AutoIndexer: Flexible-Order Decoding by Training Causal Models on Chains of Edits
arXiv:2610.12545v1 Announce Type: new Abstract: Autoregressive decoding is less suited to non-causally ordered data, e.g., cross-references or future dependencies, since their outputs cannot be revised post hoc, unlike iterative human reasoning. Diffusion LLMs partially mitigate this limitation but…
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- 2026-10-12 04:00 · arXiv cs.CL
AutoIndexer: Flexible-Order Decoding by Training Causal Models on Chains of Edits