arXiv:2601.19747v2 Announce Type: replace-cross
Abstract: In the rapidly evolving field of Electronic Design Automation (EDA), the deployment of Large Language Models (LLMs) for Register-Transfer Lev...
By Jiale Liu, Taiyu Zhou, Tianqi Jiang
E2E-SWE is a benchmark that tests large language models’ ability to create complete, functional software repositories from scratch. It includes 186 tasks across 11 programming languages, each requiring an agent to build an installable project based solely on a natural‑language specification and an empty workspace, while passing a hidden test suite. The benchmark was crafted by software engineers and LLMs, then refined through iterative verification by autonomous agents to ensure clarity and solvability.
By Hantian Ding, Chloe Bi, Jiacheng Zhu, John Yang, Matt Deitke, Pengcheng Yin, Zijian Wang, Rui Hou
arXiv:2607. 20499v1 Announce Type: new Abstract: Large Language Models generate plausible backend code, but a single-pass paradigm provides no guarantee of correctness or runtime reliability.
By Sai Deekshith Lekkala, Jothi Prabha Appadurai, Rohith Reddy Bellibatlu, Manpreet Singh
arXiv:2606. 30573v1 Announce Type: new Abstract: We introduce SWE-Interact, a new testbed for evaluating coding agents on multi-turn, interactive, user-driven software engineering tasks.
By Mohit Raghavendra, Anisha Gunjal, Aakash Sabharwal, Yunzhong He
The paper introduces Req2Test-Bench, a benchmark of 5,086 instances linking natural‑language Gherkin requirements to developer‑written test implementations across five languages and four BDD frameworks. It evaluates eight agent–model–reasoning setups, finding that while the best configuration (Codex with DeepSeek‑V4.1‑Flash) produces passing tests for 82.8% of cases, only 33.8% are behaviorally adequate, as measured by kill‑completeness. The study highlights diminishing returns from scaling agents, the impact of under‑specified requirements, and the potential of requirement‑grounded harnesses to improve test generation.
By Binhang Qi, Yiming Liu, Yun Lin, Yangjie Zhou, Hailong Sun, Jin Song Dong
The study evaluates local open‑weight large language model agents for data‑engineering tasks, using a benchmark of fifteen mobility‑workflow tasks that cover discovery, processing, enrichment, and reporting. Ten local model configurations were tested in one‑shot and closed‑loop conditions, producing 1,500 scored attempts on a consumer‑grade GPU. Results show that models larger than two billion parameters achieve up to 85.3% artifact‑level success, with closed‑loop workspaces boosting pass rates by 26.7–52.0 percentage points, and a quantized 9‑billion‑parameter model reaching 69.3% success with a 6.5 GB memory footprint.
By Jorge Garc\'ia-Carrasco, Javier Sanchis, Alejandro Reina-Reina, Alejandro Mat\'e, Juan Trujillo