The Platonic brain bridge hypothesis: human brain networks as an architectural prior for omni models

Abstract:We propose the Platonic brain bridge hypothesis: omni models, which process video, audio and text jointly like the brain, converge on brain-like representations, and the correspondence is bidirectional. From model to brain, brain-likeness of seven omni models is stable across participants, and our encoding models on their internal hidden states rank first on the Algonauts 2025 out-of-distribution leaderboard. From brain to model, three contributions follow. Brain-MoE gives seven cortical networks one brain-pretrained expert each and raises held-out accuracy in all 15 model-benchmark pairs by 6.42 percentage points on average. Brain-AVQA builds questions from video clips labelled by the most responsive brain network; the real network-to-expert map exceeds shuffled maps in-domain on all three models. Brain-Scope uses sparse autoencoders to localize the correspondence to a small subset whose removal weakens brain prediction in all three bases tested. Human brain networks are therefore a usable architectural prior for omni models.

Neurons and Cognition (q-bio.NC); Machine Learning (cs.LG)

arXiv:2609.10947 [q-bio.NC]

(or arXiv:2609.10947v1 [q-bio.NC] for this version)

https://doi.org/10.48550/arXiv.2609.10947 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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