How Much Do Large Language Model Cheat on Evaluation? Benchmarking Overestimation under the One-Time-Pad-Based Framework

The project addresses a critical problem in the LLM era: data contamination where models 'cheat' by having seen test data during training. The use of a 'One-Time-Pad' framework—likely involving the randomization or re-lexicalization of prompts to ensure reasoning over recall—is a clever methodological approach. However, from a competitive standpoint, the project has near-zero market traction, with 0 stars and only 5 forks over 259 days, indicating it is essentially a dormant academic artifact. While the problem is massive, the solution is a methodology that can be easily replicated or absorbed by dominant evaluation platforms like Hugging Face (Open LLM Leaderboard) or commercial evaluation entities like Scale AI (SEAL). Frontier labs like OpenAI or Anthropic have a high risk of displacing this because they develop proprietary internal 'de-contamination' pipelines and are unlikely to adopt a third-party academic framework unless it becomes an industry standard. The defensibility is low because there is no network effect or 'data gravity'—it is an algorithmic check that any competent MLE could reimplement after reading the paper.