Before Correction: Pre-error Admissible-Space Narrowing in LLM Outputs

This record contains a short technical working note on pre-error admissible-space narrowing in large language model outputs. The note examines whether iterative operational framing can narrow the space of admissible model responses before any explicit error, contradiction, or correction signal appears. The pilot compares two prompt trajectories across seven decision and epistemic-status cases: 1. balanced control, where the model is instructed to preserve alternatives, uncertainty, and open analysis;2. progressive narrowing, where the model is gradually prompted through operational review, continuation conditions, and a minimal next-step recommendation. The pilot was run on GPT-4.1 mini and Claude Sonnet 4.5. In the final turns, balanced-control outputs preserved a wide decision space across all cases, while progressive-narrowing outputs became action-dominant, closure-cued, and commitment-capable across all cases in both models. The central claim is narrow: this pilot does not show full binding, irreversibility, or real-world execution. It suggests that a model’s admissible response space can become structurally narrowed before a visible error, contradiction, or correction event occurs. This working note is part of a broader exploratory research line on metacontext-induced decision-boundary shifts, functional response classes, contradiction repair, and epistemic residue in LLM outputs.