1 citations · 2 across the 6 of their papers we have counts for
6 papers
From Symbolic Perception to Logical Deduction: A Framework for Guiding Language Models in Geometric Reasoning
Weichen Dai, Rafael Medeiros Cabral, Ziyi Shou +4
Plane geometry remains a significant challenge in AI, requiring the integration of visual perception and mathematical reasoning. While Large Multimodal Models (LMMs) naturally hand…
Internalizing Geometric Law: Learning from Solver Residuals for Precision-Critical Generation
Rafael Cabral, Pang Zixi, Ziyi Shou +1
Large Language Models frequently hallucinate in precision-critical domains such as technical diagramming and mechanical design, where outputs must satisfy strict geometric constrai…
ProofFlow: A Dependency Graph Approach to Faithful Proof Autoformalization
Rafael Cabral, Tuan Manh Do, Xuejun Yu +3
Proof autoformalization, the task of translating natural language theorems and proofs into machine-verifiable code, is a critical step for integrating large language models into ri…
Robustness, model checking and latent Gaussian models
Rafael Cabral, David Bolin, Håvard Rue
Model checking is essential to evaluate the adequacy of statistical models and the validity of inferences drawn from them. Particularly, hierarchical models such as latent Gaussian…
Fitting latent non-Gaussian models using variational Bayes and Laplace approximations
Rafael Cabral, David Bolin, Håvard Rue
Latent Gaussian models (LGMs) are perhaps the most commonly used class of models in statistical applications. Nevertheless, in areas ranging from longitudinal studies in biostatist…
Controlling the flexibility of non-Gaussian processes through shrinkage priors
Rafael Cabral, David Bolin, Håvard Rue
The normal inverse Gaussian (NIG) and generalized asymmetric Laplace (GAL) distributions can be seen as skewed and semi-heavy-tailed extensions of the Gaussian distribution. Models…