← 研踪 PaperTrailarXiv:2606.09707 · v2
BrainSurgery: Reproducible and Flexible Declarative Weight Manipulations for Model Editing and Upcycling
首次提交 2026-06-09 00:26
版本更新 2026-10-28 16:29
雷达观测 2026-10-30 10:22
摘要
Checkpoint transformations such as model merging, precision casting, and low-rank rewriting are commonly implemented through Python code or separate task-specific tools. We present BrainSurgery, a tool that consolidates these operations into reusable declarative YAML plans over checkpoint tensors with built-in targeting, validation, diffing, and sharded export; a Web UI supports interactive inspection and plan construction. We compare BrainSurgery with three specialized systems across 13 settings, 11 benchmarks, 8 models, 4 architectures, and model sizes from 1.7B to 32B parameters. Without use-case-specific framework customization, BrainSurgery reproduces their model edits with comparable run cost, often lower memory usage, and close downstream performance. We further perform correctness, preservation, robustness, and scaling checks. Finally, a coding-agent study shows that agents can reliably generate and review BrainSurgery plans, which are substantially shorter and more structurally consistent than equivalent Python implementations. These results support BrainSurgery as a flexible alternative to maintaining separate tools for heterogeneous checkpoint-editing tasks.
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v2 · 2026-10-28 16:29 · 最新观测版本
BrainSurgery: Reproducible and Flexible Declarative Weight Manipulations for Model Editing and Upcycling
Checkpoint transformations such as model merging, precision casting, and low-rank rewriting are commonly implemented through Python code or separate task-specific tools. We present BrainSurgery, a tool that consolidates these operations into reusable declarative YAML plans over checkpoint tensors with built-in targeting, validation, diffing, and sharded export; a Web UI supports interactive inspection and plan construction. We compare BrainSurgery with three specialized systems across 13 settings, 11 benchmarks, 8 models, 4 architectures, and model sizes from 1.7B to 32B parameters. Without use-case-specific framework customization, BrainSurgery reproduces their model edits with comparable run cost, often lower memory usage, and close downstream performance. We further perform correctness, preservation, robustness, and scaling checks. Finally, a coding-agent study shows that agents can reliably generate and review BrainSurgery plans, which are substantially shorter and more structurally consistent than equivalent Python implementations. These results support BrainSurgery as a flexible alternative to maintaining separate tools for heterogeneous checkpoint-editing tasks.
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