Unverified50% confidencePredictionExact time
If a large amount of thinking is done through the same category of models, then the models' biases, hallucinations, or interface outages will affect the entire landscape of cognitive activity.
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50%
Confidence
Long-term
Relevance
11/1/2026
First Seen
Sources
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Related Claims
UnverifiedThere is currently no conclusion showing that thinking with LLMs degrades people's thinking ability; the key lies in how they are used: treating the model as a first reaction easily weakens independent thinking, while treating it as a second opinion may amplify cognitive ability.76% similarUnverifiedA model with metacognition will proactively express uncertainty and suggest calling external tools when it exceeds the boundaries of its knowledge, rather than generating hallucinated answers.75% similarUnverifiedAfter prolonged high-frequency interaction with a system, the brain gradually simulates and predicts its response patterns, language style, and thinking framework, forming a phenomenon of cognitive internalization.75% similarVerifiedThe technical root cause of hallucination in large models lies in the autoregressive generation mechanism, where the model predicts only the statistically most likely next token based on the preceding context; researchers divide hallucinations into intrinsic hallucinations and extrinsic hallucinations.74% similarUnverifiedAblation experiments on semantic attributes revealed that semantic contributions span multiple dimensions, producing cumulative effects on the predicted variance of subjects' brains rather than being dominated by a single dimension.74% similar
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