Unverified50% confidenceSolutionExact time
由于LLM的非确定性，单次测试通过无法区分80%可靠和100%可靠的系统，因此重复运行多次是Agent测试的必要方法
1
Sources
50%
Confidence
Long-term
Relevance
8/7/2026
First Seen
Sources
Related Claims
UnverifiedThe non-determinism of LLMs (where identical inputs may produce different outputs) fundamentally conflicts with the testing domain's requirement for reproducibility, and is a core engineering challenge for AI testing Agents.74% similarUnverifiedClassical-school tests avoid brittleness by verifying only the final state, but at the cost of making it harder to pinpoint the root cause when a test fails, because multiple real components participate in the test simultaneously.73% similarUnverified对抗测试的效果高度依赖场景库的完备性，未被预见的攻击路径依然是盲区，单次通过测试不代表Agent在生产中永远安全71% similarVerifiedStrategies for mitigating LLM judge instability include majority voting over multiple samples, cross-validation using multiple judge models, designing more fine-grained scoring dimensions, and retaining a manual review step.71% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/700679API
curl https://kongchang.com/api/v1/knowledge/claims/700679MCP
get_claim(id=700679)