feat(benchmark): add robust psychometric test assembly - #101
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🤖 AI Code Review (gemini-3-flash-preview)🇬🇧 English Analysis1. Executive Summary
2. AI Content Analysis
3. Engineering & Economic Assessment
4. Quality Assurance
5. Security & Privacy Check
🇨🇳 中文分析1. 摘要
2. AI 成分分析
3. 工程与经济评估
4. 质量保证
5. Security & Privacy Check
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Review — RobustTestAssembly (PR #101) Thank you for contributing this benchmark. Psychometric test assembly is a genuine assessment engineering problem. The overall design is solid — deterministic scenario generation, an independent scorer, a comprehensive test suite, and a baseline anchor are all in place. However, several issues need to be addressed. 1. Domain, Economic Value, and Frontier-Eng Fit ✅ Psychometric test assembly is a real educational and personnel assessment engineering problem — selecting items from an item bank to form a test while satisfying content coverage, administration time, fairness risk, item exposure, and security constraints, while maximizing measurement information. The quality of test assembly for standardized tests (e.g., SAT, GRE, professional certification) directly affects test validity and fairness, giving the task clear economic value. Starting from a feasible baseline and iteratively improving via a verifier aligns with Frontier-Eng's positioning. 2. Not purely numerical ✅ The agent implements 3. Search space ✅ Choosing 24 items from 80 while satisfying multiple constraints has a combinatorial space of C(80,24) ≈ 6.4×10^18, making brute-force search infeasible. The baseline uses greedy + local swap, with clear headroom for improvement (the independent local-search fixture reaches 72.6 vs. the baseline's 50 per design_notes.md). 4. Evaluator and engineering verification ✅ The evaluator independently implements all constraint checks and objective computation:
The objective is independently computed in scoring.py (2PL information function + weighted information + worst-point information + profile balance + DIF/exposure/time/coverage quality), without trusting candidate-reported values. The candidate runs in a subprocess with a 10-second timeout. 5. Constraint enforcement ✅ Constraints are implemented one by one in 6. Baseline experiment ✅
7. Scoring system ✅
8. Test suite ✅ (a highlight)
Issues to address Issue 1 (most critical): Evaluator lacks EVOLVE-BLOCK boundary validation
Issue 2: Missing The Issue 3: Candidate subprocess lacks resource limits
Issue 4 (non-blocking): Candidate determinism is not explicitly verified by the evaluator The evaluator does not run the same candidate twice and compare outputs. Although the test suite verifies baseline determinism, an agent-improved candidate's determinism is not explicitly checked. Suggestion: run the candidate twice per scenario in Issue 5 (non-blocking):
Summary The psychometric test assembly direction is sound, with deterministic scenario generation, an independent scorer, a comprehensive test suite, and a baseline anchor all well designed. Issues 1 (missing EVOLVE-BLOCK validation), 2 (missing eval_cwd.txt), and 3 (no RLIMIT) are the main items to fix. The review can proceed once the above issues are addressed. |
🤖 AI Code Review (gemini-3-flash-preview)🇬🇧 English Analysis1. Executive Summary
2. AI Content Analysis
3. Engineering & Economic Assessment
4. Quality Assurance
5. Security & Privacy Check
🇨🇳 中文分析1. 摘要
2. AI 成分分析
3. 工程与经济评估
4. 质量保证
5. 安全与隐私检查
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Addressed the review feedback in 6b7d5d8: Added EVOLVE-BLOCK boundary validation. Validation: python verification/test_task_v1.py — all tests passed Thanks for the detailed review. |
Review Conclusion: Request ChangesThe author has addressed the issues raised in the previous review, including EVOLVE-BLOCK validation, Blocking Issues1. The candidate is not actually isolated from the verifier, generator, or anchor dataevaluator.py launches the candidate as a regular Python subprocess within the task directory.
Candidate code inside the EVOLVE-BLOCK can read these files. EVOLVE-BLOCK validation prevents changes outside the designated region, but does not prevent reading evaluator assets, environment variables, or network resources. Therefore, the stated offline and confidentiality constraints are not actually enforced or tested. The candidate should run in an isolated directory or container containing only the candidate script, public problem file, and output path. Hidden files must not be readable from that environment; network access and unrelated environment variables should also be removed. Adversarial tests should verify that a candidate cannot access the generator, scorer, anchors, or environment secrets. 2. Validation scenarios leak per-scenario feedback and do not provide an effective holdoutevaluator.py returns the following for every scenario, including validation scenarios:
Only the detailed objective components are omitted. Because all ten scenarios and their order are fixed, and each generated item bank forms a stable fingerprint, an agent can repeatedly tune against individual validation scenarios. The committed generator and fixed seeds further weaken the intended generalization test. Validation scenarios should contribute only to the final aggregate score. Their names, individual scores, objective values, constraint statistics, and errors should not be exposed. Preferably, validation scenarios should use undisclosed seeds or be generated dynamically during evaluation. Major Issues3. The engineering domain is real, but the evaluator remains a highly stylized synthetic modelAutomated test assembly is a genuine psychometric and assessment-engineering problem with clear economic value. The task is also structured discrete optimization rather than simple continuous parameter tuning. However, the discrimination, difficulty, DIF, exposure, and timing values for the 80 items are generated from largely independent random distributions. Enemy groups are mechanically constructed as item pairs. In particular, Consequently, the verifier evaluates subset selection under synthetic scalar constraints rather than a realistic assessment-engineering environment. It should not currently be described as production-grade. Recommended improvements include:
4. The scoring weights and normalization are insufficiently justifiedscoring.py directly combines components with different scales. The documentation describes the coefficients as percentages, but the information terms and the normalized quality terms do not share a common range. The coefficients therefore cannot be interpreted directly as contribution percentages. evaluator.py uses: No empirical or theoretical justification is provided for Furthermore, one invalid scenario makes the entire formal score zero. This discontinuity may cause many agents with substantially different performance to receive the same zero score. 5. The baseline is reproducible, but the submitted evidence does not establish agent-level discriminationI verified that:
However, the baseline scores 50 by definition because it is compared against anchors generated from itself. This is not independent experimental evidence. The only improved implementation uses an objective highly similar to the formal scorer, with only minor weight changes. Its score of 72.6 therefore does not establish discrimination among different agents or algorithm families. The PR should report:
Minor Issues
Overall AssessmentThe domain, economic relevance, discrete search space, and basic constraint enforcement are sound. The search space exceeds However, fixed-scenario feedback leakage, insufficient runtime isolation, limited engineering realism, and the absence of real agent performance distributions currently prevent this task from meeting the standard expected of a robust Frontier-Eng benchmark. |
2 participants
Summary
AssessmentEngineeringdomain.RobustTestAssembly, a deterministic and fully offline psychometric test-assembly benchmark.Validation
50.010/1072.605462Commands run
python verification/test_task_v1.pypython verification/evaluator.py scripts/init.pypython -m frontier_eval task=unified task.benchmark=AssessmentEngineering/RobustTestAssembly algorithm=openevolve algorithm.iterations=0All item banks and risk indicators are synthetic. The benchmark requires only the Python standard library and no external data or network access.