（FlatQuant：扁平化对LLM量化的重要性）[08:44] 🔄 Retrospective Learning from Interactions（从交互中回顾学习）[09:22] 🔄 Failing Forward: Improving Generative Error Correction for ASR with Synthetic Data and Retrieval Augmentation（向前失败：利用合成数据和检索增强改进ASR的生成错误校正）[10:06] 🖼 Can MLLMs Understand the Deep Implication Behind Chinese Images?（多模态大语言模型能否理解中文图像的深层含义？）[10:43] 📱 MedMobile: A mobile-sized language model with expert-level clinical capabilities（MedMobile：具备专家级临床能力的移动端语言模型）[11:22] 🌍 WorldCuisines: A Massive-Scale Benchmark for Multilingual and Multicultural Visual Question Answering on Global Cuisines（世界美食：多语言多文化视觉问答的大规模基准）[12:04] 🤖 Remember, Retrieve and Generate: Understanding Infinite Visual Concepts as Your Personalized Assistant（记住、检索与生成：理解无限视觉概念作为个性化助手）[12:48] 🔄 LoLDU: Low-Rank Adaptation via Lower-Diag-Upper Decomposition for Parameter-Efficient Fine-Tuning（LoLDU：通过下三角-对角-上三角分解实现低秩适应的参数高效微调）[13:29] 🔒 AERO: Softmax-Only LLMs for Efficient Private Inference（AERO：仅使用Softmax的LLM实现高效隐私推断）[14:12] 🌐 $γ-$MoD: Exploring Mixture-of-Depth Adaptation for Multimodal Large Language Models（γ-MoD：探索多模态大语言模型的深度混合适应）[14:45] 🌐 Long-LRM: Long-sequence Large Reconstruction Model for Wide-coverage Gaussian Splats（长序列大重建模型：广覆盖高斯点云）[15:24] 🎶 MuVi: Video-to-Music Generation with Semantic Alignment and Rhythmic Synchronization（MuVi：视频到音乐生成与语义对齐及节奏同步）[16:05] 🔒 Do LLMs Have Political Correctness? Analyzing Ethical Biases and Jailbreak Vulnerabilities in AI Systems（大型语言模型是否具备政治正确性？分析AI系统中的伦理偏见与越狱漏洞）[16:48] 📚 SBI-RAG: Enhancing Math Word Problem Solving for Students through Schema-Based Instruction and Retrieval-Augmented Generation（基于模式教学和检索增强生成的数学应用题解决方法）[17:27] 🗺 Roadmap towards Superhuman Speech Understanding using Large Language Models（基于大型语言模型的超人类语音理解路线图）[18:05] 🔄 Toward Guidance-Free AR Visual Generation via Condition Contrastive Alignment（面向无指导的AR视觉生成的条件对比对齐）[18:47] 🤖 TransAgent: Transfer Vision-Language Foundation Models with Heterogeneous Agent Collaboration（TransAgent：异构代理协作迁移视觉语言基础模型）[19:25] 🔬 Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models（开放材料2024（OMat24）无机材料数据集与模型）[20:05] 📚 Minimum Tuning to Unlock Long Output from LLMs with High Quality Data as the Key（最小调整解锁LLM长输出：高质量数据的关键）【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

本期的 19 篇论文如下：[00:28] 🧠 HumanEval-V: Evaluating Visual Understanding and Reasoning Abilities of Large Multimodal Models Through Coding Tasks（HumanEval-V：通过编码任务评估大型多模态模型的视觉理解和推理能力）[01:15] 🎥 VidEgoThink: Assessing Egocentric Video Understanding Capabilities for Embodied AI（VidEgoThink：评估具身AI的自中心视频理解能力）[01:50] 🧠 The Curse of Multi-Modalities: Evaluating Hallucinations of Large Multimodal Models across Language, Visual, and Audio（多模态的诅咒：评估大型多模态模型在语言、视觉和音频中的幻觉）[02:31] 🤖 Revealing the Barriers of Language Agents in Planning（揭示语言代理在规划中的障碍）[03:15] 📄 DocLayout-YOLO: Enhancing Document Layout Analysis through Diverse Synthetic Data and Global-to-Local Adaptive Perception（DocLayout-YOLO：通过多样合成数据和全局到局部自适应感知增强文档布局分析）[03:56] ⚙ Large Language Model Evaluation via Matrix Nuclear-Norm（大型语言模型评估通过矩阵核范数）[04:38] 🧬 Exploring Model Kinship for Merging Large Language Models（探索大型语言模型合并中的模型亲缘关系）[05:15] 📊 ProSA: Assessing and Understanding the Prompt Sensitivity of LLMs（ProSA：评估和理解大型语言模型的提示敏感性）[05:50] ⚡ ZipVL: Efficient Large Vision-Language Models with Dynamic Token Sparsification and KV Cache Compression（ZipVL：动态令牌稀疏化和KV缓存压缩的高效大视觉-语言模型）[06:31] 📄 Improving Long-Text Alignment for Text-to-Image Diffusion Models（改进文本到图像扩散模型的长文本对齐）[07:11] 🔄 Simplifying, Stabilizing and Scaling Continuous-Time Consistency Models（简化、稳定和扩展连续时间一致性模型）[07:55] 🛡 Controllable Safety Alignment: Inference-Time Adaptation to Diverse Safety Requirements（可控安全对齐：推理时适应多样安全需求）[08:34] 🔍 Tracking Universal Features Through Fine-Tuning and Model Merging（通过微调和模型合并追踪通用特征）[09:08] 🔄 Insights from the Inverse: Reconstructing LLM Training Goals Through Inverse RL（逆向洞察：通过逆向强化学习重构LLM训练目标）[09:46] 🧠 Neural Metamorphosis（神经变形）[10:25] 🌍 WorldMedQA-V: a multilingual, multimodal medical examination dataset for multimodal language models evaluation（世界医学QA-V：多语言、多模态医学考试数据集用于多模态语言模型评估）[11:09] 🌐 OMCAT: Omni Context Aware Transformer（全上下文感知变压器）[11:44] ⏳ ChroKnowledge: Unveiling Chronological Knowledge of Language Models in Multiple Domains（ChroKnowledge：揭示语言模型在多领域中的时间知识）[12:22] 📚 DyVo: Dynamic Vocabularies for Learned Sparse Retrieval with Entities（DyVo：动态词汇表用于实体学习的稀疏检索）【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