We introduce AuK, an open-source foundational model that unifies speech generation and editing through a common interfac…

We study how model post-training and test-time inference design affect natural-language proof generation for hard olympi…

World-Action Models inherit world knowledge from video-generative priors, and channel it into executable control signals…

Video foundation models increasingly rely on large-scale pretraining data, yet the end-to-end data pipelines behind them…

Reinforcement learning with verifiable rewards (RLVR) substantially improves single-sample accuracy (pass@1) but causes …

Command-line coding agents (e.g., Claude Code, Gemini CLI) can already read and write files and sustain long sessions, y…

AI-powered search products such as ChatGPT search, Google's AI Overviews, and Perplexity provide LLM-synthesized answers…

梳理 RSI 从 Good 1965 到 2026 的思想史、技术图谱与一手实证：Anthropic 承认其代码库 >80% 合并代码由 Claude 撰写、METR 测得 AI 可完成任务时长约每 4 个月翻倍、AlphaEvolve 优化了支撑自身的计算栈。核心判断——有界自我精炼已工程化，开放式 RSI 尚未发生；而进步与安全共享同一个「验证瓶颈」。

AI 没有消灭交付瓶颈，只是把瓶颈从"写代码"搬到了"验证代码"。本文基于 2 篇 arXiv 论文、DORA 2025 报告与 2026 年三份行业基准（LinearB 8.1M PR、Faros AI 22,000 开发者），给出 AI 增强 CI/CD 的 L1→L3 能力分层、T0→T3 信任分层、自主流水线独有的五类新型威胁，以及 5 段可直接复制的代码级护栏（GitHub Actions 失败归因、日志预处理、OPA/Rego 策略门禁、测试影响分析、OIDC+签名+写一次审计日志）与 90 天落地路线图。关键数据：任务吞吐 +33.7% 但评审耗时 +441.5%、生产事故/PR 比值 +242.7%；AI PR 30 天合并率 32.7% vs 人工 84.4%；论文实验中 Lead Time −35%、CFR −38%、MTTR −43%，AI 干预准确率 87.5%、人工否决率 14.3%、零策略违规。

Autoregressive video world models enable interactive, long-horizon exploration, but flexible control remains challenging…

Large language models (LLMs) have demonstrated remarkable capabilities in reasoning and code generation, raising the pro…

面向工程师的 OpenClaw 架构深度长文：四层设计总览、Gateway 单进程控制平面、Agent Loop 完整生命周期、Markdown 记忆管线、四槽插件体系、安全模型与多代理路由，还原一个生产级 AI Agent 运行时的设计取舍。

Leading benchmarks for formal theorem proving with large language models are small collections drawn from competition ma…

Invasive coronary angiography (CAG) is the gold standard for diagnosing coronary artery disease, but interpretation vari…

Model misalignment, prompt injection, or operator misuse could lead AI agents operating frontier-lab accounts to exfiltr…

Robot foundation models are trained and evaluated predominantly in English, and robot demonstration corpora do not exist…

Recursive Super-Resolution (SR) extends fixed-scale SR to extreme magnification by repeatedly feeding predictions back i…

Diffusion-based models enable monocular geometry estimation, yet their pixel-space precision is limited by a shared, und…

Temporal reasoning benchmarks for Video-LLMs are often mediated by language, leaving room for linguistic shortcuts from …

Ensuring factuality remains a critical challenge for deploying LLMs in high-stakes settings. Existing hallucination dete…

Understanding agent behavior requires methods that scale to thousands of trajectories and surface new patterns in long, …