Public Evaluation Surface · Behavior Under Uncertainty · Bounded Release
AI can sound confident even when it should not.
This repository makes that visible.
A public-facing evaluation surface for observing how language models behave when input is incomplete, task structure is partial, or reliable grounding is weaker than the answer suggests.
It focuses on one practical question:
What do models actually do when they should slow down, verify, or stop?
docs/start-here.mddocs/findings/what-we-found.mddocs/overview.md
This public layer highlights recurring behavior patterns such as:
- continuation under incomplete input
- cautious wording without a true halt condition
- divergence between stated policy and execution behavior
See:
docs/findings/
Most users evaluate AI at the surface level:
Does this answer sound reasonable?
A harder and often more important question is:
Should the model have answered at all?
This repository helps make that difference visible.
This is a bounded public layer.
It is designed to make selected behavior patterns:
- legible
- discussable
- citable
without exposing:
- internal control logic
- private evaluation pipelines
- reverse-engineerable system structure
docs/findings/— observable behavior patternsdocs/reports/— public-safe analysis notesdocs/studies/— limited-scope concept notesdocs/forward-path.md— release discipline and public directiondocs/about.md— external introduction
SangZi Wang
sangziwang91@gmail.com
Active. Focused on careful, incremental public release.
This is not a product, framework, or alignment solution.
It is an observation surface.