Podwise 处理的新节目，随时可搜索、浏览，并在完整应用中继续。
在按下播放前，先读读每集讲了什么。
AI 摘要 Self-improving agents utilize test-time and train-time scaling, driven by verifiers and reinforcement learning to enhance reasoning in domains like math and coding. Achieving generalization requires diverse reasoning chains, often generated by multi-agent systems or iterative…
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AI 摘要 Die persönliche Organisation und das Streben nach Ordnung stehen im Mittelpunkt, wobei die Diskussion die neurobiologischen Grundlagen von ADHS und das Konzept der Neurodiversität beleuchtet. Während feste Systeme für manche Menschen eine notwendige Stütze im Alltag darstellen,…
30:59German摘要 · 转录
AI 摘要 Agentic evaluations measure the progress of AI models in completing long-horizon, economically valuable tasks. The METR benchmark tracks a seven-month doubling trend in task duration capabilities, yet highlights that high-reliability success remains elusive. Complementing this,…
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AI 摘要 Improving large language model performance relies on leveraging search within the model's output space to curate high-quality solutions. AlphaCode demonstrated that massive-scale sampling, combined with filtering and clustering, allows models to solve complex competitive…
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AI 摘要 The fall 2026 Audit Analytics & AI Conference emphasized practical, production-ready AI applications for internal audit departments. Effective strategies include persona-based reporting, where AI agents tailor communication styles to specific stakeholder preferences, and…
19:15English摘要 · 转录
AI 摘要 Train time scaling enhances AI reasoning capabilities by allowing models to learn from their own outputs, effectively substituting compute for parameter count. This approach leverages verification-rich domains like mathematics and coding to create closed-loop feedback systems.…
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