arXiv:2609.38269v1 Announce Type: cross Abstract: Coding agents are increasingly asked to build software rather than patch it, yet benchmarks for from-scratch repository construction are mostly limit...
arXiv:2608. 09072v1 Announce Type: cross Abstract: Large language model-powered coding agents are increasingly used to modify existing code repositories, for example, by adding features or fixing bugs.
arXiv:2606. 03907v1 Announce Type: cross Abstract: Agentic AI coding tools write code with increasing autonomy and in doing so decide when to import a library and when to implement functionality from scratch.
The paper investigates how coding agents can self‑evolve their harnesses—software, configuration, and instructions—by reviewing completed sessions and proposing changes. Using Claude Code on 30 SWE‑bench issues, the authors analyzed 125 proposals from the etro skill, implemented 24, and evaluated 12 changes on subsequent tasks. The results show mixed effectiveness: some changes eliminated targeted problems, others reduced or had no impact, and some added unrelated work, indicating that self‑evolution requires mechanisms to assess and refine changes.
arXiv:2607. 15854v1 Announce Type: cross Abstract: Coding agents can fix a failing example without preserving the domain rule that made it fail, so later generations can repeat the same plausible mistake.
arXiv:2610.35333v1 Announce Type: cross Abstract: Code agents have become increasingly capable of performing software engineering tasks through interaction with software environments. However, agents...
arXiv:2609.37143v1 Announce Type: cross Abstract: Modern coding agents can deliver increasingly large repository-level changes, and recent benchmarks reflect this by emphasizing long-horizon tasks wi...
Coding agents can adapt their harnesses, the software, configuration, and instructions used to run them, based on experience from earlier tasks, but whether these adaptations improve their behavior on...
Modern coding agents can deliver increasingly large repository-level changes, and recent benchmarks reflect this by emphasizing long-horizon tasks with large reference implementations. Many benchmarks...
arXiv:2610.34633v1 Announce Type: cross Abstract: Repository-level code migration rewrites an entire project in a new programming language while preserving its observable behavior. It is challenging...
arXiv:2607. 28591v1 Announce Type: cross Abstract: Scaling coding agents requires a continuing supply of executable data for training, benchmarking, and continuous evaluation.
arXiv:2607. 03691v2 Announce Type: replace-cross Abstract: Coding agents, autonomous systems that use large language models (LLMs) to resolve software engineering tasks, rely on agent harness: a middleware layer in between a developer and a large language model that orchestrates system prompts, tool execution, context management, and iterative reasoning loops.