# FRC
2 itemsThe Coherence Delta: Benchmarking Reflexive Closure in Large Language Models
The current bottleneck in artificial intelligence is not compute, but reliability. For builders and investors, the "hallucination problem" has remained an intractable shadow over the scaling of Large Language Models (LLMs). Conventional benchmarks (MMLU, HumanEval) measure static knowledge retrieval or narrow logic, but fail to predict the breakdown of reasoning in high-entropy, multi-step environments.
Quantitative Benchmarking of Coherence Density in Recursive Architectures
The current discourse surrounding Large Language Model (LLM) performance remains stalled by qualitative descriptors. Terms like "reasoning," "understanding," and "emergent behavior" lack the formal rigor required for precision engineering and high-stakes capital allocation. To move beyond heuristic-based evaluation, we must transition to a framework grounded in the conservation laws of information.