Capable yet Parsimonious: Extracting and Characterizing Hidden Chain-of-Thought in Frontier Models

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Capable yet Parsimonious: Extracting and Characterizing Hidden Chain-of-Thought in Frontier Models. It centres on Reasoning Models, and also names GPT and Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Models and AI Tools — the section covering a new or updated model, its capabilities, benchmarks or availability.

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arXivCapable yet Parsimonious: Extracting and Characterizing Hidden Chain-of-Thought in Frontier ModelsYesterdayXiaoyu Luo

AI Regulation32

Transcribe, Translate, and Optimize: Joint Reward Learning for Speech Translation

Transcribe, Translate, and Optimize: Joint Reward Learning for Speech Translation. It centres on Reasoning Models, and also names Fine-tuning and Inference. Reported by arXiv. Bharat Hunt files it under AI Regulation — the section covering law, policy, courts, standards and government positions on AI.

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AI Models32

Spoken Language Models that Think Aloud

Spoken Language Models that Think Aloud. It centres on Reasoning Models, and also names Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Models — the section covering a new or updated model, its capabilities, benchmarks or availability.

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Capable yet Parsimonious: Extracting and Characterizing Hidden Chain-of-Thought in Frontier Models

Companies, models and people in this story

Reported by arXiv

Transcribe, Translate, and Optimize: Joint Reward Learning for Speech Translation

Spoken Language Models that Think Aloud

‘Now We Can Know Everything and Do Anything,’ Jensen Huang Says at Dreamforce

Difficulty-Adaptive Tree-Structured Policy Optimization for Expanding Reasoning Coverage in RLVR

Companies, models and people in this story

Reported by arXiv

Related AI coverage

Transcribe, Translate, and Optimize: Joint Reward Learning for Speech Translation

Spoken Language Models that Think Aloud

‘Now We Can Know Everything and Do Anything,’ Jensen Huang Says at Dreamforce

Difficulty-Adaptive Tree-Structured Policy Optimization for Expanding Reasoning Coverage in RLVR