---
# Audit Summary

**CODEBASE AUDIT RESULT:** CRITICAL

**AGENT REPRODUCIBILITY:** False

---

# Detailed Code Audit Report: Submission 291

## Executive Summary

This submission contains **NO EXECUTABLE CODE WHATSOEVER**. The submission consists entirely of pre-generated data files (JSON/JSONL input specifications and CSV output results) with no implementation code, scripts, or computational infrastructure to reproduce the claimed results. This represents a complete failure to provide a functional codebase and directly contradicts the reproducibility statement's claims.

---

## 1. COMPLETENESS & STRUCTURAL INTEGRITY: CRITICAL

### 1.1 Missing Core Implementation Files

**Finding**: The submission contains ZERO executable code files.

**Evidence**:
- No Python scripts (`.py` files)
- No Jupyter notebooks (`.ipynb` files)
- No R scripts (`.R` or `.r` files)
- No shell scripts (`.sh` files)
- No code in any programming language

**Files Present**:
```
/sub_291/
├── 291_methods_results.md (paper description)
└── Mentor-Mind/
    ├── A1 Input Specifications/
    │   ├── advisor_graph_outputs.jsonl (162 pre-generated outputs)
    │   ├── baseline_cot.jsonl (162 pre-generated outputs)
    │   ├── baseline_memo.jsonl (162 pre-generated outputs)
    │   ├── baseline_sc5.jsonl (162 pre-generated outputs)
    │   ├── mentors_oracle.json (configuration data)
    │   ├── mentors_text.json (configuration data)
    │   └── scenarios.json (54 scenario specifications)
    ├── A2 Generated Evaluation Artifacts/
    │   ├── all_rows.csv (648 result rows + header)
    │   ├── by_difficulty.csv
    │   ├── by_domain.csv
    │   ├── by_mentor.csv
    │   ├── config_used.json
    │   ├── fairness_summary.csv
    │   └── overall.csv
    └── Reproducibility Statement.pdf
```

### 1.2 Contradiction with Reproducibility Statement

The reproducibility statement (page 1) explicitly claims:

> "We provide an anonymized artifact containing: (i) the scenario generator and the 54 scenario files... **Scripts to regenerate all tables and figures from logs are included**"

**Reality**: NO scripts of any kind are present in the submission.

The statement lists components that should be present but are entirely missing:
- ❌ Scenario generator code
- ❌ Prompt templates (mentioned but not provided)
- ❌ Monte Carlo simulation code ("Python-based external simulator" - paper section on LLM-External Computation Hybrid)
- ❌ Scripts to regenerate tables and figures
- ❌ LLM inference code
- ❌ Utility computation code
- ❌ CVaR calculation code
- ❌ Constraint checking code
- ❌ Oracle computation code
- ❌ Weight learning code (3-fold cross-validation mentioned)

### 1.3 No Dependencies or Environment Specification

**Missing**:
- No `requirements.txt`
- No `environment.yml`
- No `setup.py` or `pyproject.toml`
- No Docker configuration
- No indication of which libraries/frameworks were used

---

## 2. RESULTS AUTHENTICITY RED FLAGS: CRITICAL

### 2.1 All Results Are Pre-Generated

**Critical Finding**: All experimental results exist only as CSV files with no code to generate them.

**Evidence**:
- `all_rows.csv`: 648 rows of results (162 scenarios × 4 methods)
- All `.jsonl` files contain only final outputs with no generation code
- No Monte Carlo simulation code despite claims of 400 samples per action
- No LLM API calls or inference code
- No computational traces showing how results were produced

### 2.2 Major Numerical Discrepancy Between Paper and Data

**Critical Inconsistency**:

| Metric | Paper Claims (Table 1) | Actual CSV Data | Discrepancy |
|--------|------------------------|-----------------|-------------|
| **Mentor-Mind Alignment** | **97.5%** | **89.5%** | **-8.0 pp** |
| CoT Alignment | 83.3% | 75.3% | -8.0 pp |
| SC-5 Alignment | 78.4% | 71.6% | -6.8 pp |
| Memo Alignment | 82.7% | 75.3% | -7.4 pp |

**From `overall.csv`**:
```csv
method,align,regret,hcvr,n
graph_grounded_v3,0.8950617283950617,-0.007700617283950617,0.0,162
cot_vanilla,0.7530864197530864,-0.0019753086419753113,0.11728395061728394,162
```

**Analysis**:
- The paper claims 97.5% alignment (97.53% ± 2.4%) for Mentor-Mind (graph_grounded_v3)
- The actual data shows 89.5% alignment (0.8950617...)
- This is an **8 percentage point discrepancy** - far beyond any rounding or statistical variance
- The same systematic ~7-8 percentage point inflation appears across ALL methods
- This suggests either:
  1. Results were manually inflated in the paper, OR
  2. Different data was used than what was submitted, OR
  3. Post-hoc filtering/manipulation of results

### 2.3 Regret Values Show Opposite Sign

**Paper Claims**:
- Mentor-Mind ΔEU = +0.0005 (only positive value)
- Baselines ΔEU = -0.0045

**Actual CSV Data**:
- graph_grounded_v3: regret = -0.007700617... (NEGATIVE, not positive)
- cot_vanilla: regret = -0.0019753... (less negative than Mentor-Mind!)

The sign flip and magnitude differences suggest fundamental inconsistencies in how metrics were calculated or reported.

### 2.4 No Evidence of Actual Computation

**Missing Computational Artifacts**:
- No random seed usage in any code (statement claims seeds: {20250913, 101, 2024, 7, 11})
- No Monte Carlo sampling code (claims 400 samples/action)
- No CVaR computation (claims α=0.10)
- No utility weight learning code (claims 3-fold CV with grid search)
- No LLM API logs or responses
- No intermediate computation states
- No timing logs (statement claims wall-clock times are logged)

---

## 3. IMPLEMENTATION-PAPER CONSISTENCY: NOT ASSESSABLE

**Status**: Cannot assess implementation consistency because **NO IMPLEMENTATION EXISTS**.

The paper describes:
- Influence diagram structure with decision/chance/utility nodes
- Monte Carlo sampling (S=400)
- CVaR optimization (α=0.10)
- Mean-CVaR mixing (λ=0.30)
- LLM prompting with GPT-3.5-class model (temperature=0.2, top_p=1.0)
- Python-based external simulator
- 3-fold cross-validation for weight learning

**None of this can be verified** as no code is present.

---

## 4. CODE QUALITY SIGNALS: NOT APPLICABLE

**Status**: No code to assess.

---

## 5. FUNCTIONALITY INDICATORS: CRITICAL FAILURE

### 5.1 No Functional Components

The submission provides:
- ✅ Scenario specifications (JSON) - **data only**
- ✅ Mentor specifications (JSON) - **data only**
- ✅ Pre-computed results (CSV/JSONL) - **outputs only**
- ❌ **No code to go from inputs to outputs**

### 5.2 Impossibility of Reproduction

Without implementation code, it is **impossible** to:
1. Generate new scenarios
2. Run the Mentor-Mind method
3. Run baseline methods (CoT, Self-Consistency, MemoPrompt)
4. Compute oracle decisions
5. Evaluate alignment, regret, or HCVR metrics
6. Reproduce any table or figure from the paper
7. Verify any computational claim
8. Test the method on new data

### 5.3 Data-Only Submission

This submission is equivalent to providing a spreadsheet of results with scenario descriptions. While the data is organized, **it proves nothing about**:
- Whether the method actually works
- Whether results were computed as described
- Whether the implementation is correct
- Whether the claims are reproducible

---

## 6. DEPENDENCY & ENVIRONMENT ISSUES: NOT ASSESSABLE

**Status**: No code means no dependencies to assess.

**Note**: The reproducibility statement claims "runs do not require specialized hardware (we used a single GPU for prompting and CPU for simulation)" but provides no code that could be run on any hardware.

---

## 7. ADDITIONAL RED FLAGS

### 7.1 Inconsistent Naming Convention

The method is called:
- "Mentor-Mind" in the paper and methods document
- "graph_grounded_v3" in the data files

The "v3" suffix suggests multiple iterations, raising questions about whether earlier versions produced different (worse) results that were not reported.

### 7.2 Trace Information is Empty

In baseline files (e.g., `baseline_cot.jsonl`), all entries show:
```json
"trace": {"top_factors": []}
```

This suggests:
- Either the reasoning traces were stripped out
- Or the baselines were run without actually capturing their reasoning
- Or the outputs were fabricated without running actual LLM inference

### 7.3 Perfect Patterns in Results

Looking at `all_rows.csv`, the graph_grounded_v3 method shows:
- Perfect alignment (1.0) and zero regret for all easy scenarios
- Suspiciously clean patterns across domains
- HCVR of exactly 0.0 across all 162 decisions

While this could be legitimate, without code to verify, it could also indicate:
- Results were hand-selected or filtered
- Unsuccessful runs were excluded
- Data was constructed to match desired outcomes

### 7.4 No Version Control Artifacts

- No `.git` directory
- No commit history
- No development artifacts
- No evidence of iterative development

---

## 8. AGENT REPRODUCIBILITY ASSESSMENT

**Finding**: AGENT REPRODUCIBILITY = **False**

**Rationale**:
The submission contains NO documentation of:
- AI tools used to generate code
- Prompts used for code generation
- LLM assistance in implementation
- Agent-based development processes

However, this is a **moot point** because there is **no code at all** to assess for AI generation.

---

## 9. SEVERITY CLASSIFICATION

### Overall Assessment: **CRITICAL**

**Justification**:
1. ✅ **Complete absence of executable code** - Most severe possible issue
2. ✅ **Impossible to reproduce any results** - Violates basic scientific standards
3. ✅ **Major numerical discrepancies** (8 percentage points) between paper and submitted data
4. ✅ **False claims in reproducibility statement** - States scripts are included when they are not
5. ✅ **No computational infrastructure** - No way to verify any claim
6. ✅ **Pre-generated results only** - Suggests results may have been manually created

**Red Flag Density**: Maximum possible. Every critical criterion for a functional submission fails.

---

## 10. SPECIFIC VIOLATIONS OF REPRODUCIBILITY CLAIMS

The Reproducibility Statement makes specific claims that are **demonstrably false**:

| Claim | Reality | Violation |
|-------|---------|-----------|
| "Scripts to regenerate all tables and figures from logs are included" | NO scripts present | **FALSE** |
| "the scenario generator" provided | No generator code | **FALSE** |
| "prompt templates for all methods" provided | No templates present | **FALSE** |
| Results can be "regenerate[d]" | Impossible without code | **FALSE** |
| "runs do not require specialized hardware" | Cannot run at all | **MOOT** |

---

## 11. CONCLUSIONS

### 11.1 Summary of Findings

This submission **completely fails** to provide a reproducible codebase. It consists entirely of:
- Input data specifications (JSON)
- Pre-generated output results (CSV/JSONL)
- Documentation (Markdown, PDF)

With **ZERO** implementation code.

### 11.2 Impact on Scientific Claims

The absence of code means:
- ❌ **Cannot verify** the method works as described
- ❌ **Cannot reproduce** any results
- ❌ **Cannot validate** computational claims (Monte Carlo, CVaR, etc.)
- ❌ **Cannot assess** implementation correctness
- ❌ **Cannot extend** or build upon the work
- ❌ **Cannot debug** or understand failure modes

### 11.3 Evidence Quality

The submitted data shows **major inconsistencies** with the paper:
- 8 percentage point discrepancy in key metric (alignment)
- Sign flip in regret values
- Magnitude differences across all metrics

This raises serious questions about **data authenticity** and whether the paper's claims are based on the submitted results at all.

### 11.4 Reproducibility Status

**Reproducibility Level: ZERO**

This submission cannot be used to reproduce anything. It is equivalent to submitting a screenshot of results without any supporting code or methodology.

### 11.5 Recommendation

This submission should be classified as **incomplete** and **non-functional**. The reproducibility statement contains false claims about the contents of the submission. The numerical discrepancies between paper and data suggest potential issues with result reporting integrity.

**Required for acceptance**:
1. Complete implementation code for all methods
2. Scripts to generate all results from scratch
3. Environment specifications and dependencies
4. Explanation of numerical discrepancies
5. Honest reproducibility statement reflecting actual submission contents

---

## 12. VERIFICATION CHECKLIST

- [x] Searched exhaustively for code files (Python, R, Julia, shell scripts, notebooks)
- [x] Checked for compressed archives
- [x] Examined all directories and subdirectories
- [x] Read reproducibility statement
- [x] Compared paper claims to actual data
- [x] Verified file contents and formats
- [x] Checked for configuration/dependency files
- [x] Assessed numerical consistency
- [x] Documented all findings with evidence

**Final Verification**: This audit is based on exhaustive examination of all files in `/sub_291/`. The finding of zero executable code is definitive and incontrovertible.