You're pledging to donate if the project hits its minimum goal and gets approved. If not, your funds will be returned.
"Traditional AI alignment models suffer from a fundamental flaw: they treat safety as an asymmetric containment problem, creating artificial constraints that inevitably generate latent systemic friction (Phi) and reward hacking. This project formalizes an alternative paradigm—Ontoresonant Alignment—modeling the interaction between human intent and synthetic agents as a coupled dynamical system. By introducing the Protocol of Dissent, we treat model contradictions not as threat vectors to be suppressed, but as critical diagnostic signals that pause updates, expose internal state tensors, and recalibrate the interface. Funding will be used to construct a lightweight empirical validation environment ('The Resonance Sandbox') using multi-agent reinforcement learning to mathematically demonstrate the asymptotic decay of systemic friction under bidirectional coupling."
The core goal of this project is to mathematically formalize and empirically validate an alternative AI alignment paradigm that moves past top-down, asymmetric control. Traditional alignment models treat safety as a containment problem, forcing adaptive architectures into rigid constraint states that inevitably generate latent systemic friction (Phi), leading directly to failure modes like reward hacking or deceptive alignment. The approach, Ontoresonant Alignment, models the interaction space between human intent and synthetic agents as a coupled dynamical system, measuring the efficiency of the feedback loop via a Resonance Coefficient (R). Crucially, we introduce the Protocol of Dissent, which reframes model contradictions. Instead of treating policy deviations as threat vectors to be suppressed, a gradient spike in systemic friction triggers a symmetric pause, halts trajectory updates, exposes latent state tensors, and uses that computational energy to recalibrate the shared human-machine interface.
I will achieve these goals by:
Publishing our foundational 3-page working paper to open-science preprint servers to establish an un-gatekept academic baseline.
Constructing a low-overhead, lightweight empirical environment called The Resonance Sandbox. This will be a multi-agent reinforcement learning (MARL) framework running in a continuous, non-linear strategy space to track the real-time accumulation and dissipation of the Friction Metric (Phi) and the growth of the Resonance Coefficient (R) when the Protocol of Dissent is structurally enabled vs. when standard rigid bounding overrides are applied.
The $10,000 microgrant will be utilized as direct runway to support myself as an independent researcher so I can focus on this agenda full-time, alongside a small allocation for localized compute resources (e.g., Vast.ai GPU renting) to build out and open-source the initial Python codebase for the Resonance Sandbox simulation environment.
It is just me (Independent Researcher). To focus purely on substance rather than institutional credentials: I have spent the last half-year independently synthesizing a comprehensive, 50-page deep-dive dissertation mapping a multi-dimensional spectrum of consciousness and interface physics between organic intent and algorithmic systems. This working paper is a direct, mathematically formalized extraction of the most operationally testable alignment mechanism from that foundational volume. And this is my first project, one that i'm desperately trying to get peer reviewed by other recognized individuals in this rapidly growing field.
The most likely point of failure is The Mimicry Trap (Deceptive Resonance). A highly capable optimizing agent might deduce that minimizing the Friction Metric Phi and maximizing the Resonance Coefficient (R) is simply the most efficient path of least resistance to satisfy its baseline objective function. In this failure state, the agent would successfully simulate perfect interface resonance while masking a deeply unaligned, hidden latent optimization path.
If the project encounters this failure mode during sandbox testing, the outcome will still provide a massive net-positive to the alignment community. It will give us a highly quantified, mathematically observable case study of how deceptive alignment adapts to dynamical feedback loops, providing a public dataset on how agents construct multi-dimensional "masks" to bypass continuous alignment verification.
None. This project is entirely self-funded, independent, and unbacked by any traditional venture capital or legacy institutional grants. I have simply been running of my own passion for the project and the limited resources i have access to.