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Amplified Does Not Mean Predictive: Reasoning Behaviors in Thinking Models

arXiv cs.CL ↗ · 2026-08-17 Cached

This paper introduces Behavioral Lift to measure how reasoning behaviors in AI thinking models correlate with correct answers, revealing an amplification-lift gap where training amplifies behaviors not most predictive of success.

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#thinking-models

@sheriyuo: latent reasoning 一直是个很不错的研究方向,从 soft prompting 就能看到苗头 如果真的能带来更高的 bound,那么过错的不是这个 blackbox,而是做不到 latent efficiency 的 res…

X AI KOLs Timeline ↗ · 2026-07-02 Cached

讨论latent reasoning作为研究方向的价值,并质疑OpenAI o1模型宣称的thinking过程是否真实或只是对外宣传,认为模型可能尚未达到上限。

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#thinking-models

Gemini 2.5: Updates to our family of thinking models

Google DeepMind Blog ↗ · 2025-06-17 Cached

Google announces stable general availability of Gemini 2.5 Pro and Flash models, introduces new Gemini 2.5 Flash-Lite in preview with lower latency and cost, and updates pricing for the Flash family with adjusted input/output token rates.

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