So this is not a benchmark for software engineering agents. It's meant to test core reasoning and in…

新论文：iREPA 扩散模型是其底层表征的渲染器。通过这种新设置，我们能更清楚地洞察这些表征的真正含义。Jas 开始了一场自发的探索，过去三个月我们学到了很多 ps. 这也是我们对一种新型线上"饮水机效应"的小实验，我很喜欢看到这种现象。让我们争论、讨论，然后用真正的努力将其转化为正经科学 【引用 @1jaskiratsingh】：！！️ 表征对生成很重要！但事实证明，我们对表征如何帮助生成的理解一直都是错的 ！！️ 我们之前的想法：（我们错了） ❌ 更大的视觉编码器 → …

这不只是建模问题。也是基准测试问题。

谢赛宁转发介绍 V-RAE，该模型直接采用冻结视觉基础模型（DINOv3、SigLIP2、EUPE、V-JEPA 2.1）的表征作为视频生成潜空间，而非传统 VAE 潜空间。在匹配设置下，V-RAE 在 Kinetics-600 上达 2.13 rFVD，UCF101 上 117.86 gFVD，收敛速度比 VAE 潜空间快 6 倍，并引入 tFVD 评估时序平滑度。

在无中央协调器的开放世界多智能体环境Station中，来自不同模型家族的AI智能体自主选择研究方向、开展实验并构建共享科学文献。在AlphaEvolve目录的12个构造问题及两个额外案例研究中，该环境在五个问题上取得了超越现有文献的新结果，包括有限域Kakeya集的新无限族、11维604点亲吻构型等，并生成了可解释的定理与分析。所有原始智能体对话、证明和验证代码均已公开。

斯坦福大学研究人员领衔发布 Terminal-Bench-Science 0.1，用来自生命、物理、地球、数学和工程科学的 70 个专家精选任务评估 AI 智能体的科研能力。

Recent advances in inference-time scaling have significantly improved the reasoning performance of large language models (LLMs). However, these methods typically rely on repeated generation or external verification. To address this limitat…

Agent skills package specialized knowledge and workflows into reusable resources that extend AI agent capabilities. Recent work automatically discovers such skills from agent experience, which enables agents to progressively adapt through …

Recent prominent post-training methods, such as Reinforcement Learning (RL) and On-Policy Self-Distillation (OPSD), have driven rapid progress in mathematical reasoning for large language models, yet their reliance on ground-truth labels p…

To improve large language models' ability to resolve real-world software issues, prior work has focused on constructing large-scale agent trajectory datasets and performing supervised fine-tuning (SFT) on successful trajectories. However, …

In real-world software development, code review typically involves iterative interactions between developers and reviewers to improve software quality, making the process costly and time-consuming. Although recent work explores large langu…