New research establishes fundamental, architecture-based limits on the performance and feasibility of various AI tasks (like preference learning and verification). The work translates these theoretical impossibility results into concrete design specifications for building truly trustworthy AI.
A new thesis, 'The Deterministic Horizon,' reinterprets impossibility results from theoretical computer science (such as No Free Lunch theorems) as actionable design rules for artificial intelligence. The research proves that accuracy ceilings are set solely by the model's architecture, regardless of training or fine-tuning efforts past a critical reasoning depth. The study catalogs sixteen impossibility specifications across various subfields—including preference learning, multi-stage retrieval, and zero-knowledge verification—quantifying the violation costs and providing constructive design rules. This methodology offers a framework for the generative research program seeking to ensure trustworthy AI by acknowledging and designing around the fundamental limits of computation.