Repository associated with paper titled "CLP: Finetuning Vision-Language-Action Models Requires Fewer Layers Than You Think"

Boundary-discovery and anti-self-deception framework for AI efficiency research. Produces falsifiable, condition-specific verdicts. First validated result: a hard failure boundary for token pruning.

Tracking State-of-the-Art AI Models and Performance is an open-source dataset documenting AI advancements from the 1950s to today. It includes model details, organizations, compute requirements, and benchmarks. Researchers and developers can analyze trends, compare models, and contribute updates. The dataset is open for collaboration $ fostering AI

White paper and specification for the Decentralized Universal Compute Protocol (DUCP).

Why more compute doesn't mean faster AI : The Roofline Model explained, with an interactive calculator and Python workload analyzer

Adaptive inference algorithm for transformers inspired by quantum collapse (SR framework)