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  "text": "🚨Reasoning LLMs are e̵f̵f̵e̵c̵t̵i̵v̵e̵ ̵y̵e̵t̵  inefficient!\n\nLarge language models (LLMs) now solve multi-step problems by emitting extended chains of thought. During the process, they often re-derive the same intermediate steps across problems, inflating token usage and latency.\n\nMetacognitive Reuse: turn recurring LLM reasoning into concise, reusable “behaviors”. The model learns named skills from its own chains-of-thought and reuses them to think faster & cheaper.\n\nArxiv 🔗  - https://t.co/zA1gB4eYTG",
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