Beyond Fixed Biases: Decoding the Role of Reasoning Uncertainty in MLLM Modality Conflicts

Multimodal Large Language Models (MLLMs) must resolve conflicts when modalities provide contradictory information, a behavior we term "modality following". We propose a framework that decomposes this behavior into case-specific relative preference uncertainty and stable inherent preference. Across diverse MLLMs and benchmarks, the probability of following a modality consistently decreases as its relative preference uncertainty increases, a trend robust to alternative uncertainty indices. This regularity defines a "balance point'' where modality preferences are evenly matched, offering a capability-disentangled measure of modality bias. Layer-wise probing further shows that ambiguous cases near the balance point trigger middle-to-late-layer "concept oscillations," where top predictions vacillate between modality-supported answers. Finally, we demonstrate the framework's utility for preference steering through Supervised Fine-Tuning (SFT). We find that data efficiency is governed by preference uncertainty: training on easy samples (where one modality dominates) fails to generalize, whereas targeting the identified ``boundary cases" is essential for robust preference alignment and suppressing internal vacillation.