arXiv AI
How Should Diffusion Language Models Edit Code?
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv Computation and Language
Sep 3
Knowledge Editing for Masked Diffusion Language Models
The paper investigates whether the locate‑then‑edit approach for knowledge editing, previously applied only to autoregressive language models, can be transferred to masked diffusion models (MDMs). It finds that the optimal edit location—an early‑to‑mid‑layer MLP at the last subject token—remains the same for both model types, but that MDMs suffer a sharper decline in performance when editing longer, multi‑token facts. By incorporating intermediate partially‑unmasked states into the edit optimization, the authors restore multi‑token editing performance in MDMs.
arXiv Machine Learning
Jul 23
LaSEr-Edit: Localized Span-level Error Editing with Energy-based Localization
arXiv:2407. 00740v2 Announce Type: replace-cross Abstract: As large language models (LLMs) are widely adopted in real-world applications, it has become critical to ensure LLMs satisfy safety constraints, such as non-toxicity and logical consistency, as well as task- and situation-specific constraints.
arXiv AI
Jul 28
Beyond Block Boundaries: Multi-Block Editing for Diffusion Large Language Models
arXiv:2607. 22663v1 Announce Type: new Abstract: Block diffusion has emerged as the dominant paradigm for scaling discrete diffusion language models (dLLMs), because decoding text in fixed-size blocks preserves parallel generation within each block while keeping the quadratic attention cost tractable.
arXiv AI
Jun 30
Can Fine-Tuning Erase Your Edits? On the Fragile Coexistence of Knowledge Editing and Adaptation
arXiv:2511. 05852v4 Announce Type: replace-cross Abstract: Knowledge editing (KE) offers a lightweight alternative to retraining for updating large language models (LLMs).
arXiv Computation and Language
Sep 14
Local Edits, Global Ripples: Replay-Informed Policy Adaptation for Workflow Synthesis
The paper introduces RIPPLE, a method for adapting workflow-synthesizing agents through prompt-policy editing without retraining the underlying model. RIPPLE diagnoses failed execution trajectories, maps failures to specific policy segments, and restricts edits to those segments. It then evaluates candidate edits in isolation and replays only those that remain safe after composition, achieving up to a 23.1% improvement in validation success on a synthetic benchmark and positive gains on additional language‑model backbones.
arXiv AI
Sep 10
Diffs vs. Whole Files: An Empirical Comparison of Iterative Edit-Based and Direct Generation for Flutter/Dart Code Models
The paper compares two output regimes for code-editing language models: direct generation, where the model outputs the entire modified file, and iterative diff-based generation, where the model emits a sequence of localized edits. Experiments on Flutter/Dart tasks show that direct generation consistently outperforms diff-based generation across metrics such as compilation success, token efficiency, and quality judgments. However, diff-based generation can be competitive for short, spatially localized edits, particularly in refactoring and error-handling tasks with few edit steps.