Designing structures for stable human–AI collaboration
Most discussions about AI focus on making models smarter.
My work explores something different:
How to stabilize long-form collaboration with AI.
A structural framework for maintaining reference continuity during long AI conversations.
Instead of treating conversations as a linear timeline,
BRM models interaction as a network of references.
→ BRM Repository
https://github.com/continuity-model/branching-reference-model
⭐ If you're interested in stable AI collaboration, consider starring the repository.
Long AI conversations often collapse due to structural issues:
- role drift
- shallow reasoning
- context instability
- hallucinated references
Most solutions try to improve prompts.
BRM explores a different direction:
structural control of references.
A layered model for structured AI collaboration.
The stack separates:
- reference structure
- reasoning discipline
- task behavior
Operational governance layers designed to stabilize AI collaboration.
Examples include:
- Writing Stable
- Review Stable
- Debug Stable
- Research Stable
- Spec Stable
- Legal Stable
- Symptom Stable
These are behavioral governance layers, not prompt templates.
Zenn
https://zenn.dev/continuitymodel
Gumroad
https://brmmodel.gumroad.com/