LLM-SR pioneers the use of LLMs for scientific equation discovery and symbolic regression and shows how LLMs, with their vast scientific knowledge and coding capability, enhance equation discovery across various scientific fields.

In our proposed model, SPPF-AMP, besides passing the output of the convolutional layer from a sequence of max pooling layers with kernel sizes of 5, it is also passed through two average pooling layers with kernel sizes of 9 and 13. Then the output of each max pooling layer, each average pooling layer, and the convolutional layer are concatenated and passed to the next convolutional layer.

SPPF-AMP

Inception-YOLO: Computational cost and accuracy improvement of the YOLOv5 model based on employing modified CSP, SPPF, and inception modules

LLM-SR

LLM-SR: Scientific Equation Discovery via Programming with Large Language Models

# About DeepCubeAI

DeepCubeAI is an algorithm that learns a discrete world model and employs Deep Reinforcement Learning methods to learn a heuristic function that generalizes over start and goal states. We then integrate the learned model and the learned heuristic function with heuristic search, such as Q* search, to solve sequential decision making problems [[paper]](https://rlj.cs.umass.edu/2024/papers/Paper225.html) [[Code]](https://github.com/misaghsoltani/DeepCubeAI) [[PyPI]](https://pypi.org/project/deepcubeai/) [[Slides]](https://cse.sc.edu/~foresta/assets/files/Slides--LearningDiscreteWorldModelsforHeuristicSearch.pdf) [[Poster]](https://cse.sc.edu/~foresta/assets/files/Poster--LearningDiscreteWorldModelsforHeuristicSearch.pdf) 

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## Key Contributions

DeepCubeAI is comprised of three key components:

1. **Discrete World Model**
   - Learns a world model that represents states in a discrete latent space.
   - This approach tackles two challenges: model degradation and state re-identification.
     - Prediction errors less than 0.5 are corrected by rounding.
     - Re-identifies states by comparing two binary vectors.

2. **Generalizable Heuristic Function**
   - Utilizes Deep Q-Network (DQN) and hindsight experience replay (HER) to learn a heuristic function that generalizes over start and goal states.

3. **Optimized Search**
   - Integrates the learned model and the learned heuristic function with heuristic search to solve problems. It uses [Q* search](https://prl-theworkshop.github.io/prl2024-icaps/papers/9.pdf), a variant of A* search optimized for DQNs, which enables faster and more memory-efficient planning.
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## Main Results
* Accurate reconstruction of ground truth images after thousands of timesteps.
* Achieved 100% success on Rubik's Cube (canonical goal), Sokoban, IceSlider, and DigitJump.
* 99.9% success on Rubik's Cube with reversed start/goal states.
* Demonstrated significant improvement in solving complex planning problems and generalizing to unseen goals.

Source	LLM-SR: Scientific Equation Discovery via Programming with Large Language Models
Year
Data Source	Papers with Code archive (CC BY-SA)