The Platonic brain bridge hypothesis: human brain networks as an architectural prior for multimodal large language models

Pengfei Zhang; Biao Tian; Xiangang Li; Li Liu

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arXiv · 2609.10947

The Platonic brain bridge hypothesis: human brain networks as an architectural prior for multimodal large language models

Pengfei Zhang·Biao Tian·Xiangang Li·Li Liu

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Abstract

Multimodal large language models predict brain activity, but brain alignment has been a measurement, not a design tool. We propose the Platonic brain bridge hypothesis: omni models, multimodal large language models that process video, audio and text jointly, converge on brain-like representations usable in both directions. From model to brain, brain-likeness of seven omni models is stable across participants, rises with every input channel in three bases, and our encoders lead the Algonauts 2025 out-of-distribution leaderboard. From brain to model, Brain-MoE fixes the expert partition of a frozen base to the seven networks of human cortex, trains experts on network-labelled Brain-AVQA questions, raises held-out accuracy in all 15 model-benchmark pairs by 6.42 percentage points on average and exceeds capacity-matched random experts in 14. Brain-Scope localizes the correspondence to sparse features whose removal weakens brain prediction. Human brain organization is therefore a usable architectural prior for multimodal large language models.

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Pengfei Zhang, Biao Tian, Xiangang Li, Li Liu. 2026-09-14. The Platonic brain bridge hypothesis: human brain networks as an architectural prior for multimodal large language models. https://arxiv.org/abs/2609.10947

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