Epistemic Constraints and Semantic Compression in Natural Language Processing: A Theoretical Foundation for the HGC³AE² Framework

Large language models produce fluent, confident output across a wide distributional surface but fail systematically at the boundaries of domains governed by external validation mechanisms — law, medicine, engineering, empirical science. This paper develops a theoretical foundation for that failure mode. It introduces the concept of thin context as a formally defined epistemic condition: the state in which a representation lacks the domain-specific constraints necessary for a validation mechanism to constitute an authoritative answer. It identifies semantic compression — the statistical compression of meaning performed by distributional language representation — as the generative mechanism of thin context, and it argues that thin context is the epistemic substrate from which the structural failure modes of agentic systems are generated.

The analysis proceeds in three movements. The first (§§2–5) develops the theoretical apparatus: language as lossy compression (Shannon, Harris, the distributional hypothesis); epistemic domains and their validation mechanisms (law, medicine, engineering, science); the formal definition of thin context; the mechanism of semantic compression that produces it. The second (§§6–7) draws the bridge to human cognition and to the HGC³AE² framework: human inference under thin context succeeds where distributional inference fails because humans carry domain-specific validation authority that distributional models structurally cannot; HGC³AE² is not a governance preference layered on top of a capable system — it is what the epistemic analysis structurally requires. The third (§§8–11) develops the operational implications: architectural interventions (retrieval-augmented generation, constrained decoding, explainability) reduce but do not eliminate thin context; human epistemic authority at domain boundaries is the supply side of the validation mechanism that no architecture replaces; evaluation regimes calibrated to distributional objectives cannot detect thin context at domain boundaries and must be reformed.

This paper is the epistemic companion to *Mitigating Confident Misalignment in Agentic Systems: The HGC³AE² Framework* (Kuiper 2026). Paper One identified confident misalignment as the dominant failure mode and proposed HGC³AE² as a governance-first architecture for addressing it. Paper Two provides the epistemic account of why that architecture is structurally necessary rather than merely prudent.

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