Not All Answers Are Contextually Persuadable: Inference Dynamics in Large Language Models under Contextual Influence

2026-10-03

AI Summary

This study addresses the unclear internal reasoning dynamics of large language models under strong contextual influence. Moving beyond conventional output-layer analyses, this work establishes a theoretical framework grounded in internal representations, integrating quantitative analysis with empirical validation to systematically characterize how context constrains reasoning processes. The findings reveal that predictive representations converge toward stable states, repeated assertions do not accumulate evidential weight, and predictive shifts are confined within query-dependent stable regions. These theoretical predictions align closely with observed behaviors, elucidating the intrinsic boundaries of contextual influence. Ultimately, this research provides both principled pathways and practical guidance for delineating the fundamental limits of context-driven reasoning in large language models.

Abstract

At the core of modern prompting techniques is contextual sensitivity, the ability of large language models to adapt their predictions based on inference-time context. Despite its central role, inference behavior under strong contextual influence remains poorly understood, particularly at the level of internal inference dynamics. We introduce a theoretical framework for analyzing contextual influence through inference dynamics, enabling quantitative characterization of inference behavior beyond output-level answer changes. Our analysis shows that inference dynamics do not exhibit unbounded drift under repeated contextual assertions. Instead, predictive representations converge to stable, query-dependent regimes that fundamentally constrain whether contextual signals can alter a model's prediction. This leads to a surprising finding: Repeated contextual assertions do not act as accumulating evidence during inference and may therefore fail to alter a model's prediction even under unbounded repetition, while in other cases a prediction change becomes inevitable. We empirically validate our theoretical predictions, demonstrating strong alignment between theory and observed inference behavior. These contributions offer a principled pathway toward characterizing the limits of contextual influence during inference, providing practical implications for model development.

Problem

• Large Language Models
• Contextual Influence
• Inference Dynamics
• Prompting
• Predictive Representations

Innovation

• Inference Dynamics
• Contextual Influence
• Large Language Models
• Theoretical Framework
• Predictive Representations