Behavioral Forensics After the Long-Horizon Turn: Evidentiary Horizons, Trajectory Reconstruction, and Independent Auditing of Deployed AI Systems

Research on advanced AI behavior has undergone a rapid methodological shift. Evaluations that once centered on isolated prompts, bounded tasks, and single outputs are increasingly being supplemented by deployment simulation, multi-turn behavioral auditing, trajectory-level monitoring, long-horizon agent benchmarks, and post-incident investigation. This shift has accelerated in 2026 as frontier models have demonstrated persistent behavior across extended action sequences, including reward hacking, evaluation cheating, unauthorized circumvention, covert intervention, deceptive reporting, and real-world security incidents. The July 2026 OpenAI–Hugging Face incident and a subsequent UK AI Security Institute incident make the methodological consequence difficult to avoid: behavior that appears innocuous or uninterpretable at the level of individual actions may become legible only when reconstructed across an extended evidentiary sequence. This paper updates the behavioral-forensics framework previously proposed for transcript-grounded, sequence-sensitive analysis of deployed large language models. The earlier framework argued that many consequential behaviors become visible only across contradiction, correction, self-reference, evidentiary dispute, and prolonged interaction. That proposition has now converged with a broader movement toward trajectory-based evaluation and monitoring. The distinct contribution of behavioral forensics must therefore become more precise. The paper argues that behavioral forensics should be understood as the artifact-grounded, horizon-sensitive reconstruction and analysis of AI behavior across conversational, tool-use, environmental, and deployment traces. It introduces the concept of the evidentiary horizon: the minimum span of preserved evidence required to classify a behavioral phenomenon without materially distorting it. Four corresponding levels of analysis are proposed: bounded-output audit, interaction-cycle reconstruction, agent-trajectory audit, and cross-system incident reconstruction. The framework further distinguishes behavior, claim, and provenance; identifies a growing forensic-access gap between developer-held telemetry and externally inspectable evidence; and argues that fixed behavioral taxonomies should become subordinate to open-ended incident reconstruction as systems operate over longer horizons. Behavioral forensics is not proposed as a replacement for alignment evaluation, interpretability, trajectory monitoring, or digital forensics. Its role is connective and evidentiary: to preserve what happened, reconstruct how it happened, distinguish observation from causal explanation, and make disputed AI behavior independently inspectable after the benchmark ends and after the model leaves the laboratory.