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0⏱️ Highlights
00:25 🎞️ FrameForge: Harnessing Priors of Video Foundation Models for Unified Image Generation（FrameForge：利用视频基础模型先验实现统一图像生成）
01:04 🔁 R^2Orch: Recursive Self-Improvement for Agent Orchestration via Self-Play within a Skill-Internalization Loop（R^2Orch：在技能内化循环中通过自博弈实现智能体编排的递归自我改进）
01:42 🧠 Does Scaling Toward AGI Depend on What System-1 Lets System-2 Execute?（迈向 AGI 的规模扩展，取决于 System-1 允许 System-2 执行什么吗？）
02:20 🤖 REA: A Routed Embodied Agent Runtime for Asynchronous Brain-Cerebellum Control（REA：面向异步大脑—小脑控制的路由式具身智能体运行时）
02:58 🎙️ Apriel Voice 27B Preview: An Open Model for Speech, Reasoning, and Tool Use（Apriel Voice 27B 预览版：面向语音、推理与工具使用的开放模型）
03:37 🎮 Wizard: Manipulate Any World in Real Time via Agentic Control（Wizard：通过智能体控制实时操纵任意世界）
04:14 💭 Latent Reasoning in Continuous Space for Unified Multimodal Models（统一多模态模型在连续空间中的潜在推理）
04:51 🔭 HiFOR: Hierarchical Foresight with Action-Guided World Grounding for Vision-Language-Action Model（HiFOR：面向视觉—语言—动作模型的分层前瞻与动作引导世界信息锚定）
05:31 🔎 WeFair: Fidelity-Aware Alignment from Human Feedback for Personalized Query Rewriting in Short-Video Search（WeFair：面向短视频搜索个性化查询改写的保真度感知人类反馈对齐）
06:11 ⚡ OSWAM: An Effective World Action Model with One-Step Action Generation（OSWAM：具备单步动作生成能力的高效世界动作模型）
06:49 🧵 ReLift: Dense 3D Tracking as Lifted Trajectory Repair（ReLift：将稠密三维跟踪视为提升后轨迹的修复）
07:27 🤝 CEPO: A General Framework for Improving Prompt Optimization in Multi-Agent Systems（CEPO：改进多智能体系统提示词优化的通用框架）
08:06 🕵️ Venus-DefakerOne2: A Vision-Forensics Foundation Model with Broader Coverage（Venus-DefakerOne2：覆盖范围更广的视觉取证基础模型）
08:43 🦾 CaPtain: A Pipeline for Post-Training Code-as-Policy Robot Agents（CaPtain：代码即策略机器人智能体的后训练流程）
09:22 🧭 Probing Spatial State Representations in Visual Foundation Models（探测视觉基础模型中的空间状态表征）
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小红书: AI速递
