From capability to assertability: epistemic regulation of LLM-mediated claim presentation

Abstract Improvements in large language model (LLM) capability can increase accuracy, reasoning, and access to relevant information while leaving unassessed whether a particular output is appropriately presented for epistemic uptake. This creates an evaluative gap: first-order capability metrics can register genuine gains while leaving claim-presentation conditions underdetermined. LLM outputs increasingly occupy assertion-like roles without thereby acquiring the standing of accountable asserters. We develop assertability as a relational normative status of an output-in-practice, determined by the evidential and contextual conditions governing its presentation. One such condition is the evidence frame: the set of sources admissible as grounds within an interaction. A true answer, or one well supported outside the governing frame, may therefore remain non-assertable when it relies on an inadmissible evidential basis. To address this capability–assertability gap, we use epistemic regulation to denote the normative-functional regulation of whether and how an LLM-mediated output is presented for epistemic uptake in light of the conditions bearing on its assertability. We organize the framework across four regulatory loci—assertoric force, evidential grounding, evidential candor, and epistemic response selection. The framework is normative-functional, while its realization may be distributed across learned behavior, retrieval and verification mechanisms, interface design, evaluation, deployment rules, and institutional practices, with responsibility and accountability remaining with identifiable human and organizational actors. Because standards governing assertion, qualification, verification, and withholding can distribute epistemic risk and verification burdens unevenly, their specification and oversight are also governance questions rather than merely technical calibration choices.