arXiv:2604. 14137v3 Announce Type: replace-cross Abstract: Evaluating LLMs is challenging, as benchmark scores often fail to capture models' real-world usefulness.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2604. 10645v2 Announce Type: replace-cross Abstract: There is a pressing need for better development methods and tools to keep up with the growing demand and increasing complexity of new software systems.
arXiv:2601. 02430v3 Announce Type: replace-cross Abstract: Web applications (web apps) have become a key arena for large language models (LLMs) to demonstrate their code generation capabilities and commercial potential.
arXiv:2607. 27816v2 Announce Type: replace-cross Abstract: Role-playing agents (RPAs) have become one of the most important consumer applications of large language models.
arXiv:2606. 18293v1 Announce Type: cross Abstract: Thanks to rapid developments in generative AI, we are in the midst of a paradigm shift that may change how we interact with computers forever.
arXiv:2608. 11493v1 Announce Type: new Abstract: Traditional offline recommendation evaluation relies heavily on complex, manually maintained feature pipelines that are difficult to scale.
arXiv:2608. 13566v1 Announce Type: cross Abstract: Post-training papers, model cards, and blog posts often treat scores on a small set of coding benchmarks (e.