LLM-Condition / Research-Result Boundary

A protocol-facing boundary note on how research results produced with, through, or about a large language model depend on the execution conditions under which the model was accessed and used. The note distinguishes model name from model condition, prompt disclosure from full instruction context, LLM output from validated research result, and readable output from auditable, interpretable, and methodologically bounded evidence. The note treats LLM-condition and research-result boundary failure not as an argument against LLM-mediated research, a claim that commercial systems are unusable, a demand for complete computational reproducibility, or a general judgment that model-generated outputs are invalid, but as a structural condition in which a reported result becomes detached from the model version, provider, access mode, interface, system instructions, prompt structure, configuration, memory state, input material, post-processing, validation, automation degree, and other conditions that materially shaped its production. This work is theoretical and conceptual in nature. It does not propose empirical hypotheses, causal claims, diagnostic criteria, universal disclosure requirements, reproducibility standards, model-evaluation benchmarks, prompt-engineering methods, validation thresholds, reporting mandates, compliance procedures, audit checklists, certification systems, or technical workflows. The note is articulated as a descriptive methodological boundary rather than as a complete reporting protocol, platform-specific tutorial, reproducibility standard, research-governance framework, or operational method for determining whether an LLM-mediated result is valid. Although developed within Meta-Writing Ecology, LLM-Condition / Research-Result Boundary is presented here as an external-facing conceptual object. This registration serves as a stable, citable anchor for Version 0.1 of the note, intended to support structural analysis of model conditions, execution fields, auditability, reproducibility boundaries, validation status, model-mediated evidence, and the non-equivalence between a broad model label or visible output and a methodologically interpretable research result, without constraining subsequent theoretical development or applied interpretation.