Attribution Without a Second Pass: Inline Per-Sample Gradient Provenance at 1% Overhead

arXiv:2609.32380 · cs.LG · Submitted 2026-09-26 · Read on arXiv

cs.LG

Submitted: 2026-09-26

Updated: 2026-09-26

Code: https://github.com/AmitoVrito/Traceprop

Terminology

• Provenance — 33× in this paper · explained in 4 episodes
• Fine-Tuning — 9× in this paper · explained in 5 episodes
• Machine Unlearning — 6× in this paper · explained in 2 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Transformer — 5× in this paper · explained in 2 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Approximate Unlearning — 4× in this paper · explained in 1 episode
• Influence Functions — 4× in this paper · explained in 2 episodes
• LoRA Fine-Tuning — 2× in this paper · explained in 3 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• LoRA Adapters — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes

Sources

• Studying Large Language Model Generalization with Influence Functions
• Large Language Model Unlearning
• Compression and In-Situ Query Processing for Fine-Grained Array Lineage

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