Mochi: Aligning Pre-training and Inference for Efficient Graph Foundation Models via Meta-Learning
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| Format: | Preprint |
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2026
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| _version_ | 1866909021094543360 |
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| author | Mattos, João Silva, Arlei |
| author_facet | Mattos, João Silva, Arlei |
| contents | 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 objective with inference rather than relying on a post-hoc unification step. We show that Mochi, along with its more powerful variant Mochi++, achieves competitive or superior performance compared to existing Graph Foundation Models across 25 real-world graph datasets spanning node classification, link prediction, and graph classification, while requiring 8$\sim$27 times less training time than the strongest baseline. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_22031 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Mochi: Aligning Pre-training and Inference for Efficient Graph Foundation Models via Meta-Learning Mattos, João Silva, Arlei Machine Learning Artificial Intelligence We propose Mochi, a Graph Foundation Model that addresses task unification and training efficiency by adopting a meta-learning based training framework. Prior models pre-train with reconstruction-based objectives such as link prediction, and assume that the resulting representations can be aligned with downstream tasks through a separate unification step such as class prototypes. We demonstrate through synthetic and real-world experiments that this procedure, while simple and intuitive, has limitations that directly affect downstream task performance. To address these limitations, Mochi pre-trains on few-shot episodes that mirror the downstream evaluation protocol, aligning the training objective with inference rather than relying on a post-hoc unification step. We show that Mochi, along with its more powerful variant Mochi++, achieves competitive or superior performance compared to existing Graph Foundation Models across 25 real-world graph datasets spanning node classification, link prediction, and graph classification, while requiring 8$\sim$27 times less training time than the strongest baseline. |
| title | Mochi: Aligning Pre-training and Inference for Efficient Graph Foundation Models via Meta-Learning |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2604.22031 |