AI: Controlling for Proximal Reasoning in LLM Systems
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Proximal Reasoning is a term coined by DB Marketing Technologies to name the mechanism by which large language models produce outputs that occupy the neighborhood of deterministic judgment — close enough to be mistaken for it — without being derived from it. When an LLM is asked to perform structured judgment, classification, or rule application, it produces an output that looks like the result of reasoning by drawing on its training to recognize what such outputs look like and generating a plausible version. The output is proximal to correct deterministic judgment — near it, shaped like it — but produced through probabilistic pattern-matching rather than logical derivation.

Proximal Reasoning is distinct from hallucination, which occurs when a model’s training data is sparse or absent on a subject. Proximal Reasoning occurs on every structured judgment task, regardless of how well-represented the subject matter is in training. It is not a deficiency to be corrected in future model generations — it is a structural property of how LLMs work. The enterprise implication is direct: AI output quality and AI impact management both require Proximal Reasoning to be actively governed through Harness Engineering and controlled against defined Output Quality Standards.