Unverified50% confidenceOpinionExact time
The model's mildness is not accidental but the product of an explicitly optimized objective function; style drift or a particular "punch" may also stem from edge cases that the reward signal does not cover, or from behavioral differences across versions and system prompts.
1
Sources
50%
Confidence
Long-term
Relevance
10/26/2026
First Seen
Sources
Related Entities
Related Claims
VerifiedA well-aligned model will guide meta-prompts toward compliant creative expression, while an insufficiently aligned model may exhibit unexpected behavior.77% similarVerifiedFine-tuning can deeply alter a model's behavioral patterns, whereas prompt engineering can only operate within the boundaries of the model's existing capabilities77% similarUnverified某些越狱攻击的有效性来自于诱导模型进入特定吸引子状态，使输出在该框架内自我强化74% similarVerifiedModel behavior drift means that even if the code and prompts are unchanged, after the underlying model is silently updated, temperature parameters change, or context length is adjusted, the output distribution for the same input may also change.74% similarUnverifiedFine-tuning acts directly on representations the model has already learned, and may simultaneously perturb other behaviors that share internal representations with the target behavior; this is the technical soil in which behavioral coupling occurs.73% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1101507API
curl https://kongchang.com/api/v1/knowledge/claims/1101507MCP
get_claim(id=1101507)