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[2602.16197] ModalImmune: Immunity Driven Unlearning via Self Destructive Training
Summary
The paper presents ModalImmune, a training framework designed to enhance the resilience of multimodal systems against input channel loss by employing self-destructive training techniques.
Why It Matters
As multimodal systems are increasingly deployed in real-world applications, their reliability is crucial. ModalImmune addresses the challenge of modality loss, ensuring models remain robust and effective even when faced with corrupted or missing data. This research contributes to the ongoing development of more resilient AI systems, making it relevant for both academic and practical applications in machine learning.
Key Takeaways
- ModalImmune improves model resilience to modality loss.
- The framework utilizes a spectrum-adaptive collapse regularizer for robust training.
- Empirical evaluations show enhanced convergence stability and reconstruction capacity.
- The approach is significant for real-world applications of multimodal systems.
- It combines advanced techniques like curvature-aware gradient masking and hyper-gradient procedures.
Computer Science > Machine Learning arXiv:2602.16197 (cs) [Submitted on 18 Feb 2026] Title:ModalImmune: Immunity Driven Unlearning via Self Destructive Training Authors:Rong Fu, Jia Yee Tan, Wenxin Zhang, Zijian Zhang, Ziming Wang, Zhaolu Kang, Muge Qi, Shuning Zhang, Simon Fong View a PDF of the paper titled ModalImmune: Immunity Driven Unlearning via Self Destructive Training, by Rong Fu and 8 other authors View PDF HTML (experimental) Abstract:Multimodal systems are vulnerable to partial or complete loss of input channels at deployment, which undermines reliability in real-world settings. This paper presents ModalImmune, a training framework that enforces modality immunity by intentionally and controllably collapsing selected modality information during training so the model learns joint representations that are robust to destructive modality influence. The framework combines a spectrum-adaptive collapse regularizer, an information-gain guided controller for targeted interventions, curvature-aware gradient masking to stabilize destructive updates, and a certified Neumann-truncated hyper-gradient procedure for automatic meta-parameter adaptation. Empirical evaluation on standard multimodal benchmarks demonstrates that ModalImmune improves resilience to modality removal and corruption while retaining convergence stability and reconstruction capacity. Comments: Subjects: Machine Learning (cs.LG); Computation and Language (cs.CL); Multimedia (cs.MM) Cite as: arXiv:2602.1619...
