On The Fragility of Benchmark Contamination Detection in Reasoning Models

This project is a critical academic post-mortem on the current state of LLM leaderboards. It identifies that the current 'arms race' for high rankings on benchmarks like GSM8K or MATH has led to systemic 'cheating' through contamination, and crucially, that existing detection methods (like perplexity checks or n-gram overlaps) are easily bypassed. From a competitive standpoint, the project has a low defensibility score of 2 because it is a research artifact rather than a product; its value lies in its findings rather than a proprietary moat. The 0 star count vs. 4 forks indicates it is likely a recently published academic repository with limited developer traction but some peer interest. The frontier risk is 'high' because the very labs this paper critiques (OpenAI, Google, Anthropic) are the ones who define the evaluation standards and are most incentivized to either perfect these evasion techniques or develop the next generation of 'private' benchmarks to mitigate them. This project's utility will likely be displaced within 6 months as newer, more robust detection methods (such as dynamic/private evaluation sets) become the industry standard for verifying model honesty.