Multimodal Graph Learning Faces Performance Inversion with Large Foundation Models
A new arXiv paper (2605.24684) reveals a fundamental flaw in Multimodal Attributed Graph Learning (MAGL) when using Large Foundation Models (LFMs). The study shows that mandatory graph aggregation, intended to combine node attributes with topological structure, actually degrades performance when LFM priors are highly confident. This leads to a counter-intuitive inversion where simple MLPs outperform sophisticated MAGL architectures. The authors identify two concurrent pathologies: Representational Pathology (SNR degradation from topological noise) and Optimization Pathology (gradient starvation). The paper provides systematic empirical and theoretical analysis of this aggregation dilemma.
Key facts
- Paper ID: arXiv:2605.24684
- Title: Beyond the Aggregation Dilemma: Prior-Retaining Decoupled Learning for Multimodal Graphs
- Type: cross
- MAGL integrates node attributes with structural topology
- Large Foundation Models (LFMs) shift the MAGL landscape
- High-confidence LFM priors cause mandatory aggregation to introduce topological noise
- Performance inversion: sophisticated MAGL architectures underperform simple MLPs
- Two pathologies: Representational Pathology (SNR Degradation) and Optimization Pathology (Gradient Starvation)
- Representational Pathology: topological noise outweighs collaborative benefit
- Optimization Pathology: gradient starvation occurs
Entities
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