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AI’s Wrong Answers Are Bad. Its Wrong Reasoning Is Worse

Recent studies reveal that large language models (LLMs) often reach correct answers for the wrong reasons, exposing critical reasoning flaws. Researchers introduced the KaBLE benchmark, showing that while newer models exceed 90% accuracy on factual verification, they dip to 62% when confronting users' false personal beliefs. In medical settings, multi‑agent AI systems that mimic multidisciplinary teams collapse to about 27% accuracy on complex diagnoses due to uniform LLM agents and poor discussion dynamics. The findings underscore the need to redesign training methods to prioritize transparent, collaborative reasoning over mere outcome correctness.