Researchers recast the attention step in AI language models as particles moving under physics-style equations. From this view they proved when token values can blow up with depth, and designed small geometric tweaks that lowered validation loss in small-scale tests.
In deep language models, token values can grow layer by layer until the model becomes unstable. This work ties that growth to a precise, checkable depth — a mathematical early warning — plus small geometric tweaks that already lowered prediction loss in modest tests.
✦ Attention is particles in motion — the right math can keep them from running away.