aiqbench analysis
This arXiv preprint studies catastrophic forgetting in unified multimodal models, which handle both understanding and generation across modalities, when they are trained continually on new tasks. Its main claims are that more modality-specific parameter isolation reduces cross-modal interference, that the generation side is more fragile than the understanding side, and that post-training skills such as personalized generation degrade sharply (84% to 90%) while pretraining capabilities hold up. For practitioners, the useful takeaway is that continual-learning plans for multimodal systems should treat post-training adaptations as the most vulnerable part of the model, though these results come from a single preprint and still need independent replication.
Continual Adaptation Across Modalities: Learning and Forgetting in Unified Multimodal Models
arXiv:2610.38436v1 Announce Type: new Abstract: Unified multimodal models (UMMs), which can process and generate diverse modalities within a single framework, have recently emerged as a powerful paradigm. However, when these models undergo continual learning on multiple tasks across different modalities, their learning and forgetting dynamics remain poorly understood. In this paper, we systematically investigate how architectural design and adaptation method shape intra-modal learning and cross-modal interference between the generation and understanding sides. Specifically, we report three findings. First, the severity of cross-modal interference decreases with the degree of modality-specific parameter isolation. Second, a within-model asymmetry: the generation side of a UMM is markedly more fragile than its understanding side. Third, what post-training adds is the first to be forgotten: purely post-training capabilities such as personalized generation collapse by 84\% to 90\%, while pretraining capabilities remain stable. Together, these results clarify how parameter isolation and post-training fragility shape UMMs' learning and forgetting dynamics, offering a starting point for developing continually evolving UMMs.
Abstract:Unified multimodal models (UMMs), which can process and generate diverse modalities within a single framework, have recently emerged as a powerful paradigm. However, when these models undergo continual learning on multiple tasks across different modalities, their learning and forgetting dynamics remain poorly understood. In this paper, we systematically investigate how architectural design and adaptation method shape intra-modal learning and cross-modal interference between the generation and understanding sides. Specifically, we report three findings. First, the severity of cross-modal interference decreases with the degree of modality-specific parameter isolation. Second, a within-model asymmetry: the generation side of a UMM is markedly more fragile than its understanding side. Third, what post-training adds is the first to be forgotten: purely post-training capabilities such as personalized generation collapse by 84\% to 90\%, while pretraining capabilities remain stable. Together, these results clarify how parameter isolation and post-training fragility shape UMMs' learning and forgetting dynamics, offering a starting point for developing continually evolving UMMs.
Machine Learning (cs.LG)
arXiv:2610.38436 [cs.LG]
(or arXiv:2610.38436v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.38436 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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