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[2602.18628] Non-Interfering Weight Fields: Treating Model Parameters as a Continuously Extensible Function
Summary
The paper introduces Non-Interfering Weight Fields (NIWF), a novel framework that allows neural networks to extend capabilities without forgetting previously learned tasks, addressing catastrophic forgetting in large language models.
Why It Matters
This research is significant as it proposes a solution to catastrophic forgetting, a persistent challenge in machine learning. By enabling models to retain knowledge while learning new tasks, NIWF could enhance the adaptability and efficiency of AI systems, making them more robust in dynamic environments.
Key Takeaways
- NIWF replaces fixed weight vectors with a learned function for dynamic weight configuration.
- The framework allows for zero forgetting on committed tasks while maintaining competitive performance on new tasks.
- NIWF introduces software-like versioning for neural networks, facilitating task management without retraining.
Computer Science > Machine Learning arXiv:2602.18628 (cs) [Submitted on 20 Feb 2026] Title:Non-Interfering Weight Fields: Treating Model Parameters as a Continuously Extensible Function Authors:Sarim Chaudhry View a PDF of the paper titled Non-Interfering Weight Fields: Treating Model Parameters as a Continuously Extensible Function, by Sarim Chaudhry View PDF HTML (experimental) Abstract:Large language models store all learned knowledge in a single, fixed weight vector. Teaching a model new capabilities requires modifying those same weights, inevitably degrading previously acquired knowledge. This fundamental limitation, known as catastrophic forgetting, has resisted principled solutions for decades. Existing approaches treat weights as immutable artifacts that must be protected through techniques like regularization heuristics, replay buffers, or isolated adapter modules. The problem is none of these provide a structural guarantee against forgetting. In this work, we propose Non-Interfering Weight Fields (NIWF), a framework that replaces the fixed weight paradigm with a learned function that generates weight configurations on demand from a continuous capability coordinate space. After training on a task, we commit the occupied coordinate region by snapshotting the fields outputs on anchor points to enforce a functional lock during all future training. We validate NIWF on sequential instructionfollowing and code generation tasks using Mistral-7B, demonstrating zero forget...
