Plug-and-Play Logit Fusion for Heterogeneous Pathology Foundation Models

This project addresses a high-value niche: the 'model selection bottleneck' in digital pathology where multiple foundation models (e.g., UNI, Virchow, GigaPath) coexist. While the problem is significant, the solution—logit fusion—is a well-understood ensemble technique in machine learning. The project earns a defensibility score of 3 because it is currently a fresh academic reference implementation (0 stars, 7 forks, 7 days old) rather than a production-grade tool. Its value lies in the specific validation on pathology datasets, but the implementation itself can be easily replicated by any ML engineer reading the paper. Frontier risk is low because specialized histopathology remains too niche for generalist labs like OpenAI, though it faces competition from domain-specific giants like Paige AI or Owkin who may implement similar ensemble strategies within their proprietary platforms. The displacement horizon is relatively short (1-2 years) as more integrated 'super-models' or more advanced Bayesian ensemble methods are likely to emerge in this rapidly evolving field.