[
    {
        "id": "osp-16322",
        "type": "article-journal",
        "title": "One Block, Multiple Depths: Recurrent Vision Transformers with Depth-Programmed Experts",
        "author": [
            {
                "family": "Bulat",
                "given": "Adrian"
            },
            {
                "family": "Ouali",
                "given": "Yassine"
            },
            {
                "family": "Tzimiropoulos",
                "given": "Georgios"
            }
        ],
        "URL": "https://omanscience.com/en/articles/one-block-multiple-depths-recurrent-vision-transformers-with-depth-programmed-experts",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "In this work, we show that a single Transformer block, applied recurrently, can match the accuracy of a full-depth vision encoder at comparable inference FLOPs without intermediate feature distillation. reViT restores depth-specific transformations by representing the FFN at each recurrent depth as a convex combination of a small shared expert bank. A continuous normalized-depth coordinate programs this mixture, defining a resampleable trajectory through FFN parameter space. We evaluate this design in two regimes: supervised ImageNet-1k training and distillation from a DINOv2 teacher. Across both regimes, controlled adaptations identify weight-space merging as the strongest tested MoE family at a matching one-FFN budget, ahead of the token-dispatch and output-mixture alternatives. Trained from scratch, reViT-B/16 attains DeiT III accuracy with about 70\\% fewer stored parameters. An 8-experts model distilled using only the teacher's output features retains nearly all of its DINOv2 teacher's linear-probe accuracy and transfers across classification, segmentation, and depth prediction. Elastic-depth training allows one checkpoint (trained model) to operate at multiple tested depths by resampling the same normalized coordinate interval. For fixed-depth deployment, the recurrent block can be materialized as a conventional dense graph, removing online routing and merging without changing the one-FFN-per-depth compute but expanding deployment storage."
    }
]