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

2026-06-08

AI Summary

This work addresses the lack of reliable and reproducible tools for managing weights in large-scale deep learning models, a gap often filled by fragile ad-hoc scripts. The authors propose BrainSurgery, the first framework to introduce declarative programming into model editing, enabling users to specify tensor surgery operations via YAML configuration files. By leveraging regular expressions for precise parameter targeting, BrainSurgery supports structured transformations such as reshaping, low-rank decomposition, and precision conversion. The system incorporates built-in assertions to guarantee correctness and has been validated on tasks including model upgrading and LoRA extraction. Experimental results demonstrate that BrainSurgery significantly enhances the reliability, reproducibility, and efficiency of complex weight manipulation workflows.

Abstract

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.

Problem

• model editing
• weight manipulation
• checkpoint management
• reproducibility
• tensor surgery

Innovation

• declarative weight manipulation
• tensor surgery
• model editing
• reproducible transformation
• checkpoint upcycling