Researchers have introduced DiffuTester, a technique to accelerate unit test generation using diffusion large language models. The method extracts structural patterns from code to guide parallel generation, aiming to make automated testing faster and more scalable. The work addresses the need for efficient large-scale software testing.
A new arXiv paper introduces ModiGen, a large language model-driven workflow designed to handle several Modelica code generation tasks at once. Modelica is used to simulate complex physical systems, but building and tuning such models typically demands deep domain knowledge. The work targets lowering that barrier by having LLMs assist with code creation for these simulation models.
A newly posted arXiv thesis investigates whether large language models can gauge the reliability of the code they produce, addressing the problem of fluent but functionally incorrect output. The work focuses on introspective uncertainty estimation as a way to flag low-confidence generations in software engineering workflows. The abstract is truncated, so the full methods and results are not yet detailed in the listing.
A new arXiv preprint reviews how diffusion-based large language models can be applied to code generation, an area currently dominated by left-to-right autoregressive decoding. The authors examine the limitations of standard autoregressive generation and assess whether diffusion approaches offer advantages for producing source code. The work is a replacement submission (v3) to the cs.AI category.
A research paper posted to arXiv examines how large language models struggle when asked to write code against APIs from private, non-public libraries. The authors note that most existing methods focus on public code and propose teaching models to work with private library documentation. The work is a replacement submission on the cs.AI listing.
A new arXiv preprint examines how automatic research agents, which use large language models to write code and iterate on experiments, can be scaled further. The authors propose incorporating world models so these agents can better predict and plan within their research environments. The work targets the long-standing goal of automating empirical research end to end.
A new arXiv paper introduces SparseDitto, a framework that uses an agentic approach to generate sparse matrix computation code for GPUs. It synthesizes implementations that account for both the sparsity pattern of the input and the specifics of the target hardware. The authors note that no single existing implementation performs best across all sparsity patterns, operators, and GPU architectures.
Researchers introduced KernelGenBench, a benchmark that evaluates how well large language models and agentic systems can produce specialized accelerator kernels. The benchmark assesses code generation across diverse operator sources and hardware platforms, aiming to fill a gap left by earlier evaluations of kernel-writing capability.
Researchers introduce IdeaAMBIG, a benchmark that assesses how well written research-idea specifications support faithful implementation of the proposed methods. The work targets details that are critical for turning a method into working code but are often left implicit or ambiguous in idea descriptions. It highlights the gap between concepts that appear novel and plausible on paper and methods that can actually be reproduced as specified.
A new arXiv paper investigates the natural-language comments that large language models produce while writing code, which end up as part of the context for generating subsequent code. It remains unclear which properties of these comments actually affect the quality of the generated code. The study sets out to identify what makes such self-generated comments helpful rather than distracting.
A new arXiv paper investigates whether large language models can add exception-related code to existing software, covering throw statements, the conditions that guard them, and try/catch blocks. The goal is to automatically equip codebases with better handling of exceptional states, an area the authors identify as essential but often underdeveloped. The work is aimed at developers seeking AI-assisted ways to strengthen error handling in mature systems.