Edit the Bits, Diff the Codes: Bitwise Residual Editing for Visual Autoregressive Models

Text-guided image editing with visual autoregressive (VAR) generators requires controlling both what the model samples and where the sampled change is written back into the image. Existing VAR editors mainly operate on token streams, features, or flat next-token logits, leaving two native structures of bitwise-residual VAR models underused: the per-bit Bernoulli prediction head and the additive multi-scale residual code field. We propose BitResEdit, a training-free editor for bitwise-residual VAR generators such as \textsc{Infinity}. BitEdit performs source-negative guidance by tilting the post-CFG per-bit log-odds along a source--target contrast computed on a shared edited prefix, then projects each update into a closed-form Bernoulli-KL trust region around the clean CFG sampler. ResEdit converts the resulting sampled bits into per-scale continuous-code residuals, gates them with a localization mask, and re-injects them through the generator's native sum-of-scales. The two components couple decision-time bit guidance with combination-time code composition, preserving masked-out latent features exactly while applying localized, scale-aware edits inside the target region. On PIE-Bench with Infinity-2B, BitResEdit achieves the strongest text alignment among same-backbone VAR editors and competitive background preservation, setting a new state of the art among training-free VAR editing methods. Ablations show that BitEdit and ResEdit play complementary roles in target alignment and background preservation. Detailed ablations show that both code-space residual editing and bitwise phrase-contrast guidance are necessary for robust autoregressive image editing.