The Generative Horizon: Applied Hermeneutics, Linguistic Attractors, and the Limits of Model Self-Report

For language-model agents, language is simultaneously an object of interpretation and a medium of generation. The represented situation formed through instructions, prior turns, retrieved records, tool results, summaries, and corrections is therefore not merely what a model computes about; it is part of what the model computes through. This paper calls that situated condition the generative horizon. When an output is retained as context, memory, policy, evidence, or authorization, one generation can alter the conditions of later interpretation and action. This is recursive interpretive conditioning: the output of one horizon becomes part of another.

Hermeneutics supplies a vocabulary for historically formed and revisable understanding; applied hermeneutics translates that problem into requirements for provenance, status, revision, and authority. The argument neither attributes human historical consciousness to language models nor reduces computation to language. It distinguishes the external task, computational substrate, and historically formed organization through which representations become action-guiding.

Gurnee et al.'s 2026 J-space study [1] provides a mechanistic stress test. It supports sparse, token-associated representations that causally participate in report, reasoning, and control, but not an inner observer, privileged point of view, or phenomenal awareness. Its counterfactual-reflection result instead asks how training or retaining a possible report reorganizes later computation.

Systems using summaries, self-reports, decoded features, or evaluation labels should distinguish measurement from intervention, preserve correction and supersession, and bind a representation's authority to evidence proportionate to what it may govern. Mechanistic interpretability does not remove interpretation; it makes the problem experimentally and operationally precise.

Companion paper: Precise Records, Unstable Meanings: Measurement Validity and Unsupported Claims Derived from AI Agent Telemetry. The papers address distinct questions; neither validates the other.