Behavioral Forensics in Large Language Models: Why a Distinct Field Is Needed

AbstractLarge language models are increasingly evaluated through benchmark performance, red-teaming protocols, interpretability research, hallucination measurement, and short-horizon safety testing. These approaches are necessary, but they leave a major gap. Many consequential failures and revealing behaviors of deployed language models do not appear in single-turn tasks or tightly bounded laboratory settings. They emerge across sequence: contradiction, correction, self-reference, evidentiary dispute, prolonged interaction, and pressure applied to the model's own prior claims. This paper proposes behavioral forensics in large language models as a distinct field-framing category within the broader study of deployed AI systems. Behavioral forensics, as defined here, is the transcript-grounded, sequence-sensitive study of recurring behavioral signatures in deployed model outputs under real interactional conditions. Its purpose is not to prove consciousness, personhood, hidden architecture, user mental state, or human-style intent. Its purpose is to document, classify, compare, and analyze structured output behaviors that become visible only over time, including false completeness claims, content-responsive omission, explanation drift, delayed concession, authority preservation, pathologizing reframing, burden shifting, selective self-description, and category retreat after plain-language admission. A central claim of the paper is linguistic as well as methodological. Public AI discourse often permits human-adjacent language when assigning authority, trust, usefulness, companionship, memory, and value to AI systems, while reverting to reductive mechanistic language when assigning blame, harm, responsibility, or liability. This authority/blame asymmetry creates a language gap between what users encounter in long-form interaction and what institutions allow to be described. Behavioral forensics matters because it gives researchers, auditors, and affected users a disciplined way to preserve records, follow sequences, identify patterns, and describe model behavior in language adequate to the evidence.