umjunsik132

I've always felt that standard benchmarks focus too much on final accuracy, while the architectural choices that get us there are often treated like a dark art. We celebrate a new SOTA model, but rarely do we have a common language to discuss why a specific operation is efficient or how it contributes to the whole.

That's why I built "The Architect's Arena," a GWO-based benchmark that tries to score the "architectural intelligence" of neural network operations.

The Core Idea (How it Works): Inspired by the "Window is Everything" paper, it breaks down any operation (like a Conv layer) into its fundamental components (Path, Shape, Weight) and calculates a theoretical "Operational Complexity" score. The final efficiency score is a function of this complexity and the model's actual performance.

What's special about this benchmark? We explicitly separate the leaderboard into two parts: 1. Baseline Operations: Individual building blocks like StandardConv. The goal here is to invent new ops with high efficiency scores. 2. Reference Architectures: Complete models like ResNet. This isn't for score competition, but serves as a "performance target" to aim for. The challenge is to use your efficient new ops to build an architecture that can match ResNet's accuracy with lower total complexity.