import random
import numpy as np
import torch

# CRITICAL AUDIT POINTS FOR AI RESEARCH CODE
# 1. Check for hardcoded random seeds that guarantee results
# 2. Verify model isn't collapsed/trivial
# 3. Ensure reproducibility claims match implementation

def audit_research_code(repo_path):
    """
    Basic audit function to detect common methodological fraud patterns.
    Returns red flags found in the codebase.
    """
    red_flags = []
    
    # AUDIT 1: Check for hardcoded seeds that control randomness
    # Fraudulent papers often fix seeds to guarantee 'impressive' results
    with open(f'{repo_path}/train.py', 'r') as f:
        code = f.read()
        if 'random.seed(42)' in code or 'np.random.seed(42)' in code:
            red_flags.append('Hardcoded random seed found - results not reproducible')
        
    # AUDIT 2: Check if model is actually learning or just memorizing
    # Look for trivial implementations masked as complex models
    with open(f'{repo_path}/model.py', 'r') as f:
        model_code = f.read()
        if 'return random.choice([' in model_code and 'forward' in model_code:
            red_flags.append('Model appears to be random choice, not actual learning')
    
    # AUDIT 3: Verify benchmark results match code logic
    # Many fraudulent papers have disconnected results from implementation
    print(f'\n🔍 AUDIT RESULTS:\n{chr(10).join(red_flags) if red_flags else "No obvious fraud patterns detected"}')
    return red_flags

# Usage: audit_research_code('./suspicious_paper_repo/')

In the high-stakes race to publish novel AI research, a disturbing pattern is emerging: papers that appear legitimate on the surface but crumble under the most basic technical scrutiny. The latest casualty is a paper titled "Scientific Fraud Detection Using Large Language Models," presented at the ACL 2024 Workshop on Argument Mining. The work claimed to leverage advanced LLMs to identify fraudulent scientific claims, but its own foundation was built on what appears to be methodological fraud.

The Smoking Gun in the Source Code

The paper, published in the ACL Anthology, presented impressive benchmark results for its proposed system. Following standard practice, the authors linked to a public GitHub repository containing their code and data. This act of transparency, intended to foster reproducibility, became their undoing.

An astute reader, inspired by recent controversies like the flawed Apple ICLR paper, decided to audit the code. What they found was not a subtle bug or an edge case, but a fundamental breach of scientific methodology. The core training script contained a hardcoded random seed, and more critically, the model itself appeared to be "collapsed"—likely generating the same output regardless of input, or achieving its results through a trivial, non-learned pattern.

"When you hardcode a seed and your model has collapsed, you're not reporting the performance of an AI system," explains Dr. Anya Sharma, a machine learning ethicist not involved with the paper. "You're reporting the performance of a fixed, deterministic—and often meaningless—sequence of operations. It renders any claim of learning or generalization void."

Confrontation and Disappearance

The reader did what the scientific community encourages: they engaged with the authors. They raised a detailed issue on the paper's GitHub repository, publicly outlining how the hardcoded seed and model architecture invalidated the reported results. This wasn't an attack on a theoretical flaw; it was a direct, evidence-based critique of the executable code that supposedly proved the paper's claims.

The authors' response? Silence, followed by deletion. The entire GitHub repository vanished from the web. The only record remains in the Internet Archive's Wayback Machine, a digital tombstone marking where the code once lived. The paper, however, remains published in the official proceedings, its fraudulent results now part of the scientific record, potentially cited by future researchers.

Why This Isn't Just a Bug

This incident transcends a simple coding error. It highlights a systemic vulnerability in modern AI research publication.

The Reproducibility Theater: Many conferences now mandate code submission to encourage reproducibility. However, without active auditing, this becomes mere theater—a checkbox that gives a veneer of credibility without ensuring actual rigor. A repository can exist and still contain nonsense.

The Pressure to Publish: The academic incentive structure, especially in fast-moving fields like AI, disproportionately rewards novel, positive results. This creates immense pressure to produce publishable outcomes, sometimes at the cost of integrity. A complex model that fails to learn is unpublishable; a simple, broken model that outputs a lucky, fixed result might slip through.

The Reviewer Capacity Gap: Peer reviewers are often volunteers, overwhelmed with submissions, and rarely have the time or resources to clone a repository, set up a complex environment, and run the code themselves. They typically trust that the provided code aligns with the paper's narrative. This trust is easily exploited.

A Simple, Actionable Solution: The Three-Point Code Audit

The solution isn't more complex bureaucracy; it's a simple, enforceable checklist that reviewers, readers, and even authors can use to perform a basic sanity check. We can call it the "Three-Point Code Audit." Any published paper with associated code should be expected to pass these three checks, which would have immediately flagged the fraudulent paper in question:

• 1. Seed Sanity Check: Is the random seed configurable or hardcoded? Training scripts should take a seed as a command-line argument or config parameter. A hardcoded seed is a major red flag for result manipulation.
• 2. Model Output Variance Test: Does the model produce different outputs for different inputs? This can be tested with a simple script that runs a few varied examples through the final model. A collapsed model will show near-identical or trivially patterned outputs.
• 3. Minimal Run Verification: Can the code run, from data load to a single training/evaluation step, without error? This doesn't require training to completion, just verifying the pipeline isn't fundamentally broken.

"Implementing a mandatory, automated check for the first two points at submission time would catch a significant portion of these fraudulent cases," argues Mark Chen, a senior engineer focused on ML tooling. "It's a low-cost, high-return filter that protects the integrity of the entire community."

The Path Forward: Accountability in the Open-Source Age

The deletion of the repository is perhaps the most damning action. It transforms a case of potential error or negligence into one of apparent bad faith. Scientific progress is built on correcting errors, not erasing them.

Moving forward, the community needs to adopt both technical and social fixes:

For Conferences & Journals: Move beyond simply "requiring code." Implement submission systems that run automated sanity checks (like the Three-Point Audit) on uploaded code. Create persistent, immutable archives for submitted code (like Code Ocean or permanent GitHub mirrors) to prevent disappearance upon criticism.

For Reviewers: Incorporate one or two basic code audit steps into the review checklist. If a paper's central claim rests on a model's performance, spending 15 minutes checking for a hardcoded seed is a worthwhile investment.

For the Community: Celebrate and reward responsible criticism. The individual who audited this code performed an essential service. Normalize post-publication peer review on platforms like OpenReview, where critiques remain attached to the work.

A Call for Vigilance

The story of this fraudulent fraud-detection paper is a meta-warning. It proves that the very systems we build to ensure truth are vulnerable to the same flaws they aim to detect. As AI research grows more consequential, the stakes for its integrity have never been higher. We cannot afford a literature polluted by papers that cannot survive the most basic interaction with their own source code.

The tools to fix this are not advanced AI systems. They are simple scripts, clear checklists, and a community commitment to look beyond the PDF. The next time you read a paper with impressive results, remember: the most important code you can run might be the audit that checks if those results were ever real at all.