arXiv:2606. 06462v1 Announce Type: new Abstract: Benchmarks are fundamental for evaluating and advancing LLMs and MLLMs by providing standardized and explicit measures of performance.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2606. 11070v1 Announce Type: cross Abstract: Recent advances in reasoning and tool-calling capabilities of large language models (LLMs) have enabled increasingly capable agentic systems.
arXiv:2608. 06329v1 Announce Type: cross Abstract: Task-oriented conversational agents are evaluated using curated or automatically generated benchmarks, yet benchmark quality is rarely assessed.
arXiv:2606. 14397v1 Announce Type: new Abstract: As agentic systems continue to evolve and are widely deployed in real-world scenarios, there is a growing demand to faithfully evaluate their capabilities.
arXiv:2605. 30407v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated strong performance on general tasks, while often struggling to adapt to specialized domains without high-quality domain-specific data.
arXiv:2607. 01647v1 Announce Type: cross Abstract: Data science aims to derive actionable insights from heterogeneous raw data, unlocking the value of the massive amounts of data generated in modern society.
arXiv:2604. 18543v4 Announce Type: replace Abstract: Constructing environments for training and evaluating claw-like agents remains a manual, human-intensive process that does not scale.