HuggingFace 每日AI论文速递

2025.03.25 | 稀疏自编码器解读LLM中的推理特征,交互视频革新


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本期的 15 篇论文如下:

[00:24] 🧠 I Have Covered All the Bases Here: Interpreting Reasoning Features in Large Language Models via Sparse Autoencoders(我已经覆盖了所有基础:通过稀疏自编码器解读大型语言模型中的推理特征)

[01:03] 🎮 Position: Interactive Generative Video as Next-Generation Game Engine(立场:交互式生成视频作为下一代游戏引擎)

[01:47] 🎬 Video-T1: Test-Time Scaling for Video Generation(Video-T1:面向视频生成的测试时缩放)

[02:35] 🌐 Aether: Geometric-Aware Unified World Modeling(Aether:几何感知统一世界建模)

[03:11] 🧠 SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild(SimpleRL-Zoo:探索和驯服开放基础模型中的零强化学习)

[03:51] 🎬 OmnimatteZero: Training-free Real-time Omnimatte with Pre-trained Video Diffusion Models(OmnimatteZero:基于预训练视频扩散模型的免训练实时全域Matte)

[04:31] 🤖 Judge Anything: MLLM as a Judge Across Any Modality(万物皆可判:多模态大型语言模型作为跨模态的评估者)

[05:16] 💡 LEMMA: Learning from Errors for MatheMatical Advancement in LLMs(LEMMA:通过从错误中学习促进大型语言模型在数学领域的进步)

[05:57] 🖼 Equivariant Image Modeling(等变图像建模)

[06:37] 🚀 Training-free Diffusion Acceleration with Bottleneck Sampling(基于瓶颈采样的免训练扩散加速方法)

[07:11] ✨ CFG-Zero*: Improved Classifier-Free Guidance for Flow Matching Models(CFG-Zero*:改进的用于Flow Matching模型的无分类器引导)

[07:59] 🤔 Video SimpleQA: Towards Factuality Evaluation in Large Video Language Models(视频简单问答:面向大型视频语言模型的事实性评估)

[08:39] 🚄 FFN Fusion: Rethinking Sequential Computation in Large Language Models(FFN融合:重新思考大型语言模型中的序列计算)

[09:20] 🛡 Defeating Prompt Injections by Design(通过设计击败提示注入攻击)

[10:00] 🤝 AgentRxiv: Towards Collaborative Autonomous Research(AgentRxiv:迈向协同自主研究)

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