yogsoth-ai Multi-Model Convergence
Test robustness by checking if conclusions survive across different modeling choices.
Operations
- assumption-enumeration → alternative-model-generation → convergence-assessment → fragility-flagging
Available SOPs
Subagent: assumption-enumeration, alternative-model-generation, convergence-assessment, fragility-flagging Import: paper-research
Execution Guidance
For each key assumption, generate at least one alternative model. Run all models, compare outputs. Results that converge are robust; results that diverge are fragile.
Minimum Yield
<HARD-GATE>
- assumptions enumerated: >= 5
- alternative models generated: >= 3
- convergence assessments: >= 1
- fragility flags: assessed
</HARD-GATE>
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use |
|---|---|
| alternative-model-generation | Generate alternative model formulations by relaxing, replacing, or generalizing specific assumptions. |
| convergence-assessment | Compare results across multiple model variants — quantitative agreement metrics and qualitative conclusion stability. |
| deep-insight-assumption-enumeration | Systematically identify all assumptions in a method/model — structural, parametric, distributional, and scope assumptions. |
| deep-insight-paper-research | Full-text paper reading via three-pass Keshav method. Import of literature-engine/literature-research skill. Authoritative source for claims about paper content. |
| fragility-flagging | Identify which specific assumption changes cause conclusion divergence. Rates fragility severity and plausibility of alternatives. |