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weight-perturbation

Testing & Quality
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SOP: Perturb weights to test gap-ranking stability, output a stability verdict

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Source SKILL.md: https://github.com/yogsoth-ai/de-anthropocentric-research-engine/blob/HEAD/skills/weight-perturbation/SKILL.md

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Weight Perturbation

Perturb weights to test gap-ranking stability, output a stability verdict.

HARD-GATE

Pipeline

  1. Precheck: Verify the weight vector is normalized; verify the scoring matrix dimensions match the weight vector length
  2. Baseline ranking computation: Weight-sum the scoring matrix with the baseline weights to obtain the baseline ranking
  3. Perturbation scenario generation: Apply +20% and -20% perturbations to each dimension separately (re-normalizing afterward), generating 2×n perturbation scenarios
  4. Re-compute rankings: Compute a new ranking for each perturbation scenario
  5. Compare change magnitude: Count the number of gaps whose ranking changed in each scenario; compute Kendall τ correlation against the baseline ranking
  6. Stability verdict: stable (all scenarios τ ≥ 0.8) / sensitive (any scenario 0.5 ≤ τ < 0.8) / unstable (any scenario τ < 0.5)
  7. Output: Return a PerturbationReport object

Output Format

{
  "baseline_ranking": ["gap_003", "gap_001", "gap_002"],
  "perturbation_scenarios": [
    {
      "scenario_id": "importance_+20%",
      "perturbed_weights": { "importance": 0.48, "feasibility": 0.18, "novelty": 0.17, "impact": 0.17 },
      "ranking": ["gap_003", "gap_001", "gap_002"],
      "kendall_tau": 1.0,
      "rank_changes": 0
    }
  ],
  "min_kendall_tau": 0.87,
  "stability_verdict": "stable",
  "sensitive_dimensions": [],
  "summary": "Stability summary (2-3 sentences)"
}