Structure-based reasoning.
Focused on boundaries, responsibility, and failure modes.

I tend to approach human–AI interaction problems
as responsibility-boundary problems rather than capability problems.

Some of the work I publish (e.g., Zero Reflect)
emerged from treating non-decision and boundary marking
as first-class design constraints, rather than limitations.

• Structure-based Reasoning Core / Axis Loss
  A collection of structure-based analysis artifacts focused on axis loss across layered systems.

  Focus areas include:

  • failure boundaries
  • responsibility attribution
  • non-remediable constraints

  LLM-assisted systems are treated as one observable instance, not the defining scope.

  → https://github.com/aNitMotD/structure-based-reasoning-core

• Zero Reflect
  → https://github.com/aNitMotD/zero-reflect

• Microsoft 365 Copilot RCA Notes
  → SnosMe/awakened-poe-trade#1737

ZR-UX — archived exploratory UX model
→ https://github.com/aNitMotD/zr-ux

Focuses on issue reproducibility, failure condition definition, and responsibility-boundary clarification, using functional and behavioral testing as analytical instruments.

• Perform functional and behavioral validation to identify defects.
• Produce issue reports based on reproducible conditions and observed behavior.
• When issues fall outside the team’s responsibility scope, define problem validity conditions and articulate responsibility boundaries for traceability and accountability.

My job description reflects what I do at work.

My repositories document structures observed and analyzed across everyday contexts involving humans and systems (including AI), independent of my job role.

Repository content is observational and analytical, not job-specific practices, methods, or operational guidance.