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Information Routing in Atomistic Foundation Models: How Equivariance Creates Linearly Disentangled Representations

arXiv – CS AI|Joshua Steier|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers introduce Composition Projection Decomposition (CPD) to analyze how atomistic foundation models organize information in their representations. The study finds that tensor product equivariant architectures like MACE create linearly disentangled representations where geometric information is easily accessible, while handcrafted descriptors entangle information nonlinearly.

Key Takeaways

→CPD method uses QR projection to linearly remove composition signals and analyze geometric residuals in AI model representations.
→Tensor product equivariant architectures (MACE) achieve superior linear disentanglement with R² = 0.782 for HOMO-LUMO gap predictions.
→MACE routes target-specific signals through specific irreducible representation channels in a systematic pattern.
→Linear probes are recommended over gradient boosted trees for representation analysis due to systematic inflation issues.
→Linearly disentangled representations offer improved sample efficiency advantages beyond raw prediction accuracy.

#ai-research #foundation-models #machine-learning #representation-learning #equivariant-networks #molecular-modeling #arxiv

Read Original →via arXiv – CS AI

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