Editing Models via Task Vectors is a model modification framework that decomposes fine-tuned model knowledge into portable, composable vectors — enabling transfer, removal, and combination of learned behaviors by manipulating these vectors in weight space.

Key Operations

• Extraction: $ au = heta_{fine} - heta_{pre}$ (extract what fine-tuning learned).
• Transfer: Apply $ au$ from model $A$ to model $B$: $ heta_B' = heta_B + au_A$.
• Forgetting: $ heta' = heta_{fine} - lambda au$ (partially undo fine-tuning for selective forgetting).
• Analogy: If $ au_{EN ightarrow FR}$ maps English→French, apply it to other models for similar translation ability.

Why It Matters

• Modular ML: Neural network capabilities become modular, composable units.
• Efficient Transfer: Transfer specific capabilities without full fine-tuning.
• Debiasing: Remove biased behavior by subtracting the corresponding task vector.

Editing via Task Vectors is modular surgery for neural networks — extracting, transplanting, and removing capabilities as portable weight-space operations.