TraceAI: A Reproducible Framework for Behavioral Analysis of Local Language Models

TraceAI is a reproducible, local-first framework for behavioral analysis of local language models. The framework connects model configurations, prompts, raw outputs, evaluation rules, metrics, and execution metadata into an evidence-linked research workflow. This work presents the TraceAI framework and a preliminary evaluation using local Qwen2.5 instruction models. The study investigates behavioral characteristics including output-contract compliance, consistency, and constraint-sensitive behavior across repeated controlled executions. Rather than reducing model behavior to a single performance score, TraceAI preserves the evidence required to interpret individual observations and compare behavioral profiles across experimental conditions. The work also establishes a research protocol for future evaluation of intermediate training and fine-tuning checkpoints, including the study of behavioral improvement, plateau, and regression during model development. The current study is preliminary and does not claim statistically validated behavioral fingerprints or longitudinal training-time conclusions. These remain directions for future experimental work.