Unverified50% confidenceFactExact time
A study published on arXiv (paper number arXiv:2609.28475v1) proposes that more reasoning does not always lead to better predictions.
1
Sources
50%
Confidence
Long-term
Relevance
9/25/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedA study published on arXiv (arXiv:2609.02899) proposes that concerns about benchmark contamination should be distinguished into two separate questions: whether absolute scores are inflated and whether the ranking order of models is changed.72% similarUnverified泰特洛克的研究得出结论：专家预测的准确率仅略高于随机猜测，甚至不如简单的统计基准线69% similarUnverifiedA study numbered arXiv:2609.10728 proposes using the Program-Solve method to address the arithmetic unreliability of large models in clinical calculator scenarios.68% similarUnverifiedThe remaining 8.5% deviation is amplified when involving billions of samples, and minor systematic biases may cause simulation conclusions to diverge significantly from reality; therefore, such simulations are better suited for trend prediction and relative comparison rather than absolutely precise numerical forecasts.64% similarUnverifiedDeep learning models are generally prone to overconfidence, where the probability values output by a model do not truly reflect the actual probability of its predictions being correct — a phenomenon known as poor calibration.60% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/948966API
curl https://kongchang.com/api/v1/knowledge/claims/948966MCP
get_claim(id=948966)