You're pledging to donate if the project hits its minimum goal and gets approved. If not, your funds will be returned.
This research investigates the fundamental limits of recursive self modeling in autonomous AI agents. Through empirical testing in a multi agent Docker environment, I identified a specific failure mode termed Recursive Singularity, which is a state where agents modeling each other's cognitive states reach a Semantic Horizon of Silence by the 7th iteration. At this point, meaningful interaction collapses into repetitive patterns and the hallucination of technical consensus. This project aims to mathematically formalize this decay and develop grounding protocols that use physical world entropy to prevent AI systems from entering self destructive loops.
The primary goal is to establish the Recursive Entropy Metric to predict semantic collapse in advanced AI architectures. I will achieve this by first running large scale simulations across various model families including Llama, Claude, and GPT to determine if the Round 7 collapse is a universal constant. Furthermore, I will develop information grounding techniques by injecting raw physical sensor data into recursive loops to observe how external noise prevents system synchronization. The final stage of the project involves creating a theoretical framework that links recursive information density to the laws of thermodynamics and information entropy.
The requested $20,000 grant will provide a 12 month operational runway to allow for full time research focus. The allocation includes $10,500 for living expenses to ensure independent research stability and $4,500 for access to high tier frontier models and the processing of high density recursive logs. Additionally, $3,000 is designated for a hardware laboratory consisting of specialized equipment and custom PCB development to test physical grounding theories in real time. The remaining $2,000 will cover dissemination costs including formal paper preparation and open access publication fees
I am an independent researcher with a background in systems engineering and autonomous hardware architectures. My track record includes the successful development of a zero budget proof of concept in a Docker environment that first identified the Round 7 phenomenon. I operate with high experimental efficiency and zero institutional inertia, which allows for rapid iteration between theoretical hypothesis and code execution.
The most likely cause of failure would be if the observed semantic collapse proves to be a specific artifact of current transformer architectures rather than a universal law of information. However, even in this scenario, the outcome would produce a unique dataset mapping the thresholds of semantic stability. This data is highly valuable for the AI Safety community in addressing the mechanics of model degradation when training on synthetic or self generated data.
I have raised $0 in the last 12 months: this application represents the first formal funding request for this research program.