Beyond Encoder Accumulation: Measuring Encoder Roles in Multi-Encoder VLMs

Problem
This work addresses the lack of methodologies for understanding the interactions and contributions of diverse encoders in large vision-language models (LVLMs). As foundation models scale to integrate heterogeneous visual streams, the authors highlight the need for principled design approaches to identify effective encoder configurations prior to training. The study is presented as a preprint, indicating that it has not yet undergone peer review.

Method
The authors propose a systematic evaluation framework that involves retraining and assessing all 31 non-empty subsets of five common vision encoders using a unified pipeline, which required approximately 20,000 GPU-hours. They introduce a dual-axis decomposition of encoder contributions: Capacity, which measures the performance of an encoder in isolation, and Necessity, which quantifies the performance drop when an encoder is removed from the ensemble. The study also examines the effective rank of per-encoder pre-projectors to explain variations in performance at a fixed parameter count. This analysis reveals that the optimal pairing of encoders is not simply based on their individual capacities but also on their complementary roles during joint training.

Results
The findings indicate that the rankings of encoders differ significantly when retrained from scratch compared to those derived from masking encoders in a fixed checkpoint. Notably, the study identifies that pairing the two highest-Capacity encoders is suboptimal; instead, a combination of a high-Capacity anchor with an adaptive complement achieves performance comparable to the full five-encoder model. The results demonstrate that adding more encoders beyond this optimal pair yields only marginal improvements. The authors provide quantitative metrics, although specific performance numbers against named baselines on the Cambrian-1 benchmark suite are not detailed in the abstract.

Limitations
The authors acknowledge that their findings are based on a specific set of encoders and may not generalize to all LVLM architectures. Additionally, the study’s reliance on a single benchmark suite (Cambrian-1) may limit the applicability of the results across different tasks or datasets. The absence of a comparative analysis with existing methodologies for encoder selection is also noted as a potential gap.

Why it matters
This research has significant implications for the design of multi-encoder LVLMs, providing a framework that can guide the selection and configuration of encoders to optimize performance. The introduction of the Capacity-Necessity decomposition and the analysis of pre-projector ranks offers concrete primitives for future work in this area. By addressing a methodological gap in the understanding of encoder interactions, this study lays the groundwork for more effective and efficient LVLM architectures, as published in arXiv cs.AI.