arXiv AI August 6, 2026 By Yongchao Huang

SJEPA: Learning Elegant Latent Dynamics with Hybrid Symbolic-Neural Predictors

arXiv:2608. 04060v1 Announce Type: cross Abstract: Joint-embedding predictive architectures learn abstract states by predicting target embeddings from context embeddings, but their transition models are typically opaque neural maps.

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arXiv Statistics ML

Aug 25

Symbolic Neural ODEs: Learning interpretable models from time-series data

arXiv:2608.22112v1 Announce Type: cross Abstract: We present a machine learning framework for identifying sparse, interpretable models of dynamical systems directly from time-series data. Our approac...

By Nibodh Boddupalli, Jeff Moehlis

efficiency

arXiv AI

Jun 18

OrthoReg: Orthogonal Regularization for Hybrid Symbolic-Neural Dynamical Systems

arXiv:2606. 19145v1 Announce Type: cross Abstract: Dynamical systems are fundamental to modeling the natural world, yet modeling them involves a persistent trade-off: manually prescribed mechanistic models are interpretable by design but often overly simplistic and misspecified; in contrast, flexible data-driven neural methods lack physical insight.

By Till Richter, Niki Kilbertus

benchmarks

arXiv Machine Learning

Jul 22

Hierarchical Physics-Embedded Learning for Partially Known Spatiotemporal Dynamics

arXiv:2510. 25306v3 Announce Type: replace Abstract: Partial physical knowledge--governing structures known, constitutive relations or their combinations not--pervades spatiotemporal systems.

By Xizhe Wang, Xiaobin Song, Hongbo Zhao, Qingshan Jia, Qianchuan Zhao, Hao Sun, Benben Jiang

rag benchmarks

Hugging Face Trending Papers

Aug 24

LpWM: A Case for Sparse Representations in World Models

Joint-embedding predictive architectures (JEPAs) learn latent dynamics for planning and avoid representation collapse by matching features to maximum-entropy distributions such as isotropic Gaussians,...

rag diffusion efficiency

arXiv Machine Learning

Sep 2

Neural Symbollic Regression Using Deep Learning and Sparse Modelling

Neural Symbolic Regression (NSR) uses neural networks as functional preconditioners to learn smooth, noise‑robust approximations of target functions in an interaction‑aware nonlinear feature space. A subsequent LASSO step extracts sparse, interpretable closed‑form expressions, while distributed hyperparameter optimization with Ray Tune and ASHA scheduling improves predictive accuracy and symbolic fidelity. Experiments on the Nguyen benchmark demonstrate that NSR outperforms SINDy and untuned neural baselines in RMSE, noise robustness, and out‑of‑distribution generalization, with ablation studies highlighting the importance of feature interactions, neural depth, and tuning strategies.

By Ravi Kumar U, Sumitra S

benchmarks safety

arXiv AI

Jun 9

EditSR: Enhancing Neural Symbolic Regression via Edit-based Rectification

arXiv:2606. 07915v1 Announce Type: new Abstract: Neural symbolic regression models improve inference efficiency by shifting structural search to pretraining, but their one-pass autoregressive decoding is prone to error accumulation, which may lead to generating structurally incorrect expressions, especially in complex expression generation scenarios.

By Da Li, Xinxin Li, Xingyu Cui, Jin Xu, Juan Zhang, Junping Yin