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Researchers Warn Against Treating AI Outputs as Human-Like Reasoning
Arizona State University researchers are pushing back [PDF] against the widespread practice of describing AI language models' intermediate text generation as "reasoning" or "thinking," arguing this anthropomorphization creates dangerous misconceptions about how these systems actually work. The resea...
Arizona State University researchers are pushing back[PDF] against the widespread practice of describing AI language models' intermediate text generation as "reasoning" or "thinking," arguing this anthropomorphization creates dangerous misconceptions about how these systems actually work. The research team, led by Subbarao Kambhampati, examined recent "reasoning" models like DeepSeek's R1, which generate lengthy intermediate token sequences before providing final answers to complex problems. The paper warns that treating these intermediate outputs as interpretable reasoning traces engenders false confidence in AI capabilities and may mislead both researchers and users about the systems' actual problem-solving mechanisms.
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