2025.03.25 | 稀疏自编码器解读LLM中的推理特征,交互视频革新

2025.03.25 | 稀疏自编码器解读LLM中的推理特征,交互视频革新

11分钟 ·
播放数123
·
评论数0

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)

【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