# Polymath

> Advanced AI agent framework that bridges LLMs with symbolic reasoning tools for enhanced problem-solving capabilities

Polymath is an innovative AI agent framework developed by Meta's research team that represents a significant advancement in artificial intelligence reasoning capabilities. At its core, Polymath bridges the gap between large language models (LLMs) and symbolic reasoning systems, particularly constraint solvers, to tackle complex logic and reasoning problems that have traditionally been challenging for LLMs alone.

The framework''s flagship approach, Logic.py, introduces a novel methodology for formalizing and solving search-based problems. Instead of relying solely on an LLM''s inherent reasoning capabilities, Polymath prompts the model to express problems in a logic-focused domain-specific language (DSL) called Logic.py. This formalized representation is then processed by specialized constraint solvers, effectively leveraging the complementary strengths of both technologies.

This hybrid approach has demonstrated remarkable results, achieving a 65% absolute improvement over baseline performance on the ZebraLogicBench, a challenging collection of logic grid puzzles. By reaching over 90% accuracy on these puzzles, Polymath establishes a new state-of-the-art in this domain and demonstrates how AI systems can be enhanced through the strategic combination of neural and symbolic methods.

Polymath exemplifies the growing trend of augmenting foundation models with specialized tools to overcome their limitations in areas requiring precise logical reasoning. The system is particularly effective for problems involving constraint satisfaction, such as scheduling, resource allocation, and logical deduction tasks that benefit from formal representation and systematic solutions.

The architecture is designed to be extensible, allowing for additional reasoning tools beyond constraint solvers to be integrated into the framework. This modular approach enables Polymath to potentially address a wide range of problem domains that require structured reasoning, from formal mathematics to planning and decision-making tasks.

As an open-source project, Polymath provides researchers and developers with access to both the framework and the underlying implementation of Logic.py, enabling further exploration and advancement of hybrid AI reasoning systems. While primarily positioned as a research tool, the technology demonstrates promising applications in areas where both natural language understanding and rigorous logical reasoning are required.

Meta''s Polymath represents an important contribution to the field of AI reasoning, showcasing how carefully designed integration between neural and symbolic approaches can lead to AI systems with enhanced capabilities for solving complex reasoning tasks that neither approach could handle effectively on its own.

## Features
- Logic.py domain-specific language for problem formalization
- Integration with constraint solvers for enhanced reasoning
- Hybrid neural-symbolic architecture
- State-of-the-art performance on logic grid puzzles
- Support for complex reasoning tasks
- Modular design for extensibility
- Open-source implementation
- Compatible with various LLM backends
- Benchmark evaluation framework
- Specialized symbolic reasoning tools

## Integrations
Python, Constraint solvers, LLM APIs, Hugging Face, Research benchmarks, ZebraLogicBench, FOLIO dataset

## Platforms
WINDOWS, MACOS, LINUX

## Pricing
Free

## Version
1.0

## Links
- Website: https://ai.meta.com/research/publications/logic-py-bridging-the-gap-between-llms-and-constraint-solvers/
- Repository: https://github.com/facebookresearch/polymath
- EveryDev.ai: https://www.everydev.ai/tools/polymath