Revision-capable generation is appealing because it can insert or revise earlier content, but many non-autoregressive and edit-based approaches obtain this flexibility through repeated sequence-level computation.
We propose Reviser, a decoder-only Transformer that generates a response as a sequence of cursor-relative actions on a mutable canvas.
At each step, Reviser predicts exactly one action token: INSERT(token), MOVE($Δ$), or STOP, and is autoregressive over edit-history actions rather than final text order.
This design enables genuinely non-monotonic generation while preserving a simple next-action interface.
On a continuation benchmark, Reviser is strongly preferred to SEDD and MDLM in our arena evaluations, and trajectory statistics confirm that the model performs frequent backward moves and mid-canvas insertions rather than merely emulating end-append decoding.
Against size-matched autoregressive baselines, Reviser is competitive at both the 100M and 300M scales.
Under our shared FLOPs convention, Reviser also requires substantially less inference compute than representative multi-pass refinement and diffusion-style baselines.