Mochi: Aligning Pre-training and Inference for Efficient Graph Foundation Models via Meta-Learning
We propose Mochi, a Graph Foundation Model that addresses task unification and training efficiency by adopting a meta-learning based training framework. Prior models pre-train with reconstruction-based objectives such as link prediction, and assume that the resulting representations can be aligned with downstream tasks through a separate unification step such as class prototypes. We demonstrate through synthetic and real-world experiments that this procedure, while simple and intuitive, has limitations that directly affect downstream task performance. To address these limitations, Mochi pre-trains on few-shot episodes that mirror the downstream evaluation protocol, aligning the training o...
| Authors | João Mattos, Arlei Silva |
|---|---|
| Primary category | cs.LG |
| Published | 23 April 2026 |
| Last updated at source | 7 May 2026 |
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