Lean_State_Search_Random
| Property | Value |
|---|---|
| Author | ruc-ai4math |
| Paper | arXiv:2501.13959 |
| Repository | HuggingFace |
What is Lean_State_Search_Random?
Lean_State_Search_Random is a specialized model designed for premise retrieval in the Lean theorem proving system. It leverages state representation to enhance mathematical formalization processes and implements a learning-based approach for identifying relevant premises during proof construction.
Implementation Details
The model architecture consists of two main components: pre-trained models for retrieval (410_stable_random) and reranking (410_stable_random_1024) tasks. These models are specifically optimized for mathematical theorem proving contexts and are available in separate directories for different use cases.
- Pre-trained base model for retrieval fine-tuning (410_stable_random)
- Extended model for reranking tasks (410_stable_random_1024)
- Fine-tuned variants for specific retrieval and reranking applications
Core Capabilities
- Efficient premise retrieval in Lean theorem proving
- State-based representation learning
- Dual-purpose functionality: initial retrieval and result reranking
- Specialized mathematical formalization assistance
Frequently Asked Questions
Q: What makes this model unique?
The model's uniqueness lies in its specialized focus on mathematical premise retrieval using state representation in Lean. It combines both retrieval and reranking capabilities, making it a comprehensive solution for theorem proving assistance.
Q: What are the recommended use cases?
This model is primarily designed for researchers and developers working with the Lean theorem prover, particularly those seeking to automate or enhance the premise selection process in mathematical formalization tasks.
