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arXiv AI By Shu Ishida, Aliasghar Khani, Tianyu Zhang, The Cong Luong, James Seale Smith, Adam Gaier

AutoIndexer: Flexible-Order Decoding by Training Causal Models on Chains of Edits

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AutoIndexer is a generative model that trains causal models on chains of edits, enabling flexible post‑generation modifications such as token insertions, substitutions, and deletions. It introduces marker tokens for opening and closing edits and moves a cursor through the token sequence, mimicking a text editor. By perturbing ground‑truth labels with inverse edit operations during training, AutoIndexer becomes robust to erroneous context and can perform editing tasks like code correction while maintaining performance on standard LLM benchmarks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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