aasavravi1234
Project OwnerIntegration of PLN-Atomspace and LLMs, overseeing design, implementation, validation to memory & reasoning systems. Benchmarks show scalability, adaptability, 20% improvement accuracy, efficiency.
Our proposal explores using Probabilistic Logic Networks (PLN) to guide Large Language Models (LLMs), enhancing memory and reasoning capabilities. By leveraging PLN’s probabilistic reasoning, we aim to augment Atomspace knowledge graphs through batch-mode enrichment or real-time queries, providing an innovative alternative to graphRAG. This project focuses on improving retrieval accuracy, reasoning adaptability, and scalability, showcasing PLN’s potential to revolutionize LLM functionality and applicability.
This RFP seeks proposals to explore how Probabilistic Logic Networks (PLN) can be used to provide guidance to LLMs. We are particularly interested in applying PLN to develop an alternative to graphRAG for augmenting LLM memory using Atomspace knowledge graphs.
In order to protect this proposal from being copied, all details are hidden until the end of the submission period. Please come back later to see all details.
Develop the foundational integration of Probabilistic Logic Networks (PLN) with Atomspace knowledge graphs. This includes implementing PLN for enriching graph structures with probabilistic links, ensuring compatibility with LLMs, and setting up initial APIs for communication.
Functional PLN integrated with Atomspace. Initial enriched knowledge graph with probabilistic weights. API enabling basic interaction between LLMs and PLN-enhanced graphs.
$30,000 USD
Successful integration of PLN into Atomspace with initial graph enrichment demonstrated through enhanced memory retrieval tests. LLMs can access enriched knowledge graph via the API.
Develop real-time PLN querying capabilities for dynamic reasoning in Atomspace. Focus on enabling LLMs to perform adaptive, context-aware reasoning using updated knowledge graphs.
Real-time PLN querying system. Demonstration of dynamic reasoning with LLMs in test scenarios. Documentation for setting up and extending real-time queries.
$30,000 USD
Demonstrated ability of LLMs to reason dynamically using real-time PLN queries, achieving higher accuracy and relevance in decision-making tasks compared to baseline methods.
Conduct extensive testing and benchmarking of PLN-enhanced LLM systems. Validate improvements in memory retrieval, reasoning depth, and scalability against traditional graphRAG systems.
Performance evaluation reports comparing PLN systems with graphRAG. Reproducible codebase and detailed documentation. Research publication detailing findings and methodologies.
$20,000 USD
Validation shows at least a 20% improvement in reasoning accuracy and memory retrieval efficiency. Benchmarks demonstrate scalability and adaptability across diverse domains. Results are published and independently verified.
Reviews & Ratings
Please create account or login to write a review and rate.
Check back later by refreshing the page.
© 2025 Deep Funding
Join the Discussion (0)
Please create account or login to post comments.