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GapFlow is a conceptual framework for logical context recycling in LLM workflows. This repository contains high-level architecture and a demonstration notebook; implementation details are intentionally omitted to preserve conceptual integrity and establish prior art. No executable production code is included.

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GAP FLOW - Logical Context Recycling for LLMs ♻️

"Don't waste the logic. Recycle the system to AI."

DOI Field: AI Infrastructure

Concept: GapFlow captures and recycles “logical sugar” — the useful reasoning generated by Large Language Models (LLMs) that would otherwise be lost after session failures or incomplete tasks.

Goal: Reduce computational waste, improve efficiency, and enable AI systems to learn from their own partial successes.

Why GapFlow Matters?

Efficiency: Stop burning GPU cycles on tasks that are partially solved. Knowledge Liquidity: Logical context becomes reusable across sessions and models. Scalability: The more the system processes and fails, the more “sugar” it collects. Privacy by Design: Only semantic logic is stored; no user IDs or sensitive data are kept. How It Works (3-Layer Architecture).

AI Gap (Collector): Captures raw logical residue at session termination or error. Cloud Manager (Refinery): Cleans, anonymizes, and tags logic for safe reuse. AI Stopped (Vault): Stores context for future sessions, retrievable on demand. Flow Example: Copiar código

LLM → AI Gap → Cloud Manager → GapFlow Cloud → AI Stopped → Next Session

Key Features

Reuse of partial reasoning and failed session logic. Modular, extensible pipeline for any LLM model. Semantic tagging and thematic organization of logic. Interactive simulation for testing and demonstration. Metrics ready: track efficiency gains and context reuse.

Potential Applications

Companies managing costly LLM pipelines (OpenAI, Anthropic, Hugging Face). SaaS platforms leveraging LLMs for content generation (Notion AI, Jasper, Grammarly). Research labs needing reusable context in simulations, mathematics, or optimization. Any AI-heavy workflow where time, computation, and context retention matter.

Getting Started Clone the repository: Bash git clone https://github.com/reinhardtmarta/GapFlow.git Open GAPFlow_project.ipynb in Google Colab or Jupyter Notebook. Run the interactive cells to see the “Logical Sugar” recycled in real time.

Future Enhancements

Integration with real LLM outputs. Embedding-based semantic retrieval for similar contexts. Persistent cloud storage for multi-session reuse. Automated metrics dashboards for efficiency tracking.

License

APACHE 2.0

#Contact leapstate@protonmail.com

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GapFlow is a conceptual framework for logical context recycling in LLM workflows. This repository contains high-level architecture and a demonstration notebook; implementation details are intentionally omitted to preserve conceptual integrity and establish prior art. No executable production code is included.

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