Used Does Not Mean Needed: Self-Attention in Interleaved Flow-Matching Action Experts

Does a performance drop after deleting self-attention establish that an action expert needs it? We study flow-matching vision-language-action models whose action experts interleave separate cross-attention (CA) and self-attention (SA) modules. With CA retained, we distinguish deleting SA from a fixed dense-adapted policy from adapting a policy with SA disabled throughout. In GR00T N1.7 on LIBERO-10, dense inference, post-hoc deletion, and No-SA adaptation achieve 93.27%, 85.33%, and 93.60% success. In SmolVLA at two integration steps, their four-suite means are 76.40%, 23.84%, and 88.38% under matched adaptation configurations that differ only in the SA switch. Interventions in the fixed dense policies further show that dependence is conditional: early SA changes from a negative to a positive observed contribution when late SA is retained. This characterizes learned reliance without identifying the mechanism of successful adaptation. No-SA deployment reduces measured latency by 16.97% and 23.40% under the two models' respective timing protocols. Together, these results show that post-hoc dependence does not establish downstream architectural necessity in the studied CA–SA action experts.