Recursive Alignment Infrastructure Framework (RAIF): System-2 Reasoning via Recursive Global Coherence

Modern AI systems, particularly large language models (LLMs), exhibit strong performance on tasks grounded in pattern recognition, empirical regularities, and learned priors. However, they systematically fail in open-domain reasoning tasks where truth cannot be inferred from data frequency, benchmarks, or social consensus. These failures arise from the absence of true System-2 reasoning: the capacity to recursively evaluate, revise, and correct reasoning processes themselves. This paper introduces the Recursive Alignment Infrastructure Framework (RAIF), a System-2 reasoning operator grounded in the Functional Model of Intelligence (FMI). RAIF does not generate answers directly. Instead, it evaluates the global logical coherence of competing reasoning processes, detects structural inconsistencies and unexamined assumptions, and recursively updates which reasoning paths should be trusted or revised. In domains without reliable priors, RAIF treats coherence---not consensus or empirical frequency---as the primary predictive signal. We argue that RAIF supplies a capability that LLMs fundamentally lack, and that this capability is necessary for both robust intelligence and meaningful alignment in novel, high-uncertainty domains.