Records the preprint’s scalability, approximation, calibration, hyperparameter, initial-resource, and limited-action concerns along with broader questions about robustness and transfer.
Research documentation: this page interprets a cited research source and defines evidence requirements. It does not claim a released Teleodynamic AI implementation.
Source-paper limitations
- Diagonal Fisher approximation.
- Scalability to high-dimensional and multiclass data.
- Hyperparameter sensitivity.
- Dependence on initial energy.
- Tropical-inference calibration.
- Limited structural action vocabulary.
Additional engineering questions
- How robust is phase detection to noise and window choice?
- Can resource rewards be gamed?
- How should persistent local adaptation be rolled back?
- How is catastrophic structure loss recovered?
- Does the approach transfer beyond small tabular rule-learning tasks?
- Can independent implementations reproduce the reported results?
Scope
This starter page defines the questions, boundaries, evidence, and failure modes that should be recorded before a capability is presented as supported.
Engineering considerations
- Identify the source, version, target environment, and owner.
- Separate observed values from estimates and externally reported values.
- Record trade-offs, unsupported cases, and fallback behavior.
- Link performance statements to a compatible benchmark methodology.
Verification questions
- What exact artifact, revision, backend, and environment were reviewed?
- Which assumptions could change the result?
- Which data should be retained so another engineer can reproduce the conclusion?