Isolate the contributions of routing, fusion, and encoder diversity

Determine which component of the dual-encoder Mixture of Layers configuration—intermediate-layer routing, the CLIP–DINOv2 fusion mechanism, or vision-encoder choice—causes the observed performance gains by evaluating a matched no-router model with the same fusion procedure, training schedule, and checkpoint-selection rule.

Background

The paper evaluates a dual-encoder configuration that independently applies hybrid layer routing to CLIP and DINOv2 with Txt, then upsamples, concatenates, and projects the routed representations. This system outperforms an interleaved multi-encoder baseline on some fine-grained benchmarks, particularly V* and GPT4V-hard, but the comparison changes several factors simultaneously: the routing strategy, the encoder-fusion mechanism, and potentially the effective visual representations available to the LLM.

Because the reported comparison is between complete systems rather than a controlled routing-only ablation, the paper does not identify whether the gains arise from dynamic intermediate-layer selection, the specific fusion design, the use of two different vision encoders, or interactions among these components. The authors therefore identify a matched no-router comparison as necessary to isolate the source of the improvement.

References

However, this comparison does not establish which component causes the gains. A matched no-router model with the same fusion, training schedule, and checkpoint-selection rule is needed to isolate routing from fusion and encoder choice.

— Mixture of Layers: Dynamic Layer Routing for Visual Reasoning  (2610.09440 - Kim et al., 7 Oct 2026) in Section 4, subsection “Mixture of Layers with Multiple Vision Encoders,” paragraph “Interpretation and Insights”