Information-Guided Frontier Decoding: Contextual Utility-Driven Commitment in dMLLMs

2026-08-27

AI Summary

提出信息引导前沿解码方法，通过考虑标记置信度、邻域不确定性和结构承诺风险来优化解码顺序，无需额外训练即能提高多模态语言模型的解码质量。

Abstract

Decoding quality in diffusion multimodal language models (dMLLMs) depends heavily on the order in which masked tokens are committed. Existing confidence-based strategies prioritize locally easy tokens, but confidence does not necessarily reflect contextual usefulness. As a result, structurally easy tokens such as punctuation may be committed before informative semantic anchors, weakening context propagation and increasing error accumulation. We propose Information-Guided Frontier Decoding (IGFD), a training-free decoding strategy that ranks candidates using token confidence, neighborhood uncertainty, and structural commitment risk. IGFD encourages early commitment of reliable semantic anchors while delaying fragile structural tokens, improving contextual support during decoding. A dynamic candidate frontier further constrains token selection to locally expandable regions under the same decoding budget. The method requires no additional training, auxiliary models, or extra forward passes. Experiments across multimodal understanding, reasoning, grounding, and hallucination benchmarks show that IGFD consistently outperforms existing decoding strategies across the majority of benchmarks and diffusion MLLM backbones under identical decoding budgets.

Problem

• diffusion multimodal language models
• decoding quality
• token commitment order
• contextual usefulness

Innovation

• Information-Guided Frontier Decoding
• contextual utility
• token confidence
• neighborhood uncertainty
• structural commitment risk