Unverified50% confidenceFactExact time
The model may give different conclusions when asked twice about the same piece of code, and may also give a plausible-sounding description of a problem that does not exist (hallucinated alerts).
1
Sources
50%
Confidence
Long-term
Relevance
10/21/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedWhen a model that generated code reviews its own output, it tends to follow the same assumptions and reasoning paths that produced the code, and may systematically overlook the same kinds of errors, which is called self-evaluation bias.77% similarUnverifiedWhen measuring the controllability of reasoning models in the future, it is necessary to distinguish between the "prompt-specified" and "self-instruction" conditions, otherwise the conclusions may be distorted.77% similarUnverifiedWhen generating each token, the model samples independently or takes the highest-probability token (depending on the temperature parameter), and it has no mechanism to "go back and revise" what it has already generated, so it may produce code that looks plausible but is actually wrong.75% similarUnverifiedWhen a model performs multi-layer recursive self-reference, self-descriptions from different rounds may contradict each other, because each output is an independent probabilistic sample rather than derived from a unified, persistent self-representation.75% similar
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