本期的 15 篇论文如下：
[00:22] 🌍 Geopolitical biases in LLMs: what are the "good" and the "bad" countries according to contemporary language models（LLM中的地缘政治偏见：在当代语言模型中，哪些是“好”国家，哪些是“坏”国家？）
[01:09] 🤖 RuleReasoner: Reinforced Rule-based Reasoning via Domain-aware Dynamic Sampling（RuleReasoner：基于领域感知动态采样的强化规则推理）
[01:48] 🖼 Autoregressive Semantic Visual Reconstruction Helps VLMs Understand Better（自回归语义视觉重建助力视觉-语言模型更好地理解）
[02:30] 🎬 Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion（自激：弥合自回归视频扩散中的训练-测试差距）
[03:08] 🧮 Solving Inequality Proofs with Large Language Models（利用大型语言模型求解不等式证明）
[03:49] 🤖 Look Before You Leap: A GUI-Critic-R1 Model for Pre-Operative Error Diagnosis in GUI Automation（三思而后行：用于GUI自动化中术前错误诊断的GUI-Critic-R1模型）
[04:25] 🖼 Frame Guidance: Training-Free Guidance for Frame-Level Control in Video Diffusion Models（帧引导：视频扩散模型中用于帧级别控制的免训练引导）
[05:05] 🤖 Aligning Text, Images, and 3D Structure Token-by-Token（逐Token对齐文本、图像与3D结构）
[05:51] 🔍 ECoRAG: Evidentiality-guided Compression for Long Context RAG（ECoRAG：证据性引导的长文本RAG压缩）
[06:28] 🎬 DiscoVLA: Discrepancy Reduction in Vision, Language, and Alignment for Parameter-Efficient Video-Text Retrieval（DiscoVLA：面向参数高效视频-文本检索的视觉、语言和对齐差异缩减）
[07:14] 🖼 Interpretable and Reliable Detection of AI-Generated Images via Grounded Reasoning in MLLMs（基于多模态大语言模型中具身推理的可解释、可靠的AI生成图像检测）
[08:06] 🗜 Squeeze3D: Your 3D Generation Model is Secretly an Extreme Neural Compressor（Squeeze3D：你的3D生成模型实际上是一个极致的神经压缩器）
[08:46] 🤖 Thinking vs. Doing: Agents that Reason by Scaling Test-Time Interaction（思考与行动：通过扩展测试时交互进行推理的智能体）
[09:21] 🧩 MoA: Heterogeneous Mixture of Adapters for Parameter-Efficient Fine-Tuning of Large Language Models（MoA：用于大语言模型参数高效微调的异构适配器混合）
[09:58] 📚 Institutional Books 1.0: A 242B token dataset from Harvard Library's collections, refined for accuracy and usability（机构书籍1.0：来自哈佛图书馆馆藏的2420亿token数据集，经过精确化处理，具有更高的准确性和可用性）
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小红书: AI速递
Information
- Show
- FrequencyUpdated daily
- Published11 June 2025 at 23:00 UTC
- Length11 min
- RatingClean