arXiv AI
Aug 13
REVERE: Reflective Evolving Research Engineer
arXiv:2603. 20667v2 Announce Type: replace-cross Abstract: Existing prompt-optimization techniques rely on local signals, causing poor generalization across tasks.
Agents That Teach: Towards Designing Incidental Learning Back into AI-Assisted Software Development
arXiv:2607. 06101v1 Announce Type: cross Abstract: AI coding agents are rapidly reshaping how software is built, with developers increasingly delegating substantial coding tasks to autonomous agents in pursuit of higher productivity.
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arXiv AI
Aug 6
AgentForge: An Immersive Role-Playing Platform for Learning Agentic Software Engineering
arXiv:2608. 04148v1 Announce Type: cross Abstract: Agentic AI is increasingly used to coordinate planning, implementation, review, and testing in software development, yet it often offers limited transparency into its decisions and interactions.
arXiv AI
Aug 20
Professional Software Developers Don't Vibe, They Control: AI Agent Use for Coding in 2025
The paper titled "Professional Software Developers Don't Vibe, They Control: AI Agent Use for Coding in 2025" examines how experienced developers employ AI agents in software development. Through field observations and surveys, it finds that developers value agents for productivity but maintain control over design and implementation to ensure quality. They use agents as collaborative tools rather than full delegation, selecting tasks based on suitability and leveraging their expertise to guide agent behavior.
arXiv AI
Oct 26
Can AI Agents Help Robotic Engineers? Lessons from Two Focus Days in Industry
The paper reports a case study on using AI agents to assist engineers in programming industrial robots. The agent did not increase productivity overall, except for generating highly repetitive code, but its effectiveness improved as engineers learned to use it and the agent was adapted to the domain. The study highlights that AI agents are not plug‑and‑play; successful adoption requires training, a learning curve, and domain‑specific configuration.
arXiv AI
Oct 15
Agency as a New Organizing Abstraction for Computer Science
arXiv:2610.17410v1 Announce Type: new Abstract: Advances in large-scale machine learning have enabled agentic AI systems: persistent, autonomous computational entities that pursue goals, initiate act...
arXiv AI
Sep 28
CODESKILL: Learning Self-Evolving Skills for Coding Agents
CODESKILL is an LLM-based framework that learns to extract, evolve, and maintain procedural skills from coding-agent trajectories. It treats skill extraction and skill-bank management as a learnable policy trained with reinforcement learning, using a hybrid reward combining rubric-based skill quality and verifiable execution feedback. Experiments on EnvBench, SWE-Bench Verified, and Terminal-Bench 2 demonstrate that CODESKILL raises average pass rates by 11.03 over a no-skill baseline and by 5.10 over the strongest prompt-based or memory baseline while keeping a compact skill bank.
arXiv AI
Aug 12
Do Personalized Skills Help Coding Agents? An Empirical Study of Developer Interaction Histories
arXiv:2608. 10319v1 Announce Type: cross Abstract: Large language model (LLM)-powered agents have rapidly evolved from code-completion tools into solvers of complex software engineering tasks.
arXiv AI
Jun 3
Toward Training Superintelligent Software Agents through Self-Play SWE-RL
arXiv:2512. 18552v3 Announce Type: replace-cross Abstract: While current software agents powered by large language models (LLMs) and agentic reinforcement learning (RL) can boost programmer productivity, their training data (e.
arXiv Machine Learning
Jul 27
How Do AI Coding Agents Contribute to Software Development? an Empirical Study of Agentic Pull Requests
arXiv:2607. 21832v1 Announce Type: cross Abstract: Recent advances in large language models and their rapid adoption across software engineering tasks have made Artificial Intelligence (AI) coding agents an integral component of modern software development workflows.
arXiv AI
Jul 23
Knowledge-Centric Self-Improvement
arXiv:2607. 19592v1 Announce Type: new Abstract: Self-improving AI systems typically treat the agent as the object that improves, by optimizing prompts, workflows, harnesses, or even the agent's own code.
arXiv AI
Oct 31
Do Personalized Skills Help Coding Agents? An Empirical Study of Developer Interaction Traces
The paper investigates whether tailoring coding agent skills to individual developers improves collaboration. It introduces a framework that bootstraps rule‑based skills and refines them with LLMs to capture developer‑specific preferences, and evaluates this approach using an interactive replay system with a simulated developer. Experiments on real interaction logs show that while task‑specific skills generally perform best, personalized skills become more effective when developers frequently express consistent preferences, especially on related tasks.
arXiv AI
Jun 6
The End of Software Engineering: How AI Agents Are Fundamentally Restructuring the Software Paradigm
arXiv:2606. 05608v1 Announce Type: cross Abstract: For over half a century, software engineering has operated on a foundational premise: human engineers decompose problems, encode decision logic into static code, and manually adapt that code as requirements evolve.