MODIX: A Training-Free Multimodal Information-Driven Positional Index Scaling for Vision-Language Models

arXiv cs.CV / 4/15/2026

📰 NewsSignals & Early TrendsIdeas & Deep AnalysisModels & Research

共有:

Key Points

The paper argues that current vision-language transformer positional encoding assigns indices uniformly, which can waste attention on redundant visual regions and under-allocate it to informative content.
It introduces MODIX, a training-free framework that adapts positional strides using modality-specific information density rather than changing model parameters or architecture.
MODIX estimates intra-modal density via covariance-based entropy and models inter-modal relationships via cross-modal alignment, combining both into unified scoring for positional rescaling.
Experiments across multiple VLM architectures and benchmarks show consistent gains in multimodal reasoning and more task-dependent reallocation of attention.
The authors conclude that positional encoding should be treated as an adaptive resource for multimodal transformer sequence modeling.

Abstract

Vision-Language Models (VLMs) have achieved remarkable progress in multimodal understanding, yet their positional encoding mechanisms remain suboptimal. Existing approaches uniformly assign positional indices to all tokens, overlooking variations in information density within and across modalities, which leads to inefficient attention allocation where redundant visual regions dominate while informative content is underrepresented. We identify positional granularity as an implicit resource and propose MODIX (Multimodal Information-Driven Positional IndeX Scaling), a training-free framework that dynamically adapts positional strides based on modality-specific contributions. MODIX jointly models intra-modal density via covariance-based entropy and inter-modal interaction via cross-modal alignment to derive unified scores, which rescale positional indices to allocate finer granularity to informative modalities while compressing redundant ones, without requiring any modification to model parameters or architecture. Experiments across diverse architectures and benchmarks demonstrate that MODIX consistently improves multimodal reasoning and adaptively reallocates attention according to task-dependent information distributions, suggesting that positional encoding should be treated as an adaptive resource in Transformers for multimodal sequence modeling.