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

Speculative decoding accelerates autoregressive LLMs by having a lightweight drafter propose tokens that the target model verifies in parallel. Diffusion-based drafters further reduce drafting latency by proposing multiple tokens at once. However, these tokens are predicted independently, so a single early error causes prefix verification to discard the rest of the draft, even when it contains useful downstream predictions. We introduce DEdit, a diffusion-based drafter that can not only draft by conventional parallel unmasking but also iteratively edit its draft through token-to-token predictions. Through editing, later predictions can serve as bidirectional context for repairing earlier errors and extending the accepted prefix. To teach the model to repair errors while preserving correct predictions, we propose ProposalMix, a training scheme that mixes draft predictions with ground-truth tokens based on first-pass confidence during training. Across seven benchmarks on Qwen3-4B and Qwen3-8B, DEdit achieves the highest macro-average token acceptance and speedup among the evaluated drafters, reaching macro-average speedups of $5.72\times$ and $5.97\times$ over autoregressive generation under greedy decoding, respectively. Further analysis shows that acceptance improves with more editing passes and wider drafting windows, and that ProposalMix halves harmful edits that shorten the accepted prefix. Moreover, restricting the editor to causal attention lowers acceptance, especially on highly predictable outputs, indicating that future context is a key source of these gains.

Language models write text once, left to right, and models that write in other orders, such as diffusion LMs, pay for it with many full-sequence passes. We introduce Reviser, a decoder-only Transformer that writes and edits text with cursor actions (insert, move, delete, stop) that a deterministic executor applies to a canvas. Reviser is autoregressive over its own actions, so a step costs the same as an autoregressive (AR) step. We train Reviser to continue text, including a self-correction recipe that types a wrong token and deletes it, and Reviser-Edit to revise a draft, and compare each with a same-size AR model trained on the same data, using three LLM judges on continuations cut to the same length. Reviser-Edit beats its matched AR rewriter on CoEdIT SARI (+7.2 at 100M, +4.5 at 300M; two seeds agree) with a third of the decoding steps and latency, though the AR rewriter corrects grammar better. In continuation, Reviser beats diffusion LMs of similar size (62-74% win rate) at a fraction of their compute; two of three judges prefer its AR twin, and one slightly prefers Reviser at 100M. Reviser fills gaps it left and backspaces a just-typed repeated token with probability 0.85-0.87.

Diffusion large language models (dLLMs) are appealing for code generation because they can update many tokens in parallel, yet many are optimized for sequence synthesis rather than edit prediction. This setting is poorly matched to modern AI-assisted IDEs, where edit prediction must infer the next code change from recent edits, the current file, and cursor-local context. Such changes are usually sparse and preserve most source tokens, so regenerating a full code segment wastes computation. We propose Diff Diffusion, a diffusion-inspired LLM framework for code editing that predicts edit paths from an original snippet to its revised form. Diff Diffusion represents denoising as structured edit operations, and combines this formulation with multi-stage training and efficient parallel decoding. By focusing computation on changed regions, Diff Diffusion supports accurate low-latency edits and multi-position parallel modification. Across public edit-completion benchmarks, edit-prediction evaluations, and online validation, Diff Diffusion matches or leads autoregressive baselines while delivering 2.78 times faster inference on public edit-completion benchmarks and a 39.0% P50 model-latency reduction in production. This work provides a new modeling perspective for AI applications that require high accuracy and low latency.