Unverified50% confidenceFactExact time
In the presence of external rules, more informative data may instead be harmful, which means that usage risk in general cannot be expressed as an expected reduction in uncertainty, and standard BED is merely a special case of the usage-aware framework.
1
Sources
50%
Confidence
Long-term
Relevance
10/16/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedGeneral-purpose large language models exhibit inconsistency in security judgments, and the same risky operation may bypass their security mechanisms simply by being rephrased or provided with a justifying context76% similarUnverifiedEstimation of usage risk itself depends on assumptions about the distribution of external rules; when the set of rules is completely unknown, worst-case optimization may be overly conservative, causing a substantial drop in experimental design efficiency.74% similarUnverifiedAllocating R&D funding or regulatory attention based on unstable probability estimates may lead to overinvestment in low-probability scenarios while neglecting more urgent but 'not sexy enough' risks.73% similarUnverifiedThe predictive variance of a Gaussian process quantifies uncertainty: the variance is large where data are sparse and small where data are dense, which is the theoretical basis for safe exploration in the algorithm.72% similarUnverifiedOnce a public benchmark attracts widespread attention, it risks being targeted for optimization, and high scores do not necessarily reflect generalized reasoning ability.72% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1042138API
curl https://kongchang.com/api/v1/knowledge/claims/1042138MCP
get_claim(id=1042138)