Resolution Dynamics in Large-Scale Predictive Language Systems

This work investigates resolution dynamics in large-scale predictive language models (LLMs). While LLMs are often analyzed at the token level, many observed failures—such as hallucination, overconfidence, and premature certainty—emerge across extended interactions rather than individual predictions. The paper introduces a multi-level framework for understanding LLM behavior: Token-level prediction — local conditional probability estimation and fluency. Sequence-level resolution dynamics — path dependence, confidence accumulation, and premature hypothesis fixation across multi-turn interactions. Corpus-induced priors — how training data biases outputs toward fluent closure under uncertainty. We propose lightweight, architecture-agnostic protocols for observing these dynamics, including delayed commitment, context drift injection, uncertainty preservation, and acceptance signal amplification. By emphasizing interaction-level processes and human–system coupling, this study reframes hallucination as a systems-level phenomenon and provides insights for evaluating reliability in predictive language systems. This document is research-stage and intended for empirical observation and conceptual exploration of LLM behaviors.