Adapting vision foundation models to medical imaging with LoRA has become routine, but choosing which backbone to use is usually based only on final task metrics. A new arXiv preprint (2610.28255) takes issue with that assumption, arguing that backbones with similar performance are not necessarily equivalent.
The paper's title—"Convergent Performance, Divergent Mechanisms"—signals its central finding: models that score alike on brain MRI tasks may still rely on different internal strategies. The abstract notes that model selection currently treats these backbones as if they were the same, a practice the work appears to challenge.
If the finding holds, it would mean that performance benchmarks alone give an incomplete picture of a model's behavior. Understanding the mechanisms behind a prediction could matter for reliability, interpretability, and future adaptation in medical settings. The full paper is not yet available in the abstract, but the stated direction is clear.