arXiv AI
DiffPDE: Masked Diffusion Language Models as PDE Solver
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Hugging Face Trending Papers
Aug 31
DiffPDE: Masked Diffusion Language Models as PDE Solver
Existing approaches for synthesizing Partial Differential Equation (PDE) solvers predominantly rely on autoregressive models, yet their global left-to-right decoding incurs substantial redundancy when...
arXiv AI
Oct 15
Diff Diffusion: Predicting Edit Paths with Diffusion Models for Efficient Code Editing
Diff Diffusion is a diffusion-inspired large language model framework designed for efficient code editing. It predicts edit paths from an original snippet to its revised form by representing denoising as structured edit operations, and uses multi-stage training and efficient parallel decoding to focus computation on changed regions. The approach matches or outperforms autoregressive baselines while achieving up to 2.78× faster inference and a 39.0% reduction in median model latency in production.
arXiv AI
Sep 30
Less Uniform Discrete Diffusion is More Powerful and Scalable
The paper introduces Less Uniform Diffusion (LUDI), a framework that improves uniform diffusion language models by using a less uniform loss and per-token time embeddings to guide reverse transitions and enable confidence-based few-step sampling. Experiments demonstrate that LUDI provides cleaner supervision, enhances few-step generation, and scales to a 7B model (LUDI-7B) that achieves a 3-token-per-step speedup over autoregressive decoding while matching masked diffusion baselines. The work suggests that UDLMs still have untapped potential for complex generation tasks.
arXiv Computation and Language
Sep 24
LOCKR: A Hidden-State Trajectory-Guided Planner for Detecting and Repairing Stable-but-Wrong Lock-In in Diffusion Language Models
arXiv:2609.27220v1 Announce Type: new Abstract: Diffusion language models generate text through iterative denoising, exposing intermediate trajectories before final answers are produced. We identify...
arXiv Machine Learning
Sep 7
Distilled Continuous Diffusion Language Models Can Write Code in Few Steps---or One
PlaidQ is a 0.7B continuous diffusion language model designed for code generation. By distilling its iterative refinement trajectory into only a few denoising steps—or even a single step—PlaidQ achieves competitive performance with discrete diffusion models while dramatically reducing inference time. The study demonstrates that continuous diffusion can be effectively compressed, enabling efficient and accurate code generation with minimal computational overhead.
arXiv Computation and Language
Sep 25
Large Language Models for Programming: Actually Fixing or Reimplementing Incorrect Code?
The paper investigates how large language models (LLMs) handle bug fixing versus problem solving in competitive programming. Using a dataset of ~3,000 Codeforces submissions and their human fixes, the authors compare LLM-generated patches to human patches and assess whether LLMs prefer to modify buggy code or generate new solutions. Results show that LLMs often alter more lines than necessary and sometimes produce entirely new solutions, performing better when allowed to generate solutions from scratch rather than patching existing code.