[Paper] BrainSurgery: Reproducible and Reliable Declarative Weight Manipulations for Model Editing and Upcycling

Source: arXiv - 2606.09707v1

Overview

As deep learning models scale, managing, inspecting, and modifying large checkpoints has become increasingly challenging. Researchers often need to alter model weights for layer restructuring, precision casting, low-rank factorization, and architectural debugging, yet these workflows often rely on fragile ad-hoc Python scripts. Here, we introduce BrainSurgery, a tool for robust and reproducible “tensor surgery” on neural network checkpoints, and provide a system demonstration covering four examples and three case studies from model upcycling to LoRA extraction. By abstracting storage formats and memory management, BrainSurgery executes complex transformations through declarative YAML plans. It supports structural modifications, mathematical transformations, and tensor reshaping through expressive regex and structural targeting, while built-in assertions validate tensor shapes, data types, and values to prevent silent errors. We envision that BrainSurgery will provide a strong foundation for future research through its reproducible and validated operations.

Key Contributions

This paper presents research in the following areas:

• cs.LG
• cs.CL

Methodology

Please refer to the full paper for detailed methodology.

Practical Implications

This research contributes to the advancement of cs.LG.

Authors

• Gianluca Barmina
• Annemette Broch Pirchert
• Andrea Blasi Núñez
• Lukas Galke Poech
• Peter Schneider-Kamp

Paper Information

• arXiv ID: 2606.09707v1
• Categories: cs.LG, cs.CL
• Published: June 8, 2026
• PDF: Download PDF