sifii/MoRE

MoRE represents a specialized academic approach to LLM text detection, specifically targeting the problem of adversarial evasion (PGD attacks). While the use of Mixture-of-Experts for representation learning in detection is a novel combination, the project currently lacks any market signals (0 stars, 0 forks, 1 day old). Its defensibility is minimal because the architecture is a configuration of standard deep learning components that can be easily replicated by established players like GPTZero, Originality.ai, or the frontier labs themselves (OpenAI, Google). Frontier labs represent a high risk here because they are increasingly integrating watermarking (e.g., Google's SynthID) and internal detection layers directly into their APIs, potentially making third-party post-hoc detectors like this obsolete. The displacement horizon is short (6 months) as new LLM releases often change the statistical signatures that these detectors rely on, requiring constant retraining and adaptation.