The `ccusage` npm package pulls prices and other information from LiteLLM which has a lot of diferent models: https://raw.githubusercontent.com/BerriAI/litellm/main/model...

Note that this doesn't represent a general break of RSA-2048, and doesn't affect the security of RSA-2048 as it's used anywhere.

The paper only applies to "special integers" where the prime factors are known to only differ by two bits.

Looking at the source code with web inspector it seems to be powered by 3D gaussian splatting and BabylonJS (https://doc.babylonjs.com/features/featuresDeepDive/mesh/gau...).

Maybe look into implicit in-order forests (https://thume.ca/2021/03/14/iforests/)

Figure 3 on p.40 of the paper seems to show that their LLM based model does not statistically significantly outperform a 3 layer neural network using 59 variables from 1989.

  This figure compares the prediction performance of GPT and quantitative models based on machine learning. Stepwise Logistic follows Ou and Penman (1989)’s structure with their 59 financial predictors. ANN is a three-layer artificial neural network model using the same set of variables as in Ou and Penman (1989). GPT (with CoT) provides the model with financial statement information and detailed chain-of-thought prompts. We report average accuracy (the percentage of correct predictions out of total predictions) for each method (left) and F1 score (right). We obtain bootstrapped standard errors by randomly sampling 1,000 observations 1,000 times and include 95% confidence intervals.