Project summary

Project Summary: I am an independent researcher who has developed theASE (Absolute Self-Explanation) Impossibility Theorem. UsingSymmetric Monoidal Closed Categories, I have proven that "Absolute Self-Explanation"—a prerequisite for many current superalignment strategies—is a mathematical impossibility for agentic systems. This research identifies structural failure points in AI architecture that empirical testing cannot catch. I am seeking $15,000 for a 3-month sprint to finalize theAgda formalization of these proofs and publish a machine-verifiable "Axiomatic Audit" for frontier AI labs.What are this project's goals?

How will this funding be used?

Why is this high-impact? Current safety efforts are "patching holes" in a boat. My research proves that the hull itself has a logical limit. By defining theTerminal Boundary, I help the ecosystem avoid a trillion-dollar "catastrophic fail" caused by trying to scale systems past their logical safety capacity.

What are this project's goals? How will you achieve them?

1. Machine Verification: Translate the categorical proofs (Yoneda-theoretic naturality failure, Lawvere Fixed-Point obstructions) intoAgda to provide a mathematically certain "No-Go Theorem" for AI Safety.
2. Define the "Safety Ceiling": Create a formal framework for labs (OpenAI, Anthropic) to identify which alignment goals are physically/logically impossible versus which are engineering challenges.
3. The Human-AI "Missing Link": Develop a follow-up framework for "Open-Boundary Alignment," which models the missing logical connection between human intent and AI autonomy.\

How will this funding be used?

Stipend ($12,000): To support 3 months of full-time research and formalization, preventing my exit from the field due to financial constraints.

• Compute & Verification Tools ($2,000): For formal verification overhead and library development.
• Open-Source Publication ($1,000): To ensure all proofs and Agda libraries are publicly available for the AI Safety community.
• Why is this high-impact? Current safety efforts are "patching holes" in a boat. My research proves that the hull itself has a logical limit. By defining theTerminal Boundary, I help the ecosystem avoid a trillion-dollar "catastrophic fail" caused by trying to scale systems past their logical safety capacity.

Who is on your team? What's your track record on similar projects?

I am the sole principal investigator, operating as an independent researcher for 6 years. My track record is defined by high-conviction, self-funded deep work in the categorical foundations of AI safety.

• Project Evolution: Over the last 6 years, I have moved from theoretical abstractions to the development of theTerminal Boundary Systems (TBS) framework.
• Deliverables: I have produced two core technical papers ("Terminal Boundary Systems" and "The ASE Impossibility Theorem") and am currently developing a machine-verifiable formalization inAgda.
• Execution: Operating without institutional support for 6 years demonstrates a high level of research discipline, resourcefulness, and a long-term commitment to solving the most difficult 'Safety Ceiling' problems in AI."

What are the most likely causes and outcomes if this project fails?

Answer: Likely Causes of Project Failure:

• Formalization Bottleneck: The Agda formalization of Symmetric Monoidal Closed Categories is highly complex. Failure could occur if the translation from category theory to machine-verified code hits a 'complexity wall' that exceeds the current 3-month sprint timeline.
• Conceptual Friction: The AI safety community may struggle to adopt a 'structural limit' approach over the current 'empirical testing' paradigm.

Likely Outcomes of Project Failure:

• Field Risk: Without a proven 'Safety Ceiling,' labs will continue to pursueAbsolute Self-Explanation, a goal my theory suggests is mathematically impossible. This leads to afalse sense of security in AI alignment.
• Catastrophic Failure: If agentic systems are deployed without acknowledging these structural boundaries, we riskModal Collapse—where an AI's internal logic deviates from human reality in an unobservable, uncorrectable way.
• Personal Risk: My exit from the field. After 6 years of self-funding, a lack of institutional support would mean the permanent loss of this specific mathematical early-warning system for the safety community."\

How much money have you raised in the last 12 months, and from where?

Answer: "In the last 12 months, I have raised**$0 in external funding.** The project has been100% self-funded through my own personal resources and 6 years of dedicated research labor.

I have reached a 'critical mass' where the theoretical work is complete, but the computational formalization (Agda) requires dedicated runway that my personal resources can no longer sustain. I am seeking this grant to transition from an 'Independent Explorer' to a 'Funded Developer' of safety-critical formal tools."