# Audit Summary

**CODEBASE AUDIT RESULT:** MEDIUM

**AGENT REPRODUCIBILITY:** False

---

# Detailed Code Audit Report - Submission 293

## Executive Summary

This submission presents "PsySpace," a multi-agent LLM framework for simulating astronaut crew psychology during long-duration space missions. The codebase is **generally complete and functional** with proper architecture, but has several **notable quality issues** that warrant a MEDIUM severity rating. These issues primarily relate to incomplete dependencies, potential execution barriers, and inconsistencies between documented claims and implementation details.

---

## 1. COMPLETENESS & STRUCTURAL INTEGRITY

### ✅ Strengths:
- **Complete core implementation**: All major components are present and appear functional:
  - `main.py`: Complete entry point with proper argument parsing
  - `agents.py`: Full implementation of CrewAgent and PSAgent classes
  - `environment.py`: Complete mission environments with realistic event data
  - `llm_api.py`: Proper API integration with retry logic and error handling
  - `prompts.py`: Comprehensive prompt templates for all agent interactions
  - `evaluation.py`: Working data logging and CSV/JSON export
  - `analysis.py`: Basic analysis functionality
  - `dynamic_analysis.py`: Advanced statistical analysis suite

- **No placeholder functions**: No TODO comments, empty `pass` statements, or `NotImplementedError` exceptions found
- **Proper error handling**: Try-catch blocks throughout, especially in JSON parsing and API calls
- **Logging infrastructure**: Comprehensive prompt logging system via `prompts_logger.py`

### ⚠️ Issues Identified:

1. **Incomplete Dependencies** (CRITICAL for execution):
   ```python
   # requirements.txt contains only:
   openai
   pandas
   python-dotenv
   tqdm

   # But code imports:
   matplotlib, seaborn, numpy, scipy, nltk
   ```
   This is a **major discrepancy** that would prevent the analysis scripts from running.

2. **Missing Real Data File**:
   - `dynamic_analysis.py` line 83 references: `real_data/mars500_poms_data.csv`
   - This file is not included in the submission
   - Code handles this gracefully with a skip message, but users cannot reproduce the "validation against real-world analog missions" claim without it

3. **Missing .env Configuration**:
   - Code requires `.env` file with API keys
   - Not included (correctly, for security), but no template provided
   - Users must infer structure from README

4. **Potential Model Availability Issues**:
   - Code references `gpt-4.1-mini-2025-04-14` and `gpt-4.1-nano-2025-04-14` (lines in llm_api.py:25-26)
   - These model identifiers appear to be **future-dated** (April 2025) which raises questions about:
     - Whether these models actually exist
     - Whether the experiments could have been run as described
     - Possible placeholder names for unreleased models

---

## 2. RESULTS AUTHENTICITY RED FLAGS

### ✅ Positive Indicators:
- **No hardcoded results**: Searched for specific values from the paper (0.255, 0.291, 0.181, etc.) - none found in code
- **Proper statistical computation**: Bootstrap confidence intervals and permutation tests are correctly implemented
- **Real computation paths**: All metrics appear to be genuinely computed from simulation data
- **No cherry-picking evidence**: No excessive random seed manipulation detected

### ⚠️ Concerns:

1. **Model Naming Inconsistency**:
   - Paper reports results for "GPT-4.1-mini" and "GPT-4.1-nano"
   - OpenAI's actual model naming convention typically uses "gpt-4o" variants or "gpt-4-turbo"
   - The "4.1" designation and 2025 dates suggest either:
     - Fictional model names
     - Beta/early access models not publicly available
     - Future work presented as completed research

2. **Simulation Determinism**:
   - Random events use `random.random()` and `random.choices()` without explicit seeding in main simulation loop
   - Paper claims "10 full iterations per experimental condition" but reproducibility could be challenging
   - No clear seed management for ensuring reproducible results

3. **Cost Calculation Verification**:
   - Paper claims "Total cost: $173.43 for all OpenAI models"
   - No cost tracking code visible in the submission
   - GPU hour calculations (~3,600 hours) also not tracked in code

---

## 3. IMPLEMENTATION-PAPER CONSISTENCY

### ✅ Matches Paper Claims:

1. **Dual-component psychological model**: ✓
   - Static personality profile (OCEAN + Resilience) correctly implemented
   - Dynamic state vector (Stress, Loneliness) with proper bounds [0,1]

2. **Two-stage response generation**: ✓
   - Stage 1: Response generation via `CREW_RESPONSE_PROMPT`
   - Stage 2: State update via `STATE_UPDATE_PROMPT` with LLM-computed deltas

3. **Mission data**: ✓
   - HI-SEAS I, II, IV, and Mars-500 missions properly defined
   - Correct durations (120, 120, 365, 520 days)
   - Scheduled events align with descriptions

4. **PSA intervention mechanism**: ✓
   - Stress threshold monitoring (0.7)
   - Effectiveness check using LLM
   - CBT-based prompts

### ⚠️ Discrepancies:

1. **Statistical Rigor**:
   - Paper claims: "10,000 resamples for confidence intervals"
   - Code implements: `n_bootstrap=10000` ✓ (matches)
   - Paper claims: "10,000 permutations for p-values"
   - Code implements: `n_permutations=10000` ✓ (matches)
   - **HOWEVER**: Default in main.py is `--iterations 1`, not 10
   - Users must explicitly request 10 iterations

2. **Crew Generation**:
   - Paper: "Six-agent crew generated by GPT-4.1-mini and held constant"
   - Code: Uses `gpt-4.1-mini` for generation (line 111 in agents.py) ✓
   - Correctly implements `copy.deepcopy` to maintain base crew across iterations ✓

3. **Temperature Settings**:
   - Response generation: 0.75
   - State updates: 0.2
   - PSA intervention: 0.6
   - PSA effectiveness: 0.1
   - These are reasonable but not explicitly documented in paper

---

## 4. CODE QUALITY SIGNALS

### ✅ Positive Indicators:

1. **Clean, readable code**: Proper function organization, meaningful variable names
2. **Minimal code duplication**: DRY principles largely followed
3. **Appropriate abstractions**: Clear separation between agents, environment, evaluation
4. **Error handling present**: JSON parsing errors caught and logged
5. **Progress indicators**: Uses `tqdm` for user feedback during long simulations
6. **Comprehensive analysis suite**: `dynamic_analysis.py` implements sophisticated metrics:
   - Bootstrap confidence intervals
   - Permutation testing
   - Keystone agent analysis (rolling correlations)
   - Linguistic coping strategy analysis
   - Crew cohesion metrics

### ⚠️ Quality Concerns:

1. **Commented-out code**:
   - README line 30 references `neurips_suite_final.py` but actual file is `dynamic_analysis.py`
   - Minor documentation inconsistency

2. **Import organization**:
   - All necessary imports appear used (no dead imports detected)
   - However, missing from requirements.txt creates confusion

3. **Mixed client configuration**:
   - `llm_api.py` handles both OpenAI and Ollama models
   - Ollama requires local server at `localhost:11434`
   - This dependency not mentioned in README setup instructions

4. **File naming inconsistency**:
   - Timestamps in evaluation.py create unique filenames
   - Makes analysis.py glob patterns brittle (relies on specific naming convention)

---

## 5. FUNCTIONALITY INDICATORS

### ✅ Evidence of Real Development:

1. **Proper data flow**:
   - CSV and JSON outputs properly structured
   - Dialogue history managed with `deque(maxlen=5)`
   - Agent states correctly serialized

2. **Training/simulation loop**:
   - Day-by-day iteration properly implemented
   - Event injection working as designed
   - State updates with clamping [0,1]

3. **Evaluation metrics computed**:
   - Stress/loneliness means and standard deviations
   - Cohesion score: `1 / (1 + daily_stress_std)`
   - Psychological keystone: 14-day rolling correlations
   - All genuinely calculated, not hardcoded

4. **Realistic retry logic**:
   - API calls have 5 retries with 10-second delays
   - Handles rate limits and API errors
   - Crew generation retries on JSON parse failures

### ⚠️ Execution Barriers:

1. **API Key Requirement**:
   - Code cannot run without valid `OPENAI_API_KEY`
   - For gemma3/qwen3 models, requires Ollama server running
   - These are reasonable requirements but limit immediate reproducibility

2. **Computational Cost**:
   - Mars-500 simulation = 520 days × 6 agents × multiple LLM calls
   - Paper mentions $173.43 cost, but this could be prohibitive for verification
   - No cost estimation tool provided

3. **Missing Data Directory Setup**:
   - Code creates `results/` and `analysis_output/` automatically ✓
   - But `real_data/` directory structure not documented or created

---

## 6. DEPENDENCY & ENVIRONMENT ISSUES

### Critical Missing Dependencies:

```
# Required but not in requirements.txt:
matplotlib
seaborn
numpy
scipy
nltk
```

### Version Specification Issues:

- **No version pinning**: `requirements.txt` lacks version numbers
- Could lead to compatibility issues with breaking changes
- OpenAI library frequently updates with breaking changes

### Recommended requirements.txt:

```
openai>=1.0.0
pandas>=2.0.0
python-dotenv>=1.0.0
tqdm>=4.65.0
matplotlib>=3.7.0
seaborn>=0.12.0
numpy>=1.24.0
scipy>=1.10.0
nltk>=3.8.0
```

### Platform Dependencies:

1. **Ollama Server** (for gemma3/qwen3):
   - Requires separate installation
   - Must be running on port 11434
   - Not mentioned in installation instructions

2. **NLTK Data**:
   - Code attempts to download VADER lexicon automatically
   - Good practice, but could fail in restricted environments

---

## 7. REPRODUCIBILITY ASSESSMENT

### Can the Results Be Reproduced?

**Partially, with significant effort:**

1. ✅ **Code structure supports reproduction**: Complete simulation pipeline
2. ⚠️ **API dependencies**: Requires valid API keys and payment
3. ⚠️ **Model availability**: GPT-4.1 models may not be publicly available
4. ⚠️ **Missing dependencies**: Must manually identify and install
5. ⚠️ **Computational cost**: ~$173+ to fully reproduce all experiments
6. ❌ **Real data comparison**: Cannot reproduce without Mars-500 data file
7. ⚠️ **Randomness**: No explicit seed management for perfect reproduction

### Documentation Quality:

- **README**: Good high-level overview, clear examples
- **Code comments**: Minimal but code is self-documenting
- **Docstrings**: Limited; functions lack formal documentation
- **Setup instructions**: Incomplete (missing Ollama, dependency list)

---

## 8. SPECIFIC RED FLAGS

### 🚩 Model Name Discrepancy (HIGH PRIORITY):

The use of `gpt-4.1-mini-2025-04-14` and `gpt-4.1-nano-2025-04-14` is **highly suspicious**:

- As of knowledge cutoff, these models do not exist in OpenAI's public catalog
- The date "2025-04-14" suggests future models
- Possible explanations:
  1. Placeholder names for beta/early access models
  2. Fictional models for demonstration purposes
  3. Actual unreleased models (would require documentation of special access)
  4. Typographical errors (should be gpt-4o-mini variants)

**Impact**: Readers cannot reproduce the exact experiments without access to these specific models.

### 🚩 Incomplete Requirements File:

Missing 5 critical libraries suggests:
- Hasty submission preparation
- Testing performed in environment with pre-installed packages
- Lack of fresh environment testing

**Impact**: Code will crash immediately when analysis runs.

### 🚩 Missing Real Data:

The validation claim in the paper cannot be verified without `mars500_poms_data.csv`.

**Impact**: Key claim about "third-quarter phenomenon" validation is not reproducible.

---

## 9. POSITIVE ASPECTS

Despite the issues, this submission has notable strengths:

1. **Sophisticated simulation framework**: Well-architected multi-agent system
2. **Proper statistical methods**: Bootstrap CI and permutation tests correctly implemented
3. **Comprehensive analysis**: Advanced metrics (keystone analysis, coping strategies)
4. **Clean code**: Readable, organized, follows good practices
5. **Error handling**: Robust retry logic and graceful degradation
6. **Logging system**: Excellent prompt/response tracking for debugging
7. **No obvious result fabrication**: Statistics appear genuinely computed

---

## 10. RECOMMENDATIONS FOR AUTHORS

To improve reproducibility and code quality:

1. **Fix requirements.txt**: Include all dependencies with version pins
2. **Clarify model names**: Provide actual available model identifiers or document special access
3. **Include .env template**: Add `.env.example` file
4. **Add real data**: Include Mars-500 validation data or provide download instructions
5. **Document Ollama setup**: Add instructions for local model deployment
6. **Add seed management**: Implement reproducible random seed handling
7. **Version specification**: Pin all library versions
8. **Cost estimation tool**: Add script to estimate computational costs before running
9. **Fresh environment testing**: Test installation from scratch in clean environment
10. **Clarify README naming**: Fix `neurips_suite_final.py` vs `dynamic_analysis.py` discrepancy

---

## 11. FINAL VERDICT

### Severity: MEDIUM

**Justification:**

- **Not CRITICAL**: Code structure is sound, no hardcoded results, real computation paths exist
- **Not LOW**: Significant barriers to execution (missing dependencies, questionable model names)
- **MEDIUM is appropriate**: Code is fundamentally functional but has quality issues that create barriers to reproduction

### Can Results Be Trusted?

**Conditional trust with caveats:**

- ✅ Statistical methods are sound
- ✅ No evidence of result fabrication
- ✅ Simulation logic appears correct
- ⚠️ Model name issues raise questions about whether experiments were actually run as described
- ⚠️ Lack of real data prevents validation of key claims
- ⚠️ Missing dependencies suggest insufficient testing

### Agent Reproducibility: False

**Reasoning:**

- No evidence of prompts used to generate the code itself
- No documentation of AI assistance in code development
- The paper describes simulated agents using LLMs, but this is the research subject, not documentation of AI-assisted code generation
- The `prompts_logger.py` logs simulation prompts, not code generation prompts

---

## 12. CONCLUSION

This submission presents a **well-designed and mostly complete codebase** with **legitimate implementation of the described methods**. However, several quality issues—particularly the incomplete requirements file and questionable model identifiers—create barriers to immediate reproduction and raise questions about whether the experiments were fully executed as described.

The code does not show signs of being deliberately incomplete or fraudulent, but rather appears to be a work-in-progress that was submitted before final quality assurance testing in a clean environment. With the recommended fixes, this codebase would be fully functional and reproducible.

**Primary concerns requiring clarification:**
1. What are the actual model identifiers used (GPT-4.1 variants don't appear to exist)?
2. Where can reviewers obtain the Mars-500 validation data?
3. Why are critical dependencies missing from requirements.txt?

The MEDIUM severity rating reflects these reproducibility barriers rather than fundamental flaws in the implementation approach.