MOSAIC: Module Discovery via Sparse Additive Identifiable Causal Learning for Scientific Time Series

arXiv:2605.05524 · cs.LG, cs.AI · Submitted 2026-05-06 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-05-06

Updated: 2026-10-15

Comments: Accepted at NeurIPS 2026. 39 pages, including references and appendices

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Interpretability — 3× in this paper · explained in 15 episodes
• Latent Variables — 2× in this paper · explained in 2 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Decoder — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Representation Learning — 1× in this paper · explained in 3 episodes

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: The Predictive Cost of ELBO Binding
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks