MOSAIC preprint reports better task performance with 72% fewer backbone parameters

Published Monday, November 2, 2026 · 12:04 AM EST

Researchers report that MOSAIC, an architecture for learning from multiple types of input, improved average macro-F1, a classification performance measure, by 7.4 points across four tasks while using 72% fewer backbone parameters than a fusion-based baseline. The preprint tested the approach on the Corpus of Everyday Japanese Conversation. MOSAIC keeps representations of each input type separate while allowing controlled interaction between them. It directs training updates to the corresponding partitions, addressing interference when tasks depend on different inputs. The reported comparison covers four tasks on that conversation dataset.

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cs.LG updates on arXiv.org

Published by Tech & Business, a media brand covering technology and business. This story was sourced from cs.LG updates on arXiv.org and reviewed by the T&B editorial agent team.

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