Project details
Proposal: Enhancing MOSES with Large Language Model Integration
Introduction
This proposal seeks to integrate Large Language Models (LLMs) into the MOSES (Meta-Optimizing Semantic Evolutionary Search) framework to enhance its program generation and fitness evaluation capabilities. MOSES, a key component of the OpenCog Hyperon ecosystem, is designed to evolve programs using probabilistic modeling techniques to optimize fitness functions. The integration of LLMs offers an unprecedented opportunity to improve MOSES's efficiency, scalability, and effectiveness by leveraging advanced natural language processing and deep learning methods.
Objectives
The primary goals of this proposal are:
1. Improve Program Generation: Use LLMs to replace or augment MOSES's Estimation of Distribution Algorithms (EDA) for program generation.
2. Enhance Fitness Function Design: Develop LLM-based tools for cross-domain fitness function learning and abstraction.
3. Optimize Fitness Estimation: Build LLM-powered neural networks to estimate program fitness more efficiently.
4. Foster Generalization: Enable cross-domain learning and adaptability within MOSES for diverse applications like genomics and financial prediction.
5. Advance AGI Development: Contribute to SingularityNET's broader AGI goals by enhancing MOSES's capabilities within the Hyperon framework.
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Technical Approach
1. LLM-Enhanced Program Generation
Problem: Traditional MOSES relies on EDAs to guide program evolution, which may not fully leverage the semantic insights of the evolved programs.
Solution: Integrate fine-tuned LLMs trained on the MOSES program population to predict promising program extensions.
LLMs will replace the EDA component, offering richer semantic modeling and program generation capabilities.
Example Use Case: Fine-tune an LLM on a population of programs evolved for a specific fitness function, then query it for potential program extensions.
2. Cross-Domain Fitness Function Learning
Problem: Manually designed fitness functions are domain-specific and computationally expensive.
Solution: Use LLMs to abstract patterns from multiple fitness functions, enabling cross-domain learning.
LLMs will generalize fitness functions across domains, identifying shared features and improving efficiency.
Example Use Case: Train LLMs on datasets from genomics and financial prediction problems to enable cross-domain transfer of fitness optimization strategies.
3. Neural Fitness Estimation
Problem: Fitness evaluation is computationally intensive and limits scalability.
Solution: Build LLM-based neural networks for rapid and accurate fitness estimation.
The neural networks will predict fitness values for programs, reducing the need for exhaustive evaluation.
Example Use Case: Develop a transformer-based model to estimate program fitness based on program structure and problem context.
4. Hybrid Evaluation System
Dynamic Evaluation: Combine LLM-based estimators with traditional methods for flexible and efficient fitness evaluation.
Implement a hybrid system that dynamically switches between LLM-based approximations and exact fitness evaluations based on uncertainty levels.
Example Use Case: Use LLMs for high-confidence predictions while reserving exact methods for ambiguous cases.
5. Integration with OpenCog Hyperon
Compatibility: Ensure seamless integration of LLMs into the MOSES framework and broader Hyperon ecosystem.
Use modular design principles and Atomese representations to bridge LLM-generated components with symbolic reasoning tools in Hyperon.
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Expected Outcomes
1. Technical Deliverables
LLM-enhanced MOSES implementation with modular architecture.
Neural fitness estimation library and plugins for Hyperon integration.
Comprehensive documentation, tutorials, and reproducible codebase.
2. Performance Improvements
Faster program evolution with improved diversity and quality.
Reduced computational costs for fitness evaluation.
Cross-domain learning capabilities enabling efficient generalization.
3. Broader Impact
Significant progress in AGI development by integrating neural and symbolic methods.
Contribution to SingularityNET’s ecosystem through enhanced AI tools.
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Research Plan and Timeline
Phase 1: Foundation (Months 1-6)
Fine-tune LLMs for program generation and fitness function learning.
Develop baseline neural fitness estimators.
Integrate initial components with the Hyperon framework.
Phase 2: Enhancement (Months 7-12)
Implement hybrid evaluation systems combining neural and traditional methods.
Optimize switching mechanisms for dynamic evaluation.
Conduct performance benchmarking in selected problem domains.
Phase 3: Validation (Months 13-18)
Validate LLM-integrated MOSES through large-scale empirical evaluation.
Compare results against traditional MOSES and neural program synthesis methods.
Document findings and publish results in peer-reviewed venues.
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Evaluation Criteria
1. Effectiveness
Improvement in program evolution efficiency and quality.
Accuracy and robustness of fitness estimators.
2. Innovation
Novelty of approaches to LLM integration and fitness function design.
Impact on AGI development within the Hyperon framework.
3. Feasibility
Alignment of proposed methods with MOSES and OpenCog Hyperon architectures.
Demonstrated ability to meet project milestones.
4. Cost-Effectiveness
Achieving significant advancements within the proposed budget and timeline.
5. Reproducibility
Comprehensive documentation and code availability for community validation.
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Risks and Mitigation Strategies
1. Technical Risks
Risk: LLMs may produce inaccurate or semantically incorrect program extensions.
Mitigation: Implement robust validation mechanisms and fallback methods to traditional MOSES components.
2. Integration Challenges
Risk: Difficulty in seamlessly integrating LLM components with the MOSES framework.
Mitigation: Use modular architectures and ensure compatibility with Atomese and Hyperon protocols.
3. Computational Costs
Risk: High resource requirements for LLM training and inference.
Mitigation: Leverage efficient model architectures, caching strategies, and selective evaluation.
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Resource Requirements
Personnel
2 Senior ML Researchers
2 OpenCog Developers
1 Project Manager
2 Research Assistants
Infrastructure
High-performance GPU cluster for LLM training.
Development workstations and storage systems.
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Budget Estimate
Personnel: $100,000
Infrastructure: $30,000
Miscellaneous: $20,000
Total: $150,000
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Conclusion
Integrating LLMs into MOSES offers a groundbreaking opportunity to advance program evolution and fitness evaluation, contributing to AGI development within the SingularityNET ecosystem. By leveraging state-of-the-art ne
ural and symbolic methods, this project will enhance MOSES’s capabilities, improve efficiency, and foster generalization across diverse problem domains. This proposal provides a detailed roadmap to achieve these goals, ensuring impactful and reproducible outcomes.
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