{"total":203,"rows":[{"id":"2404.18400","title":"LLM-SR: Scientific Equation Discovery via Programming with Large Language Models","created":"2024-04-29","cited_by_count":133,"area":null},{"id":"2211.10873","title":"Interpretable Scientific Discovery with Symbolic Regression: A Review","created":"2022-11-20","cited_by_count":105,"area":null},{"id":"2505.13259","title":"From Automation to Autonomy: A Survey on Large Language Models in Scientific Discovery","created":"2025-05-19","cited_by_count":93,"area":null},{"id":"2303.06833","title":"Transformer-based Planning for Symbolic Regression","created":"2023-03-13","cited_by_count":79,"area":null},{"id":"2207.01018","title":"Symbolic Regression is NP-hard","created":"2022-07-03","cited_by_count":71,"area":null},{"id":"2504.10415","title":"LLM-SRBench: A New Benchmark for Scientific Equation Discovery with Large Language Models","created":"2025-04-14","cited_by_count":71,"area":null},{"id":"2310.05573","title":"ODEFormer: Symbolic Regression of Dynamical Systems with Transformers","created":"2023-10-09","cited_by_count":62,"area":null},{"id":"2205.15764","title":"SymFormer: End-to-end symbolic regression using transformer-based architecture","created":"2022-05-31","cited_by_count":59,"area":null},{"id":"2305.01582","title":"Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl","created":"2023-05-02","cited_by_count":54,"area":null},{"id":"2310.02227","title":"SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-training","created":"2023-10-03","cited_by_count":47,"area":null},{"id":"2206.10540","title":"Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery","created":"2022-06-21","cited_by_count":46,"area":null},{"id":"2404.12355","title":"Towards a Foundation Model for Partial Differential Equations: Multi-Operator Learning and Extrapolation","created":"2024-04-18","cited_by_count":46,"area":null},{"id":"2302.11223","title":"Deep Generative Symbolic Regression with Monte-Carlo-Tree-Search","created":"2023-02-22","cited_by_count":41,"area":null},{"id":"2412.11427","title":"Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges","created":"2024-12-16","cited_by_count":41,"area":null},{"id":"2510.07172","title":"NewtonBench: Benchmarking Generalizable Scientific Law Discovery in LLM Agents","created":"2025-10-08","cited_by_count":36,"area":"agents"},{"id":"2510.11661","title":"SR-Scientist: Scientific Equation Discovery With Agentic AI","created":"2025-10-13","cited_by_count":35,"area":"agents"},{"id":"2409.09811","title":"PROSE-FD: A Multimodal PDE Foundation Model for Learning Multiple Operators for Forecasting Fluid Dynamics","created":"2024-09-15","cited_by_count":34,"area":null},{"id":"2404.19094","title":"In-Context Symbolic Regression: Leveraging Large Language Models for Function Discovery","created":"2024-04-29","cited_by_count":31,"area":null},{"id":"2401.00282","title":"Deep Generative Symbolic Regression","created":"2023-12-30","cited_by_count":28,"area":null},{"id":"2307.12617","title":"Predicting Ordinary Differential Equations with Transformers","created":"2023-07-24","cited_by_count":26,"area":null},{"id":"2301.05638","title":"Deep Learning Symmetries and Their Lie Groups, Algebras, and Subalgebras from First Principles","created":"2023-01-13","cited_by_count":22,"area":null},{"id":"2506.04282","title":"DrSR: LLM based Scientific Equation Discovery with Dual Reasoning from Data and Experience","created":"2025-06-04","cited_by_count":22,"area":"lr"},{"id":"2304.10336","title":"Controllable Neural Symbolic Regression","created":"2023-04-20","cited_by_count":20,"area":null},{"id":"2505.23947","title":"Position: The Future of Bayesian Prediction Is Prior-Fitted","created":"2025-05-29","cited_by_count":20,"area":null},{"id":"2305.02251","title":"Automated Scientific Discovery: From Equation Discovery to Autonomous Discovery Systems","created":"2023-05-03","cited_by_count":19,"area":null},{"id":"2501.15638","title":"A Comprehensive Survey on Self-Interpretable Neural Networks","created":"2025-01-26","cited_by_count":18,"area":null},{"id":"2411.03753","title":"Symbolic regression via MDLformer-guided search: from minimizing prediction error to minimizing description length","created":"2024-11-06","cited_by_count":17,"area":null},{"id":"2505.03977","title":"Call for Action: towards the next generation of symbolic regression benchmark","created":"2025-05-06","cited_by_count":17,"area":null},{"id":"2309.13705","title":"A Neural-Guided Dynamic Symbolic Network for Exploring Mathematical Expressions from Data","created":"2023-09-24","cited_by_count":16,"area":null},{"id":"2309.16816","title":"PROSE: Predicting Operators and Symbolic Expressions using Multimodal Transformers","created":"2023-09-28","cited_by_count":16,"area":null},{"id":"2408.16168","title":"LeMON: Learning to Learn Multi-Operator Networks","created":"2024-08-28","cited_by_count":14,"area":null},{"id":"2503.23616","title":"Interpretable Machine Learning in Physics: A Review","created":"2025-03-30","cited_by_count":14,"area":null},{"id":"2302.09893","title":"Efficient Generator of Mathematical Expressions for Symbolic Regression","created":"2023-02-20","cited_by_count":13,"area":null},{"id":"2402.18603","title":"MMSR: Symbolic Regression is a Multi-Modal Information Fusion Task","created":"2024-02-28","cited_by_count":13,"area":null},{"id":"2404.11341","title":"The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology","created":"2024-04-17","cited_by_count":12,"area":null},{"id":"2406.02470","title":"Meta-Designing Quantum Experiments with Language Models","created":"2024-06-04","cited_by_count":12,"area":null},{"id":"2602.18266","title":"A Probabilistic Framework for LLM-Based Model Discovery","created":"2026-02-20","cited_by_count":12,"area":null},{"id":"2205.15569","title":"GSR: A Generalized Symbolic Regression Approach","created":"2022-05-31","cited_by_count":11,"area":null},{"id":"2503.07994","title":"A Neural Symbolic Model for Space Physics","created":"2025-03-11","cited_by_count":11,"area":null},{"id":"2304.06333","title":"Priors for symbolic regression","created":"2023-04-13","cited_by_count":10,"area":null},{"id":"2310.05537","title":"ParFam -- (Neural Guided) Symbolic Regression Based on Continuous Global Optimization","created":"2023-10-09","cited_by_count":10,"area":null},{"id":"2312.04070","title":"A Transformer Model for Symbolic Regression towards Scientific Discovery","created":"2023-12-07","cited_by_count":9,"area":null},{"id":"2410.14673","title":"Self-supervised contrastive learning performs non-linear system identification","created":"2024-10-18","cited_by_count":9,"area":null},{"id":"2602.20557","title":"GENSR: Symbolic Regression Based in Equation Generative Space","created":"2026-02-24","cited_by_count":9,"area":null},{"id":"2210.08342","title":"Symbolic Recovery of Differential Equations: The Identifiability Problem","created":"2022-10-15","cited_by_count":8,"area":null},{"id":"2211.08420","title":"Is the Machine Smarter than the Theorist: Deriving Formulas for Particle Kinematics with Symbolic Regression","created":"2022-11-15","cited_by_count":8,"area":null},{"id":"2305.14656","title":"RSRM: Reinforcement Symbolic Regression Machine","created":"2023-05-24","cited_by_count":8,"area":null},{"id":"2205.11798","title":"Symbolic Expression Transformer: A Computer Vision Approach for Symbolic Regression","created":"2022-05-24","cited_by_count":7,"area":null},{"id":"2311.12904","title":"Learning to Compute Gr\\\"obner Bases","created":"2023-11-21","cited_by_count":7,"area":null},{"id":"2312.00586","title":"Explainable Fraud Detection with Deep Symbolic Classification","created":"2023-12-01","cited_by_count":7,"area":null},{"id":"2312.10913","title":"GINN-LP: A Growing Interpretable Neural Network for Discovering Multivariate Laurent Polynomial Equations","created":"2023-12-18","cited_by_count":7,"area":null},{"id":"2401.09949","title":"SymbolNet: Neural Symbolic Regression with Adaptive Dynamic Pruning for Compression","created":"2024-01-18","cited_by_count":7,"area":null},{"id":"2401.10840","title":"Symbolic Cognitive Diagnosis via Hybrid Optimization for Intelligent Education Systems","created":"2023-12-30","cited_by_count":7,"area":null},{"id":"2509.01398","title":"The Need for Verification in AI-Driven Scientific Discovery","created":"2025-09-01","cited_by_count":7,"area":null},{"id":"2509.20529","title":"MDBench: Benchmarking Data-Driven Methods for Model Discovery","created":"2025-09-24","cited_by_count":7,"area":null},{"id":"2512.23056","title":"PI-MFM: Physics-informed multimodal foundation model for solving partial differential equations","created":"2025-12-28","cited_by_count":7,"area":null},{"id":"2606.25039","title":"LLM-ACES: Closed-Loop Discovery of Dynamical Systems with LLM-Guided Adaptive Search","created":"2026-06-23","cited_by_count":7,"area":null},{"id":"2209.09543","title":"FACT: Learning Governing Abstractions Behind Integer Sequences","created":"2022-09-20","cited_by_count":6,"area":null},{"id":"2212.04630","title":"A PINN Approach to Symbolic Differential Operator Discovery with Sparse Data","created":"2022-12-09","cited_by_count":6,"area":null},{"id":"2304.01117","title":"Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition","created":"2023-04-03","cited_by_count":6,"area":null},{"id":"2306.04120","title":"MESSY Estimation: Maximum-Entropy based Stochastic and Symbolic densitY Estimation","created":"2023-06-07","cited_by_count":6,"area":null},{"id":"2402.00254","title":"Vertical Symbolic Regression via Deep Policy Gradient","created":"2024-02-01","cited_by_count":6,"area":"deeprl"},{"id":"2408.04720","title":"Learning the Simplicity of Scattering Amplitudes","created":"2024-08-08","cited_by_count":6,"area":null},{"id":"2504.02630","title":"Grammar-based Ordinary Differential Equation Discovery","created":"2025-04-03","cited_by_count":6,"area":null},{"id":"2505.12083","title":"Discovering Symbolic Differential Equations with Symmetry Invariants","created":"2025-05-17","cited_by_count":6,"area":null},{"id":"2505.18602","title":"LLM-Meta-SR: In-Context Learning for Evolving Selection Operators in Symbolic Regression","created":"2025-05-24","cited_by_count":6,"area":null},{"id":"2509.15929","title":"Improving Monte Carlo Tree Search for Symbolic Regression","created":"2025-09-19","cited_by_count":6,"area":null},{"id":"2509.18710","title":"Autonomous Data Agents: A New Opportunity for Smart Data","created":"2025-09-23","cited_by_count":6,"area":"agents"},{"id":"2511.08860","title":"When is a System Discoverable from Data? Discovery Requires Chaos","created":"2025-11-12","cited_by_count":6,"area":null},{"id":"2302.00806","title":"Oracle-Preserving Latent Flows","created":"2023-02-02","cited_by_count":5,"area":null},{"id":"2309.12207","title":"Boolformer: Symbolic Regression of Logic Functions with Transformers","created":"2023-09-21","cited_by_count":5,"area":null},{"id":"2310.13270","title":"Meta-learning of Physics-informed Neural Networks for Efficiently Solving Newly Given PDEs","created":"2023-10-20","cited_by_count":5,"area":null},{"id":"2401.15103","title":"PruneSymNet: A Symbolic Neural Network and Pruning Algorithm for Symbolic Regression","created":"2024-01-25","cited_by_count":5,"area":null},{"id":"2405.19973","title":"A Triumvirate of AI Driven Theoretical Discovery","created":"2024-05-30","cited_by_count":5,"area":null},{"id":"2406.03585","title":"A Comparison of Recent Algorithms for Symbolic Regression to Genetic Programming","created":"2024-06-05","cited_by_count":5,"area":null},{"id":"2406.05410","title":"ChatSR: Multimodal Large Language Models for Scientific Formula Discovery","created":"2024-06-08","cited_by_count":5,"area":"mllm"},{"id":"2411.16052","title":"Machine-learning emergent spacetime from linear response in future tabletop quantum gravity experiments","created":"2024-11-25","cited_by_count":5,"area":null},{"id":"2501.11350","title":"Adaptive parameters identification for nonlinear dynamics using deep permutation invariant networks","created":"2025-01-20","cited_by_count":5,"area":null},{"id":"2502.01702","title":"Al-Khwarizmi: Discovering Physical Laws with Foundation Models","created":"2025-02-03","cited_by_count":5,"area":null},{"id":"2506.19626","title":"Scaling Up Unbiased Search-based Symbolic Regression","created":"2025-06-24","cited_by_count":5,"area":null},{"id":"2602.10576","title":"LLM-Based Scientific Equation Discovery via Physics-Informed Token-Regularized Policy Optimization","created":"2026-02-11","cited_by_count":5,"area":null},{"id":"2309.07934","title":"Racing Control Variable Genetic Programming for Symbolic Regression","created":"2023-09-13","cited_by_count":4,"area":null},{"id":"2406.06751","title":"Complexity-Aware Deep Symbolic Regression with Robust Risk-Seeking Policy Gradients","created":"2024-06-10","cited_by_count":4,"area":"deeprl"},{"id":"2411.06833","title":"Learning Interpretable Network Dynamics via Universal Neural Symbolic Regression","created":"2024-11-11","cited_by_count":4,"area":null},{"id":"2412.01417","title":"Learning Elementary Cellular Automata with Transformers","created":"2024-12-02","cited_by_count":4,"area":null},{"id":"2505.15572","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","created":"2025-05-21","cited_by_count":4,"area":null},{"id":"2505.18798","title":"Governing Equation Discovery from Data Based on Differential Invariants","created":"2025-05-24","cited_by_count":4,"area":null},{"id":"2505.24776","title":"Diffusion-Based Symbolic Regression","created":"2025-05-30","cited_by_count":4,"area":null},{"id":"2506.18339","title":"Structured Kolmogorov-Arnold Neural ODEs for Interpretable Learning and Symbolic Discovery of Nonlinear Dynamics","created":"2025-06-23","cited_by_count":4,"area":null},{"id":"2507.06830","title":"Physics-Grounded Motion Forecasting via Equation Discovery for Trajectory-Guided Image-to-Video Generation","created":"2025-07-09","cited_by_count":4,"area":null},{"id":"2603.20910","title":"LLM-ODE: Data-driven Discovery of Dynamical Systems with Large Language Models","created":"2026-03-21","cited_by_count":4,"area":null},{"id":"2211.02830","title":"Discovering ordinary differential equations that govern time-series","created":"2022-11-05","cited_by_count":3,"area":null},{"id":"2306.04718","title":"Scalable Neural Symbolic Regression using Control Variables","created":"2023-06-07","cited_by_count":3,"area":null},{"id":"2401.04978","title":"Closed-Form Interpretation of Neural Network Classifiers with Symbolic Gradients","created":"2024-01-10","cited_by_count":3,"area":null},{"id":"2404.06330","title":"Generative Pre-Trained Transformer for Symbolic Regression Base In-Context Reinforcement Learning","created":"2024-04-09","cited_by_count":3,"area":"deeprl"},{"id":"2409.01416","title":"Active Symbolic Discovery of Ordinary Differential Equations via Phase Portrait Sketching","created":"2024-09-02","cited_by_count":3,"area":null},{"id":"2410.13681","title":"Ab Initio Nonparametric Variable Selection for Scalable Symbolic Regression with Large $p$","created":"2024-10-17","cited_by_count":3,"area":null},{"id":"2505.10762","title":"Deep Symbolic Optimization: Reinforcement Learning for Symbolic Mathematics","created":"2025-05-16","cited_by_count":3,"area":"deeprl"},{"id":"2506.06176","title":"SatelliteFormula: Multi-Modal Symbolic Regression from Remote Sensing Imagery for Physics Discovery","created":"2025-06-06","cited_by_count":3,"area":null},{"id":"2509.19849","title":"Analyzing Generalization in Pre-Trained Symbolic Regression","created":"2025-09-24","cited_by_count":3,"area":null}],"next_cursor":"100"}