2026.06.02 | 多智能体框架生成可编辑图表；参数高效微调支撑百万个性化模型

【目录】本期的 15 篇论文如下：[00:33] 🎨 Crafter: A Multi-Agent Harness for Editable Scientific Figure Generation from Diverse Inputs（Crafter：一种用于从多样化输入生成可编辑科学图形的多智能体框架）[01:39] 🧩 On the Scaling of PEFT: Towards Million Personal Models of Trillion Parameters（关于参数高效微调的规模化：迈向万亿参数级别的百万个性化模型）[02:35] 🧪 A Matter of TASTE: Improving Coverage and Difficulty of Agent Benchmarks（品味之道：提升智能体基准测试的覆盖度与难度）[03:25] 🌐 K-BrowseComp: A Web Browsing Agent Benchmark Grounded in Korean Contexts（K-BrowseComp：基于韩国语境的网页浏览代理基准测试）[04:21] ⚡ Draft-OPD: On-Policy Distillation for Speculative Draft Models（Draft-OPD：面向推测草稿模型的在策略蒸馏）[05:10] 🎓 VLMs are Good Teachers for Video Reasoning via Adaptive Test-Time Optimization（视觉语言模型作为视频推理的优质教师：通过自适应测试时优化）[06:18] 📡 X-Stream: Exploring MLLMs as Multiplexers for Multi-Stream Understanding（X-Stream：探索多模态大语言模型作为多流理解的多路复用器）[07:13] 🎬 VideoMLA: Low-Rank Latent KV Cache for Minute-Scale Autoregressive Video Diffusion（VideoMLA：用于分钟级自回归视频扩散的低秩潜在KV缓存）[07:59] 🤖 SkillAdaptor: Self-Adapting Skills for LLM Agents from Trajectories（SkillAdaptor：面向LLM智能体的自适应技能从轨迹中学习）[08:54] 🧠 Which Pretraining Paradigm Better Serves Spatial Intelligence? An Empirical Comparison of Vision-Language and Video Generation Models（哪种预训练范式更好地服务于空间智能？视觉语言模型与视频生成模型的实证比较）[09:51] 🧠 NITP: Next Implicit Token Prediction for LLM Pre-training（NITP：面向大语言模型预训练的下一隐式词元预测）[10:50] 👀 Where to Look: Can Foundation Models Reach a Target Viewpoint Through Active Exploration?（该看向哪里：基础模型能否通过主动探索达到目标视角？）[11:46] 🎬 LVSA: Training-Free Sparse Attention for Long Video Diffusion（LVSA：面向长视频扩散的无训练稀疏注意力机制）[12:38] 🛑 ESPO: Early-Stopping Proximal Policy Optimization（早期停止的近端策略优化）[13:37] 🎤 StreamChar: Long-Horizon Streaming Character Audio-Video Generation with Decoupled Orchestration（StreamChar：基于解耦编排的长时流式角色音频-视频生成）【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【赞助商】OpenClaw快报每天五分钟，听听 OpenClaw 快报，带你了解最新动态和业内讨论传送门 https://www.xiaoyuzhoufm.com/podcast/6a1732a2dffa135d0ab5ef43在小宇宙查看该单集文稿