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  "title": "Reasoning Models: Slower Thinking, Better Checks?",
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  "thesis": "Reasoning models improve difficult tasks by spending additional compute on explicit intermediate reasoning steps, but the gains come with higher latency, cost, and no guarantee of correctness.",
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      "claim": "Step-by-step problem solving is an old idea; what changed is scale and language-driven search.",
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          "sourceId": "source-yao-tot-2023",
          "snippet": "Tree of Thoughts frames language-model reasoning as deliberate search over coherent units of text, allowing exploration, evaluation, and backtracking.",
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          "summary": "Classical AI planners and theorem provers also performed step-by-step search, but relied on formal symbolic states rather than natural language.",
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      "claim": "In practice, reasoning models expose a longer trace of intermediate reasoning that can be inspected, even if the trace is not always faithful or complete.",
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          "summary": "Research on chain-of-thought faithfulness finds that visible reasoning traces do not always reflect the true factors determining the model's answer.",
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          "summary": "User-facing evaluations show that for many everyday queries, faster non-reasoning models are preferred because of latency and cost.",
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      "title": "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models",
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