--- license: mit language: en pipeline_tag: text-generation tags: - video-understanding - narrative-generation - generative-ai - multi-agent - stateful-ai - prompt-engineering - found-protocol - creator-economy - data-sovereignty - web3 base_model: - google/gemini-pro-vision - google/gemini-pro datasets: - FOUND-LABS/found_consciousness_log ---
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The FOUND Protocol

The Open-Source Engine for the Consciousness Economy

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--- ## Abstract Current video understanding models excel at semantic labeling but fail to capture the pragmatic and thematic progression of visual narratives. We introduce **FOUND (Forensic Observer and Unified Narrative Deducer)**, a novel, stateful architecture that demonstrates the ability to extract coherent emotional and thematic arcs from a sequence of disparate video inputs. This protocol serves as the foundational engine for the **[FOUND Platform](https://foundprotocol.xyz)**, a decentralized creator economy where individuals can own, control, and monetize their authentic human experiences as valuable AI training data. --- ## From Open-Source Research to a New Economy The FOUND Protocol is more than an academic exercise; it is the core technology powering a new paradigm for the creator economy. - **The Problem:** AI companies harvest your data to train their models, reaping all the rewards. You, the creator of the data, get nothing. - **Our Solution:** The FOUND Protocol transforms your raw visual moments into structured, high-value data assets. Our upcoming **FOUND Platform** will allow you to contribute this data, maintain ownership via your own wallet, and earn from its usage by AI companies. **This open-source model is the proof. The FOUND Platform is the promise.** --- ## Model Architecture The FOUND Protocol is a composite **inference pipeline** designed to simulate a stateful consciousness. It comprises two specialized agents that interact in a continuous feedback loop: - **The Perceptor (`/dev/eye`):** A forensic analysis model (FOUND-1) responsible for transpiling raw visual data into a structured, symbolic JSON output. - **The Interpreter (`/dev/mind`):** A contextual state model (FOUND-2) that operates on the structured output of the Perceptor and the historical system log to resolve "errors" into emotional or thematic concepts. - **The Narrative State Manager:** A stateful object that maintains the "long-term memory" of the system, allowing its interpretations to evolve. --- ## How to Use This Pipeline ### 1. Setup Clone this repository and install the required dependencies into a Python virtual environment. ```bash git clone https://huggingface.co/FOUND-LABS/found_protocol cd found_protocol python3 -m venv venv source venv/bin/activate pip install -r requirements.txt ``` ### 2. Configuration Set your Google Gemini API key as an environment variable (e.g., in a .env file): ``` GEMINI_API_KEY="your-api-key-goes-here" ``` ### 3. Usage via CLI Analyze all videos in a directory sequentially: ```bash python main.py path/to/your/video_directory/ ``` ## Future Development: The Path to the Platform This open-source protocol is the first step in our public roadmap. The data it generates is the key to our future. - **Dataset Growth:** We are using this protocol to build the found_consciousness_log, the world's first open dataset for thematic video understanding. - **Model Sovereignty:** This dataset will be used to fine-tune our own open-source models (found-perceptor-v1 and found-interpreter-v1), removing the dependency on external APIs and creating a fully community-owned intelligence layer. - **Platform Launch:** These sovereign models will become the core engine of the FOUND Platform, allowing for decentralized, low-cost data processing at scale. ➡️ Follow our journey and join the waitlist at foundprotocol.xyz ## Citing this Work If you use the FOUND Protocol in your research, please use the following BibTeX entry. ```bibtex @misc{found_protocol_2025, author = {FOUND LABS Community}, title = {FOUND Protocol: A Symbiotic Dual-Agent Architecture for the Consciousness Economy}, year = {2025}, publisher = {Hugging Face}, journal = {Hugging Face repository}, howpublished = {\url{https://huggingface.co/FOUND-LABS/found_protocol}} } ```