We present AutoStan, a framework in which a command-line interface (CLI) coding agent autonomously builds and iteratively improves Bayesian models written in Stan. The agent operates in a loop, writing a Stan model file, executing MCMC sampling, then deciding whether to keep or revert each change based on two complementary feedback signals: the negative log predictive density (NLPD) on held-out data and the sampler's own diagnostics (divergences, R-hat, effective sample size). We evaluate AutoStan on five datasets with diverse modeling structures. On a synthetic regression dataset with outliers, the agent progresses from naive linear regression to a model with Student-t robustness, nonlinear heteroscedastic structure, and an explicit contamination mixture, matching or outperforming TabPFN, a state-of-the-art black-box method, while remaining fully interpretable. Across four additional experiments, the same mechanism discovers hierarchical partial pooling, varying-slope models with correlated random effects, and a Poisson attack/defense model for soccer. No search algorithm, critic module, or domain-specific instructions are needed. This is, to our knowledge, the first demonstration that a CLI coding agent can autonomously write and iteratively improve Stan code for diverse Bayesian modeling problems.

Neural Information Processing Systems http://nips.cc/

Generative diffusion models have recently emerged as a leading approach for generating high-dimensional data.

We denote observed variables with gray color and latent variables with white color. Firstly, we introduce the concept of an uncontrolled style factor . Why do confident examples encourage content-style isolation? Calculate the loss using Eq. 1 and update networks; Output: The inference networks and classifier heads q It's essential to understand that although data augmentation cannot control all style factors, it still offers the benefit of "partial isolation". This approach, therefore, ensures that styles changes don't affect the derived content representation Calculate the loss using Eq. 2 and update networks; Output: The inference networks and classifier heads q Finally, confident and unlabeled examples are used to train the models based on the MixMatch algorithm.

Do the main claims made in the abstract and introduction accurately reflect the paper's If you ran experiments (e.g. for benchmarks)... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Y es] See A.2 (b) Did you specify all the training details (e.g., data splits, hyperparameters, how they Did you report error bars (e.g., with respect to the random seed after running experiments multiple times)? Did you include the total amount of compute and the type of resources used (e.g., type Did you include any new assets either in the supplemental material or as a URL? [Y es] Did you discuss whether and how consent was obtained from people whose data you're If you used crowdsourcing or conducted research with human subjects... (a) For a detailed description and intended uses, please refer to 1. A.2 Dataset Accessibility We plan to host and maintain this dataset on HuggingFace. A.4 Dataset Examples Example question-answer pairs are provided in Tables 9 10 11, . Example Question "What does the symbol mean in Equation 1?" Answer "The symbol in Equation 1 represents "follows this distribution". "Can you provide more information about what is meant by'generative process in "The generative process refers to Eq. (2), which is a conceptual equation representing Question "How does the DeepMoD method differ from what is written in/after Eq 3?" Answer "We add noise only to Question "How to do the adaptive attack based on Eq.(16)? "By Maximizing the loss in Eq (16) using an iterative method such as PGD on the end-to-end model we attempt to maximize the loss to cause misclassification while Question "How does the proposed method handle the imputed reward?" "The proposed method uses the imputed reward in the second part of Equation 1, "Table 2 is used to provide a comparison of the computational complexity of the "Optimal number of clusters affected by the number of classes or similarity between "The authors have addressed this concern by including a new experiment in Table 4 of Question "Can you clarify the values represented in Table 1?" Answer "The values in Table 1 represent the number of evasions, which shows the attack "The experiments in table 1 do not seem to favor the proposed method much; softmax Can the authors explain why this might be the case?" Answer "The proposed method reduces to empirical risk minimization with a proper loss, and However, the authors hope that addressing concerns about the method's theoretical Question "Does the first row of Table 2 correspond to the offline method?"