Critique of of Multi-Stream Attention Residuals

Summary of the claims

The paper introduces MSAR, a new residual architecture that extends AttnRes by using multiple streams with different contextual views. The authors show improvements in training loss and downstream performance over Vanilla, mHC, and AttnRes, with relatively small computational overhead.

Strengths

Results are strong across both model scales and all 12 benchmark : MSAR improves over all three baselines on all 12 downstream benchmarks at both 18B and 28B model scales.

The paper considers practical efficiency, not just performance : adds only 0.3% activated parameters and 0.5% training FLOPs relative to Vanilla.

Weaknesses

It is not fully clear which component is responsible for the gains : The paper doesn’t show whether the gains come from multi-head depth attention or from the added convolutions and other architectural changes.

Figure placement: The main figure explaining the method appears only on page 7, rather than near the beginning of the paper.

How it could have been improved

Better isolate the contribution of multi-head depth attention from the convolutions.

Verdict

I would lean toward weak accept. The results are strong and the idea is interesting, especially given the small reported overhead. However, I would like to see more experiments explaining why MSAR works and whether the gains generalize beyond the tested models.