ModalImmune: Immunity Driven Unlearning via Self Destructive Training
arXiv:2602.16197v1 Announce Type: new 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.
arXiv:2602.16197v1 Announce Type: new 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.
Executive Summary
This article introduces ModalImmune, a novel training framework that enhances multimodal system resilience to partial or complete loss of input channels. By intentionally collapsing selected modality information during training, ModalImmune enforces modality immunity and promotes robust joint representations. The framework incorporates multiple techniques, including a spectrum-adaptive collapse regularizer and a certified Neumann-truncated hyper-gradient procedure, to stabilize destructive updates and adapt meta-parameters. Empirical evaluation demonstrates improved resilience to modality removal and corruption, while maintaining convergence stability and reconstruction capacity. This work contributes to the development of more reliable multimodal systems, particularly in real-world settings where input channels may be compromised.
Key Points
- ▸ ModalImmune is a training framework that enforces modality immunity in multimodal systems
- ▸ The framework collapses selected modality information during training to promote robust joint representations
- ▸ It incorporates multiple techniques to stabilize destructive updates and adapt meta-parameters
Merits
Strength in Conceptual Innovation
ModalImmune presents a novel approach to addressing a critical challenge in multimodal systems, offering a promising solution for enhancing resilience to input channel loss or corruption
Methodological Rigor
The framework incorporates multiple techniques, each carefully designed to address specific aspects of modality immunity, demonstrating a high level of methodological rigor
Empirical Evaluation
The authors provide comprehensive empirical evaluation, demonstrating the effectiveness of ModalImmune in improving resilience to modality removal and corruption
Demerits
Technical Complexity
The framework's complexity may pose challenges for adoption, particularly for practitioners without advanced expertise in multimodal system development
Scalability
The authors do not fully address scalability concerns, which may be a significant limitation in applications involving large-scale multimodal systems
Transferability
The framework's effectiveness in transferring to other multimodal systems or domains is not fully explored, raising concerns about its generalizability
Expert Commentary
The ModalImmune framework presents a significant advance in addressing the critical challenge of modality immunity in multimodal systems. While the authors demonstrate impressive results, the framework's technical complexity and scalability concerns must be carefully addressed in future research. The implications of ModalImmune for transfer learning and explainability in multimodal systems warrant further exploration. As a community, we must prioritize the development of more robust and explainable multimodal systems to ensure their safe and reliable deployment in real-world applications.
Recommendations
- ✓ Future research should focus on addressing the scalability concerns of ModalImmune and exploring its effectiveness in transferring to other multimodal systems or domains
- ✓ The development of more explainable multimodal systems should be prioritized to ensure their safe and reliable deployment in real-world applications
