Unverified70% confidenceFactExact time
A 2023 study by Huang et al. pointed out that self-improvement of LLMs without external feedback is not always effective and can easily lead to degraded performance, especially in reasoning tasks.
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9/11/2026
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VerifiedLonger inference time does not always yield better results; models may fall into the trap of overthinking, and what truly determines output quality is the model's foundational capabilities and degree of alignment.69% similarUnverifiedIncreasing the number of agents does not necessarily improve reliability; homogeneous and mutually influencing agents may instead accelerate the formation of erroneous consensus.68% similarUnverifiedTreating LLMs as genuine intelligence leads to overestimating their reliability, mistakenly trusting their reasoning capabilities, and lacking vigilance regarding the hallucination problem.68% similarUnverifiedSome opponents point out that hard constraints such as compute, data, and experimental validation exist in reality, which may make the recursive self-improvement process far slower than theoretical设想.68% similarVerifiedThe study observed a clear lack of correlation between the correctness of answers and the validity of reasoning traces, with models frequently arriving at correct answers even when the reasoning traces are completely invalid.68% similar
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