The Detection-Extraction Gap: Models Know the Answer Before They Can Say It

This project identifies a critical inefficiency in the current 'reasoning' model paradigm (e.g., OpenAI o1, DeepSeek R1). The core insight—that models determine the answer long before they stop 'thinking'—is highly valuable for inference cost reduction. However, as a research paper with a nascent repository (0 stars, 8 days old), it lacks any structural moat. The 'detection-extraction gap' is a discovery, not a protected technology. Frontier labs like OpenAI and DeepSeek are the primary stakeholders and are likely already building internal 'early-exit' or 'dynamic reasoning budget' mechanisms to solve exactly what this paper describes. While the nomenclature is new, the concept of early-exiting in LLMs is an established research area. The project is a 'reference implementation' of a phenomenon that will likely be absorbed into the core architecture of future frontier models within 6 months to reduce COGS (Cost of Goods Sold) and latency. Its value is as an academic pointer for optimization rather than a standalone tool or platform.