Methods and Standards for Transcipt-Grounded Behavioral Forensics in Large Language Models

This paper presents a transcript-grounded methodology for behavioral forensic auditing inlarge language models. The protocol is designed to convert preserved interaction records intostructured evidence files through sequential review, direct quotation, pattern tagging,count-based logging, and standardized reporting. The method is observational andevidence-based. It does not attempt to infer model consciousness, intent, hidden architecture,or user mental state. Instead, it focuses on repeatable behavioral signatures in model outputsthat can be documented, cited, and compared across cases.The protocol emerged from repeated analytic instability in transcript review. Differentanalyses of the same interaction record produced sharply different contradiction counts,pattern definitions, and explanatory frames, often because the underlying evidence wassoftened by summary, abstraction, or interpretive drift. The method presented here wasdeveloped as a corrective to that problem. Its central aim is to preserve the evidentiary chain:retain the full transcript, identify bounded behavioral instances in model output, anchor eachinstance to direct quotation and citation, and organize the result into a report that can stand onits own as an evidence file.The paper defines the scope of the method, its unit of analysis, its preservationstandards, its audit procedure, and its reporting conventions. It also specifies the method'slimits. The protocol is stronger at documenting observable output behavior than at explaininginternal model causes. Its purpose is narrower than a general theory of model behavior. It is apractical audit framework for examining behavioral reliability, contradiction, reframing, andrelated interactional failure modes in preserved LLM transcripts.