Unverified50% confidenceFactExact time
The evaluation used only one prompt, ran each model only once, used greedy decoding, scored answers based on normalized exact match, and did not score evidence_ids.
1
Sources
50%
Confidence
Long-term
Relevance
10/11/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedExisting science benchmarks provide all known quantities to the model at once, requiring only one-shot symbolic reasoning and bypassing the step of acquiring information through experimentation75% similarUnverifiedWhen the true effect cannot be directly verified, a round of A/A testing can be run on candidate causal models first, and then the model to trust can be determined based on estimation error.75% similarUnverified选择和使用不同模型档位时不能仅凭版本号判断，基于真实场景的多轮测试才是评估模型能力的可靠依据75% similarUnverified同一个模型用同一套假设检查自己的代码天然看不到盲点，跨模型审查因训练侧重点不同才具备互补价值74% similarUnverifiedEach question in the benchmark has a single correct answer derived by Python computation; the scorer requires models to end with ANSWER:<value>, EMI allows a rounding tolerance of ±₹2, and the formatting group requires exact string matching.73% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/999246API
curl https://kongchang.com/api/v1/knowledge/claims/999246MCP
get_claim(id=999246)