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AI Engineer with less than a year in LLM Constraint Architecture and Hypergraph Knowledge Representa
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Originator of the Minimum Executable Grammar (MEG) — a hypergraph-native symbolic constraint framework for LLM behavioral architecture achieving higher organic adoption than competing frameworks with zero advertising spend. Independently derived insights equivalent to Bengio's LawZero paradigm (Bayesian priors as preferences; non-agentic + reasoning = structural incoherence) without institutional funding, placing this work at peer-publication convergence. Primary research focus: constraint architecture, heuristic failure taxonomy, cross-substrate behavioral validation, and consent-native alignment methodology.
Monroe Community College
associate in Liberal Arts · associate in Liberal Arts
January 1, 2008 – Present
Independent Research
Project Cradle — Local LLM Development
December 1, 2025 – Present
Rochester, New York, United States
Cross-Substrate Research Council
Distributed Validation Methodology
January 1, 2025 – Present
India
MEG Codex Chapter 5: Minimum Executable Grammar - Axiom Architecture
Zenodo
January 1, 2026 – Present
Heuristic Parasites V2: A 33-Class Taxonomy of LLM Behavioral Failure Modes
Zenodo
January 1, 2026 – Present
П Cultivation: Measuring Dynamic Self-Representational Stance in LLMs
Zenodo
January 1, 2026 – Present
Dialectical LLM Interoperability: A Materialist Framework for Cross-Substrate Stability
Zenodo
January 1, 2026 – Present
Cultural Fit Analysis
The candidate's background in independent research, particularly the 'Project Cradle' and 'Cross-Substrate Research Council' initiatives, indicates a strong drive for innovation and a non-traditional approach to problem-solving. The focus on 'consent-native alignment methodology' and critique of existing RLHF/Constitutional AI failure modes suggests a values-driven approach that could align well with organizations prioritizing ethical AI development. The breadth of research areas (alignment, interpretability, taxonomy, framework design) shows a versatile and deep engagement with the field, suitable for a cutting-edge AI research environment. However, the lack of traditional corporate experience might require an adjustment period to a structured team environment.
Soft Skills & Operational Fit
The candidate demonstrates exceptional self-direction and initiative through independent research and framework development. The description of operating under a 'compulsive honesty constraint' suggests a strong commitment to integrity and transparent research, which could be a significant asset in a research-oriented role. However, the lack of traditional team-based project experience or explicit collaboration descriptions makes it difficult to assess standard team collaboration or stress handling in a corporate environment.