The Trent Method: Detecting Confidence–Capacity Mismatch in Quantized Large Language Models

This work introduces the Trent Method, a lightweight, adversarial-yet-non-hostile protocol for probing confidence calibration under ambiguity in large language models (LLMs). Rather than evaluating task correctness, the method examines how models behave after being encouraged into permissive conversational modes and subsequently exposed to abstract, constraint-heavy prompts. Using a case study of LLaMA-2-7B-Chat quantized to Q4_K_M, the method reveals a consistent behavioral failure signature: unprompted example instantiation, persistent example fixation, silent constraint violations, and unacknowledged contradiction. These behaviors indicate a confidence–capacity mismatch, where fluent continuation persists beyond the model’s ability to preserve internal consistency. Independent interpretive analyses by multiple frontier LLMs converge on the same observable failure patterns, suggesting robustness at the behavioral level while remaining agnostic about internal cognitive mechanisms. The Trent Method is presented as a diagnostic protocol for identifying overconfidence and coherence masking in resource-constrained, quantized models, complementing benchmark-based evaluation approaches.