An LLM multi-agent framework decomposes differential-algebraic model discovery into parallel structure search and algebraic closure, recovering state dynamics and constraints from data and outperforming single-agent LLM and symbolic regression baselines on generator and inverter cases.
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LLM-SR: Scientific Equation Discovery via Programming with Large Language Models
20 Pith papers cite this work. Polarity classification is still indexing.
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ASYS recovers known analytical PDE forms and generates new interpretable symbolic approximations, such as a geometric interface formula for 2D Allen-Cahn and a nine-parameter contraction law for Keller-Segel blow-up, via agent-guided evolutionary search on differentiable programs.
FISolver trains a compact LLM on backward-generated (differential equation, first integral) pairs and uses guided reinforcement learning to outperform larger models and Mathematica on first-integral benchmarks at lower cost.
SMCEvolve applies Sequential Monte Carlo sampling to LLM program search with adaptive resampling, mutation mixtures, and convergence control, delivering finite-sample complexity bounds and benchmark gains over prior systems.
Latent Grammar Flow discovers ODEs by placing grammar-based equation representations in a discrete latent space, using a behavioral loss to cluster similar equations, and sampling via a discrete flow model guided by data fit and constraints.
Retrieval-augmented LLMs produce more cautious and guideline-aligned recommendations on cannabidiol for older adults than standalone models, demonstrated via automated evaluation on 64 diverse scenarios.
Programmatic context augmentation lets LLM-based symbolic regression perform code-driven data analysis during search, yielding superior efficiency and accuracy over baselines on LLM-SRBench.
An LLM agent autonomously selects, codes, and validates materials equations from data, recovering known laws reliably but requiring checks for new or specialized cases.
FELA deploys specialized LLM agents in an evolutionary framework to generate, validate, and refine explainable features from heterogeneous industrial event logs, improving downstream model performance.
LLM-FE is a framework that treats feature engineering as LLM-driven program search with data feedback, reporting consistent gains over baselines on classification and regression tabular tasks.
MCO-PDE trains per-dataset neural surrogates, applies soft-competitive weighting for consensus coefficients, and uses a genetic algorithm to identify shared PDE structures from multi-source data.
EvoGens uses rank-based mutation, semantic-aware crossover, and lightweight evaluation to evolve populations of LLM-generated scientific ideas, boosting novelty and diversity metrics.
GPU fitness evaluation for GP-GOMEA boosts throughput, improves benchmark results especially on large datasets, and allows reliable regression of large Feynman equations within hours.
PACEvolve++ uses a phase-adaptive reinforcement learning advisor to decouple hypothesis selection from execution in LLM-driven evolutionary search, delivering faster convergence than prior frameworks on load balancing, recommendation, and protein tasks.
GPT-4 models rediscover Langmuir isotherms and produce fits on Nikuradse pipe-flow data via iterative chain-of-thought prompting with scientific context and external code feedback.
MOOSE-Copilot introduces a unified HAII framework and no-code web interface for LLM-driven scientific hypothesis discovery that integrates exploratory search with fine-grained refinement via user-provided blueprints, routing, and feedback.
Proposes dynamics-based analysis of time series models showing partial dynamics learning and end-positioning as key to performance, plus a plug-and-play improvement method.
The paper surveys reinforced reasoning techniques for LLMs, covering automated data construction, learning-to-reason methods, and test-time scaling as steps toward Large Reasoning Models.
Position paper claims multimodal LLMs can significantly advance scientific reasoning and proposes a four-stage roadmap plus challenges and suggestions.
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In Context Learning and Reasoning for Symbolic Regression with Large Language Models
GPT-4 models rediscover Langmuir isotherms and produce fits on Nikuradse pipe-flow data via iterative chain-of-thought prompting with scientific context and external code feedback.