蘋果研究揭示AI“假性思考”真相:推理模型在複雜任務下反而崩潰
一項由蘋果研究人員主導的新研究，對當前被寄予厚望的大型推理模型（LRM）潑了一盆冷水。研究發現，在解決複雜任務時，像 Claude3.7Thinking 和 Deepseek-R1等專爲“模擬思維過程”設計的推理模型，不僅未能展現出優勢，反而出現“思考不足”、性能崩潰等嚴重問題。這項研究測試了四種經典邏輯謎題:漢諾塔、跳棋、渡河與積木世界。這些謎題允許精確控制任務複雜度，是衡量語言模型推理能力的理想場景。結果顯示，標準 LLM 在簡單任務中準確率更高且效率更高，而當複雜度升高，推理模
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