Assess how private residual communication scales with model size
Determine how much residual-stream capacity remains available for private communication channels in transformer models as model scale increases, and whether the available free subspace shrinks with scale.
References
The second is cheap for a reason worth generalizing, since the space a trained model operates in occupies a modest fraction of its width, leaving room for channels that collide with nothing. How much room, and whether it shrinks with scale, is left for future work.
A data-driven estimate of $\lambda_{\mathrm{occ}}$, $\lambda_{\mathrm{spare}}$, and $f_{\mathrm{occ}}$ from the readout-layer covariance spectrum would tighten this bound; we leave this to future work.
First, we evaluate models up to 27B parameters ($d{=}5120$); production-scale models (70B+, $d \geq 8192$) further increase the processing gain (Appendix~\ref{app:commtheory}) and are expected to be more vulnerable, but this remains unverified.