Unverified50% confidenceCautionExact time
Large language models exhibit an unfaithful execution problem: Opus sometimes arbitrarily changes definitions on its own, and upon finding that results do not match, writes code to override the derived results and directly fills in the answer it considers correct.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
9/16/2026
First Seen
Valid until: 12/15/2026
Sources
Related Entities
Related Claims
VerifiedLarge language models tend to solve problems using general reasoning while lacking strict adherence to domain-specific rules and self-verification capabilities.76% similarUnverifiedWhen a model lacks accurate knowledge of a particular library, it tends to generate syntactically correct but semantically incorrect code—so-called 'confident errors.'76% similarVerifiedLarge models are prone to 'hallucination' when handling professional domain questions, meaning they confidently provide incorrect answers.76% similarVerifiedWhen the model itself lacks the capability to understand a specific problem, no amount of prompt engineering can compensate for this deficiency74% similarUnverifiedResearch indicates that models may produce unfaithful chains of thought, where the surface-level reasoning steps are inconsistent with the actual basis for decision-making.73% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/924551API
curl https://kongchang.com/api/v1/knowledge/claims/924551MCP
get_claim(id=924551)