Flexion Dynamics is a unified operator-based framework for modeling stability, divergence, threshold behavior, and collapse in advanced systems relevant to long-term AI safety. The project aims to build the first simulation environment that uses bidirectional deviation operators (contractive and expansive dynamics) to identify failure modes, runaway behaviors, early-warning instability patterns, and structural points of no return in AI-related systems. This work provides a foundational tool for understanding systemic risks in complex intelligent systems.
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## Goals and Path to Impact
### Project goals:
- Build a simulation engine that models system deviation using Flexionization (stabilizing) and Deflexionization (destabilizing) operators.
- Implement threshold-triggered instability and collapse detection.
- Generate empirical failure scenarios relevant to advanced AI systems.
- Integrate a simple AI-loop demonstrator showing runaway dynamics and recovery pathways.
- Publish all results and simulations open-access as part of Flexion Dynamics V1.2.
### Path to impact:
This work contributes directly to technical AI safety by:
- Identifying failure dynamics that current models cannot express.
- Providing a robust mathematical and empirical basis for analyzing collapse trajectories.
- Giving researchers tools for early-warning detection, risk profiling, and stability testing for high-capability systems.
- Opening a new class of operator-based control and monitoring methods.
Success will be measured by a functioning simulation environment, documented collapse scenarios, demonstrator outputs, and the full release of Flexion Dynamics V1.2.
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## How the Funding Will Be Used
Funding supports a 30-day full-time research cycle:
- 55% — researcher stipend (including self-employment tax).
- 25% — compute resources (cloud GPU, simulation workloads).
- 15% — hardware for physical demonstrators (microcontrollers, sensors, robotic components).
- 5% — software, tooling, and documentation infrastructure.
Total project window: 30 days.
Outputs: complete simulation engine, empirical results, collapse datasets, updated theory.
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## Team and Track Record
Researcher: Maryan Bogdanov (independent).
Track record includes:
- Flexionization Theory V1.5 — DOI: https://doi.org/10.5281/zenodo.17618947
- Deflexionization V1.0 — DOI: https://doi.org/10.5281/zenodo.17637758
- Flexion-Immune-Model V1.1 — DOI: https://doi.org/10.5281/zenodo.17624206
- FRE Risk Engine V1.1 — DOI: https://doi.org/10.5281/zenodo.17628118
- FCS nonlinear control system (open access on GitHub)
- Fully open-source implementation ecosystem: https://github.com/MaryanBog
I have independently produced multiple integrated theoretical and applied works, including complete codebases, mathematical documents, demonstrators, and publications. All prior work was conducted independently with $0 external funding.
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## Failure Modes and Their Outcomes
Most likely failure modes:
- Underpowered compute → slower simulations
- Hardware delivery delays → slower demonstrator testing
- Simulation complexity may require additional time for polishing
None of these lead to catastrophic project failure; at minimum, a functional core simulation and documentation will still be delivered.
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## Previous Funding (Last 12 Months)
Raised: $0 external funding (self-funded).
Other applications: EA Long-Term Future Fund (submitted, decision pending).
No other grants or institutional support.