Unverified50% confidenceSolutionExact time
Evals produce scores that can be hill-climbed, and cross-checking with judge Agents from different models can be used to avoid scoring bias.
1
Sources
50%
Confidence
Long-term
Relevance
9/11/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedCascade智能体采用Agentic架构，能自主规划执行步骤、调用工具链、观察执行结果并迭代调整，与传统Copilot式逐行补全有本质区别66% similarVerifiedHill-climbing is a class of local search algorithms in computer science that starts from an initial solution and continuously makes local adjustments toward a better objective function value until no further improvement can be made.65% similarUnverifiedAdaptiveSpec's marginal rule and tree strategy operate on orthogonal dimensions—the former optimizes verification decisions and the latter optimizes generation decisions—and their effects can be compounded.62% similarUnverifiedHaving the model roll forward a number of steps and then roll backward the same number of steps should ideally return to the starting point, and the discrepancy between the actual return position and the starting point serves as a self-supervised proxy metric for unobservable rollout error.62% similarUnverified多智能体并行探索机制与AlphaGo的蒙特卡洛树搜索有相似的设计哲学，即用大规模并行探索替代单线程深度推理61% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/900471API
curl https://kongchang.com/api/v1/knowledge/claims/900471MCP
get_claim(id=900471)