Modular Delta Merging with Orthogonal Constraints: A Scalable Framework for Continual and Reversible Model Composition

Researchers introduce Modular Delta Merging with Orthogonal Constraints (MDM-OC), a machine learning framework that enables multiple fine-tuned models to be merged, updated, and selectively removed without performance degradation or task interference. The approach uses orthogonal projections to prevent model conflicts and supports compliance requirements like GDPR-mandated data deletion.

MDM-OC addresses a fundamental challenge in modern ML systems: scaling model management without sacrificing performance or flexibility. As organizations deploy multiple specialized models for different tasks, managing their interactions becomes increasingly complex. Traditional merging approaches suffer from task interference and catastrophic forgetting, where integrating new models degrades performance on previous tasks. This framework solves that through orthogonal constraint projections, ensuring task-specific deltas operate in separate mathematical spaces.

The research builds on growing recognition that AI systems must become more modular and compliant. Regulatory pressures like GDPR create practical demands for model reversibility—the ability to remove specific training data's influence post-deployment. Current production systems lack principled mechanisms for this, forcing companies to choose between maintaining separate models (resource-intensive) or accepting irreversible changes. MDM-OC's structured unmerging capability directly addresses this gap.

For ML infrastructure and enterprise AI applications, this work has significant implications. Organizations managing multiple specialized models can now consolidate them more efficiently while maintaining individual task performance. The memory efficiency and computational tractability make the approach practical for production environments, not merely theoretical. This extends the lifecycle and flexibility of existing model investments.

The framework's importance increases as AI systems become more specialized and regulated. Future development will likely focus on scaling beyond current benchmarks and integrating with existing ML ops platforms. The reversibility feature particularly positions this as relevant for compliance-heavy sectors like finance and healthcare.

• →MDM-OC enables interference-free merging of multiple task-specific models using orthogonal constraint projections
• →The framework supports structured model unmerging for regulatory compliance without retraining
• →Performance remains stable across tasks through elastic weight consolidation and synthetic replay mechanisms
• →Approach is memory-efficient and computationally tractable for real-world deployment scenarios
• →Outperforms existing baselines on vision and NLP benchmarks for accuracy and backward transfer