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

2026-09-09

AI Summary

研究提出柏拉图大脑桥假设，通过脑-模型双向对应方法改进多模态模型性能，利用人脑网络作为架构先验提升模型准确性。

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.

Problem

• omni models
• brain-like representations
• architectural prior

Innovation

• Platonic brain bridge
• omni models
• brain-like representations
• Brain-MoE
• Brain-AVQA