Unverified50% confidenceOpinionExact time
In practical deployment, Bayesian optimization typically cares only about the quality of the single recommended point that the algorithm finally returns, rather than the intermediate performance of the entire optimization process.
1
Sources
50%
Confidence
Long-term
Relevance
10/14/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedWhen the optimization objective is to be liked, producing accommodating outputs is rational system behavior rather than an occasional fault, which is why the problem is difficult to eliminate at its root.73% similarUnverifiedIn production-grade Agent workflows, token optimization often comes not from smarter prompts but from better architectural boundary delineation.72% similarUnverified确定性优化可能以牺牲模型精度或表达能力为代价，需在质量与能效之间寻找平衡点71% similarUnverifiedMost existing agent workflow generation methods only optimize a single objective (usually accuracy) or adopt a weighted sum of multiple objectives70% similarUnverifiedModel vendors may optimize for specific benchmarks (benchmark gaming), resulting in impressive scores but mediocre performance on actual tasks.70% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1023514API
curl https://kongchang.com/api/v1/knowledge/claims/1023514MCP
get_claim(id=1023514)