arXiv Machine Learning

Discovering Multiscale Deep Formulas in Complex Systems via Neural-Guided Lambda Calculus

arXiv:2606. 07426v1 Announce Type: new Abstract: A fundamental problem in science is identifying underlying patterns of complex systems in the form of concise mathematical formulas.

arXiv AI

Jul 29

Structured Scaling of AI Discovery Across Diverse Scientific Domains

arXiv:2604. 19341v2 Announce Type: replace-cross Abstract: Scientific discovery often requires many cycles of proposing, testing, and refining candidate solutions.

By Haotian Ye, Haowei Lin, Jingyi Tang, Yizhen Luo, Rahul Thapa, Caiyin Yang, Chang Su, Rui Yang, Ruihua Liu, Rundao Li, Zeyu Li, Pengwei Sun, Chong Gao, Dachao Ding, Guangrong He, Miaolei Zhang, Lina Sun, Wenyang Wang, Yuchen Zhong, Zhuohao Shen, Puheng Li, Pan Lu, Bianxiao Cui, Di He, Jianzhu Ma, Junfeng Li, Hexi Baoyin, Yejin Choi, Stefano Ermon, Xiaowen Chu, Tongyang Li, Yuzhi Xu, James Zou

llms benchmarks

arXiv AI

Aug 5

Deep Divide-and-Reduce in Symbolic Regression

arXiv:2608. 02628v1 Announce Type: cross Abstract: Symbolic regression (SR) is the task of discovering underlying patterns from data and representing them using mathematical expressions.

By Yusong Deng, Yanjie Li, Weijun Li

arXiv AI

Jul 2

A Multi-Resolution Finite-Volume Inspired Deep Learning Framework for Spatiotemporal Dynamics Prediction

arXiv:2607. 00460v1 Announce Type: cross Abstract: Predicting complex spatiotemporal dynamics in physical processes often demands computationally expensive numerical methods or data-driven neural networks that suffer from high training costs, error accumulation, and limited generalizability to unseen parameters.

By Xin-Yang Liu, Xiantao Fan, Jian-Xun Wang

rag safety

arXiv Machine Learning

Jul 30

From Classification to Regression: Using a Fruitfly to Solve Equations

arXiv:2607. 27196v1 Announce Type: new Abstract: We present a novel approach to regression tasks using classification which is motivated by the mechanism used by fruitflies to sense their environment.

By Shady E. Ahmed, Panos Stinis

rag

arXiv Machine Learning

Jul 27

Breaking the Data Barrier in Learning Symbolic Computation: A Case Study on Variable Ordering Suggestion for Cylindrical Algebraic Decomposition

arXiv:2601. 13731v2 Announce Type: replace-cross Abstract: Symbolic computation, powered by modern computer algebra systems, has important applications in mathematical reasoning through exact deep computations.

By Rui-Juan Jing, Yuegang Zhao, Changbo Chen

llms fine-tuning

arXiv AI

Jul 14

Recursive Multi-Agent Systems

arXiv:2604. 25917v2 Announce Type: replace Abstract: Recursive or looped language models have recently emerged as a new scaling axis by iteratively refining the same model computation over latent states to deepen reasoning.

By Jiaru Zou, Rui Pan, Ruizhong Qiu, Pan Lu, Shizhe Diao, Jindong Jiang, Hanghang Tong, Tong Zhang, Markus J. Buehler, Jingrui He, James Zou

llms agents benchmarks

arXiv Machine Learning

Jul 15

PRIME: Protein Representation via Physics-Informed Multiscale Equivariant Hierarchies

arXiv:2605. 01625v3 Announce Type: replace Abstract: Proteins are inherently multiscale physical systems whose functional properties emerge from coordinated structural organization across multiple spatial resolutions, ranging from atomic interactions to global fold topology.

By Viet Thanh Duy Nguyen, John K. Johnstone, Truong-Son Hy

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

arXiv Machine Learning

Jul 1

Joint discovery of governing partial differential equations from multi-source datasets by competitive optimization

arXiv:2606. 30699v1 Announce Type: new Abstract: Discovering governing equations directly from observational data is a key step towards interpretable scientific machine learning.

By Hao Xu, Siyu Lou, Yuntian Chen, Dongxiao Zhang

arXiv Machine Learning

Jun 9

Decomposable Neuro Symbolic Regression

arXiv:2511. 04124v3 Announce Type: replace Abstract: Symbolic regression (SR) models complex systems by discovering mathematical expressions that capture underlying relationships in observed data.

By Giorgio Morales, John W. Sheppard

llms benchmarks

arXiv Machine Learning

Jul 10

DeepPySR -- A Symbolic Regression Framework with Dynamic Pruning, Pareto Selection, and Hierarchical Composition for Real-World Scientific Discovery

arXiv:2607. 08150v1 Announce Type: new Abstract: Symbolic regression (SR) discovers analytical equations from data, yielding glass-box models with directly interpretable formulas, unlike black-box methods that rely on unstable post-hoc tools such as SHAP or LIME.

By Fuling Chen, Kevin Vinsen, Phillip Melton, Rae-Chi Huang

efficiency benchmarks

arXiv Machine Learning

Jun 30

Randomized neural operator for parametric PDEs with fast training and conformal uncertainty quantification

arXiv:2606. 29440v1 Announce Type: new Abstract: Repeatedly solving parametric PDEs is essential for uncertainty quantification, design optimization and inverse problems, but conventional neural operators require expensive non-convex training.

By Zirui Deng, Jingbo Sun, Deyu Meng, Fei Wang

benchmarks

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