arXiv:2607. 02186v1 Announce Type: new Abstract: Software development is a complex task that demands cooperation among agents with diverse roles.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2606. 17203v1 Announce Type: cross Abstract: Multi-agent AI systems are increasingly used to automate software engineering tasks including requirements analysis, architecture design, test generation, and traceability linking.
arXiv:2607. 06713v1 Announce Type: cross Abstract: Large language models are rapidly moving towards closing the development cycle, transitioning from simple assistive companions to autonomous contributors deeply embedded into collaborative development environments.
Large language model (LLM) agents are increasingly evaluated on their ability to use tools, plan multi-step tasks, coordinate with other agents, and operate over extended horizons. Reported benchmark gains often obscure recurring failure modes documented across otherwise unrelated evaluation efforts.
arXiv:2606. 19380v3 Announce Type: replace-cross Abstract: Software engineering and deployment are increasingly delegated to AI coding agents.
arXiv:2607. 05775v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly evaluated on their ability to use tools, plan multi-step tasks, coordinate with other agents, and operate over extended horizons.
arXiv:2608. 16002v1 Announce Type: cross Abstract: Reliable uncertainty quantification (UQ) is essential for deploying large language model (LLM) agents in complex interactive environments.