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Life sciences · Preprint
arXiv · September 8, 2026
Early or partial results. Treat as a signal, not a conclusion.
Chimaera is a mixture-of-experts architecture for graph foundation models that combines multiple GFM architectures and language model embeddings to enable cross-task and cross-dataset learning. Empirical results on benchmark graph datasets show capability for transfer learning, but this is an unrefereed computational study with no clinical validation or comparison to established baselines.
Empirical benchmark evaluation on graph datasets. Six benchmark text-attributed graph datasets evaluated on node, link, and graph classification tasks.. Intervention: Chimaera mixture-of-experts architecture integrating multiple GFM architectures, graph prompts, linear GNN models, and language model embeddings..
Chimaera demonstrates effectiveness on same-task and cross-task experiments across node, link, and graph classification tasks Linear GNNs show strong cross-task transferability despite architectural simplicity Both large and small language models are needed to generate embeddings for expert training
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This is an early-stage architecture paper presenting a novel computational approach with empirical validation on benchmark datasets, but lacking clinical outcomes, real-world deployment data, or peer review.
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Designing foundation models for graphs is challenging due to the irregular structure of graphs and the different sizes and characteristics of embeddings. Chimaera integrates mixture-of-experts with graph foundation models (GFM). It integrates different GFM architectures, such as graph prompts and linear GNN models. Large language models are used to generate embeddings, and experts can be trained and combined following different strategies, GFMs, embeddings, etc. Furthermore, Chimaera extends existing linear GNNs to support link-level and graph-level tasks in addition to node-level tasks. Empirical analyses are performed on same-task and cross-task experiments with node, link, and graph classification tasks using six benchmark text-attributed graph datasets. The experiments demonstrate the effectiveness of Chimaera and its capabilities for transfer across tasks and datasets. Further insights include the need to use both large and small language models to generate embeddings for the experts, a strong cross-task transferability of simple but effective linear GNNs, and using few samples only to provide strong results.
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