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README.md
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By evolving Transformers as hierarchical GIN models, we reveal their implicit capacity for graph-level relational reasoning. This perspective suggests profound implications for foundational model development, enabling the design of architectures that dynamically adapt to both local and global dependencies. Applications in bioinformatics, materials science, language modeling, and beyond could benefit from this synthesis of relational and sequential data modeling, setting the stage for interpretable and generalizable modeling strategies.
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### Podcast
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<audio controls>
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<source src="https://huggingface.co/lamm-mit/Llama-3.2-3B-Instruct-Sparse-GIN-bio/resolve/main/Podcast_Isomorphic_Attention.mp3" type="audio/mpeg">
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By evolving Transformers as hierarchical GIN models, we reveal their implicit capacity for graph-level relational reasoning. This perspective suggests profound implications for foundational model development, enabling the design of architectures that dynamically adapt to both local and global dependencies. Applications in bioinformatics, materials science, language modeling, and beyond could benefit from this synthesis of relational and sequential data modeling, setting the stage for interpretable and generalizable modeling strategies.
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### Podcast about the paper generated using [PDF2Audio](https://huggingface.co/spaces/lamm-mit/PDF2Audio)
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<audio controls>
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<source src="https://huggingface.co/lamm-mit/Llama-3.2-3B-Instruct-Sparse-GIN-bio/resolve/main/Podcast_Isomorphic_Attention.mp3" type="audio/mpeg">
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