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Audit Report: Paper 194

Code Audit Report: Submission 194

Date: 2024 Auditor: Claude Code Analysis System Submission Directory: sub_194

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EXECUTIVE SUMMARY

AGENT REPRODUCIBLE: TRUE

This submission extensively documents the use of AI (Cursor AI) to generate code for LLM scaling experiments. The submission contains detailed AI interaction logs showing prompts and code generation, but critically contains NO ACTUAL EXECUTABLE CODE FILES. All submitted materials are documentation files (.md, .pdf) describing AI-assisted code generation conversations.

CRITICAL FINDING: This submission consists entirely of: NO Python files (.py), Jupyter notebooks (.ipynb), R scripts, shell scripts, or any executable code exists in the submission directory.

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DETAILED FINDINGS

1. COMPLETENESS & STRUCTURAL INTEGRITY: CRITICAL

Status: FAIL - No executable code present Issues Identified:
  1. Missing ALL code files (CRITICAL):
    • No .py files
    • No .ipynb notebooks
    • No .R scripts
    • No .sh shell scripts
    • No configuration files (YAML, JSON)
    • No requirements.txt or environment specifications
  1. Only documentation exists:
    • The submission contains only AI conversation logs and a results document
    • Cursor context files show extensive AI-generated code within Markdown but no actual executable files
    • The AI logs reference code structure like /home/ubuntu/agent4science_nathan/ but none of this code is in the submission
  1. Code referenced but not included:
    • Cursor contexts show code generation for:
    • src/models/model_loader.py
    • src/datasets/dataset_loader.py
    • src/scaling/chain_of_thought.py
    • src/evaluation/metrics.py
    • scripts/run_experiment.py
    • Multiple other files
    • None of these files are present in the submission
Evidence: 
Search results for code files:


find sub_194 -type f \( -name ".py" -o -name ".r" -o -name ".R" -o -name ".ipynb" -o -name "*.sh" \)


Result: NO FILES FOUND
Severity: CRITICAL - Cannot verify any code functionality without actual code files.

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2. RESULTS AUTHENTICITY RED FLAGS: CRITICAL

Status: HIGHLY SUSPICIOUS - Results reported without verifiable code Issues Identified:
  1. Highly specific results with no code to verify (CRITICAL):
    • Paper claims: "Gemini 2.5 Flash with internal reasoning achieves 95.36% accuracy at $3.8 × 10⁻⁵ per sample"
    • Paper claims: "Gemini 2.5 Pro: 96.18% to 96.41% with CoT"
    • Paper claims: "GPT-4.1-mini with CoT: 110.6% improvement (45.19% to 95.15%)"
    • Paper claims: "Flash-Lite with CoT: 155.9% improvement (36.67% to 93.85%)"
  1. No way to reproduce results:
    • No code to run experiments
    • No data processing scripts
    • No evaluation code
    • No logs from actual experimental runs
  1. Results are API-based but no API code present:
    • Paper describes experiments with OpenAI GPT-4.1 and Google Gemini models
    • No API calling code
    • No rate limiting/retry logic
    • No response caching code (though mentioned in methods)
  1. AI conversation logs show debugging of non-functional code:
    • cursor_contexts4.md shows: "CoT accuracy drops from 66.0% to 54.0%" with discussion of bugs
    • Indicates code had issues but unclear if these were ever resolved
    • Shows discussion of "answer extraction failures" and "prompt contamination"
Evidence from 194_methods_results.md: Severity: CRITICAL - Published results cannot be independently verified without code.

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3. IMPLEMENTATION-PAPER CONSISTENCY: CANNOT EVALUATE

Status: CANNOT ASSESS - No code available for comparison What Paper Claims: What Cannot Be Verified: Severity: CRITICAL - Impossible to verify paper claims match implementation.

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4. CODE QUALITY SIGNALS: CANNOT EVALUATE

Status: N/A - No code to evaluate Observations from AI Conversation Logs:
  1. Evidence of iterative development (cursor_contexts show):
    • Multiple rounds of code generation
    • Bug fixes discussed (e.g., CoT accuracy issues)
    • Code refinements suggested
    • But final working code not submitted
  1. AI-generated code characteristics (from logs):
    • Well-structured code shown in conversations
    • Comprehensive error handling in examples
    • Good documentation in code snippets
    • However, only snippets within Markdown, not actual files
  1. Development environment referenced:
    • Discussions of H100 GPU instances
    • Lambda Labs cloud infrastructure
    • tmux/screen for long experiments
    • But no proof experiments were actually run
Severity: MEDIUM - Cannot assess code quality without code files.

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5. FUNCTIONALITY INDICATORS: CRITICAL

Status: FAIL - No functional code present Issues:
  1. No entry points:
    • No main.py or run_experiment.py
    • No way to execute anything
  1. No dependencies specified:
    • No requirements.txt
    • No environment.yml
    • No docker configuration
  1. No experimental artifacts:
    • No result files
    • No checkpoint files
    • No log files from actual runs
    • No cached responses (despite paper mentioning SQLite caching)
  1. AI logs show experimental issues:
    • cursor_contexts4.md documents CoT failing (54% vs 66% baseline)
    • Mentions "answer extraction failures"
    • Discusses "prompt contamination with egg examples"
    • Unclear if these issues were resolved in final version
Evidence from cursor_contexts4.md: 
Line 33-34: "Baseline: 66.0% (33/50)"

Line 34: "Majority Voting: 70.0% (35/50)"


Line 35: "Chain of Thought: 54.0% (27/50) ❌"


Line 38-40: Discussion of "overly complex prompting" and "step-by-step error accumulation"

These appear to be debugging experiments, NOT the final results reported in the paper.

Severity: CRITICAL - No evidence of functional implementation.

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6. DEPENDENCY & ENVIRONMENT ISSUES: CANNOT EVALUATE

Status: N/A - No dependency files present Referenced in AI logs but not included: Severity: HIGH - Cannot set up environment to reproduce.

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AI USAGE DOCUMENTATION ANALYSIS

AGENT REPRODUCIBLE: TRUE

Evidence of AI Usage:
  1. Extensive Cursor AI logs (5 files, ~700KB total):
    • cursor_contexts1.md (192KB) - Initial code generation
    • cursor_contexts2.md (73KB) - Code analysis and refinement
    • cursor_contexts3.md (229KB) - Experiment execution planning
    • cursor_contexts4.md (39KB) - CoT accuracy analysis
    • cursor_contexts5.md (168KB) - Experiment setup instructions
  1. Clear documentation of AI interactions:
    • User prompts in Korean and English
    • AI (Cursor) responses with code generation
    • Iterative refinement conversations
    • Debugging discussions
  1. Prompts show research workflow:
    • "AI가 생성한건데, 고칠부분 고치고싶어" (This was generated by AI, want to fix parts)
    • "@cursor_designing_an_automated_experimen.md 이 챗내용 바탕으로 이제 코드를 짜줘" (Based on this chat content, write the code)
    • Extensive instructions for experimental design
  1. AI generated substantial code:
    • Complete project structures defined
    • Multiple Python modules described
    • Configuration files designed
    • Experiment runners created
    • All within Markdown conversation logs, not as actual files
Assessment: The researchers clearly documented their AI-assisted development process, but failed to submit the actual generated code files.

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SEVERITY ASSESSMENT SUMMARY

Critical Issues (Prevent Reproducibility):

  1. ✗ No executable code files present - CRITICAL
  2. ✗ Results cannot be verified - CRITICAL
  3. ✗ No way to reproduce experiments - CRITICAL
  4. ✗ Missing all dependencies and environment specs - CRITICAL
  5. ✗ No data processing or evaluation code - CRITICAL

High Severity Issues:

  1. ✗ AI logs show debugging of broken code (CoT accuracy issues) - HIGH
  2. ✗ No experimental artifacts or logs - HIGH
  3. ✗ Mismatch between debugging results and paper results - HIGH

Medium Severity Issues:

  1. ⚠ Only documentation provided, no implementation - MEDIUM

Observations (Not Issues):

  1. ✓ AI usage well-documented - POSITIVE
  2. ✓ Research methodology clearly described - POSITIVE

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COMPARISON: DOCUMENTED DEBUGGING vs. PAPER RESULTS

Major Discrepancy Identified:

Debugging session (cursor_contexts4.md): Paper claims (194_methods_results.md): Analysis:

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RED FLAGS SUMMARY

🚨 CRITICAL RED FLAGS:

  1. Complete absence of executable code - Submission contains only documentation
  2. Results cannot be independently verified - No code path to reproduce claims
  3. AI logs show broken experimental code - CoT was failing in debugging sessions
  4. Discrepancy between debugging and paper results - Debugging shows opposite trends
  5. No experimental artifacts - No logs, caches, or results from actual runs

⚠️ WARNING FLAGS:

  1. All code exists only in AI conversation logs - Snippets in Markdown, not files
  2. Referenced file paths don't exist in submission - /home/ubuntu/agent4science_nathan/
  3. No environment specifications - Cannot recreate experimental setup
  4. Highly specific results with no verification path - Precision without proof

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RECOMMENDATIONS

For Reviewers:

  1. Request actual code files - All .py files referenced in AI logs
  2. Request experimental logs - Actual output from experiment runs
  3. Request environment specifications - requirements.txt, exact versions
  4. Request explanation of discrepancy - Why debugging showed CoT failing but paper shows success
  5. Request API usage logs - Evidence of actual API calls to GPT-4.1, Gemini
  6. Request cached responses - The SQLite database mentioned in methods
  7. Request per-sample JSONL logs - Mentioned in paper methodology

For Authors:

  1. Submit complete codebase - All files needed to run experiments
  2. Explain debugging vs. paper result discrepancy - Clarify development timeline
  3. Provide experimental artifacts - Logs, results, cached data
  4. Include environment setup - Docker, requirements, versions
  5. Add execution instructions - Step-by-step reproduction guide
  6. Clarify AI-generated code status - Which version was used for paper results

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CONCLUSION

This submission represents an incomplete code submission that fails basic reproducibility requirements. While the AI-assisted development process is well-documented, the actual executable code that would allow independent verification of the paper's claims is entirely absent.

The submission consists of:

The presence of debugging sessions showing opposite trends (CoT failing vs. paper showing CoT succeeding) combined with the absence of final working code raises serious concerns about the authenticity and reproducibility of the reported results.

Final Assessment: UNABLE TO VERIFY - The submission cannot be validated as the code does not exist in any executable form. Regardless of whether the research was conducted properly, the submission as provided cannot support the paper's claims. Recommendation: REJECT pending submission of complete, executable code and resolution of the debugging vs. results discrepancy.

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APPENDIX: File Inventory

sub_194/

├── 194_methods_results.md                    # 7.1 KB - Methods and results description


└── supplementary2/


    ├── cursor_contexts1.md                   # 192 KB - AI conversation log 1


    ├── cursor_contexts2.md                   # 73 KB - AI conversation log 2


    ├── cursor_contexts3.md                   # 229 KB - AI conversation log 3


    ├── cursor_contexts4.md                   # 39 KB - AI conversation log 4 (debugging)


    ├── cursor_contexts5.md                   # 168 KB - AI conversation log 5


    └── paper2_supplementary.pdf              # 691 KB - Supplementary materials



Total files: 7


Executable code files: 0


Configuration files: 0


Data files: 0


Result files: 0

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Report Generated: 2024 Audit Status: COMPLETE Overall Assessment: CRITICAL FAILURE - No code present for verification