Verified65% confidenceSolutionExact time
When evaluating the capabilities of large models, the stability across multiple rounds of testing reflects true ability better than a single correct answer.
3
Sources
65%
Confidence
Long-term
Relevance
7/20/2026
First Seen
Sources
Related Claims
Unverified通过回归测试主要验证功能正确性和输出一致性，不覆盖并发竞态条件、极端负载行为或长时间运行稳定性81% similarUnverifiedA more pragmatic approach for critical tasks is to establish small-scale benchmarks and regularly test model stability using fixed questions.79% similarUnverifiedTraditional benchmarks tend to focus on accuracy and inference speed, but task completion rate and convergence stability are equally important or even more critical in real-world engineering scenarios.77% similarUnverifiedClustering quality directly affects the reliability of SmoothScore, so researchers usually need to combine semantic similarity with manual verification to build stable evidence clusters.77% similarUnverifiedThe study validates that candidate answers the model considers more probable are more persuasive, and that the effectiveness of evidence depends on whether it aligns with the model's prior inclinations.75% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/573197API
curl https://kongchang.com/api/v1/knowledge/claims/573197MCP
get_claim(id=573197)