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The MAGMaR 2026 shared task demonstrates significant progress in multimodal systems, with all submitted video retrieval models outperforming last year's baseline and every generation team producing reports validated as best by human annotators. This outcome highlights the maturity of current architectures in handling complex video-to-text grounding tasks within computer vision and natural language processing domains. The results suggest that the field has moved past basic feasibility, establishing a new performance floor for multimodal augmented generation systems.
Findings of the MAGMaR 2026 Shared Task
arXiv:2606.12295v1 Announce Type: cross Abstract: This overview paper presents the results of the shared task for the second workshop on Multimodal Augmented Generation via Multimodal Retrieval (MAGMaR). In this shared task participants submitted systems focused on either (i) video retrieval or (ii) grounded generation of articles given retrieved videos. Teams could submit to either task. For the retrieval task, we had 2 participating teams that submitted a total of 17 systems -- all of which beat a baseline derived from the winner of last year's shared task. On the generation side, we had 4 teams submit 16 systems. All teams had at least one generated report that was labeled the best by a human annotator.
Abstract:This overview paper presents the results of the shared task for the second workshop on Multimodal Augmented Generation via Multimodal Retrieval (MAGMaR). In this shared task participants submitted systems focused on either (i) video retrieval or (ii) grounded generation of articles given retrieved videos. Teams could submit to either task. For the retrieval task, we had 2 participating teams that submitted a total of 17 systems -- all of which beat a baseline derived from the winner of last year's shared task. On the generation side, we had 4 teams submit 16 systems. All teams had at least one generated report that was labeled the best by a human annotator.
Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL); Information Retrieval (cs.IR)
arXiv:2606.12295 [cs.CV]
(or arXiv:2606.12295v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.12295 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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