The paper presents a novel approach to debugging multilingual web corpora by employing a large language model (LLM) as a tool-using agent rather than a classifier. Using this agent on the Slovak portion of FineWeb2, the authors identified seven distinct failure modes, including a large machine‑generated content farm that bypassed pipeline filters. The resulting test suite can scan the entire corpus in under a second without GPU resources, offering precise prevalence estimates and actionable insights into pipeline shortcomings.
By Marek \v{S}uppa, Ivan Vykopal, Jaroslav Kop\v{c}an
arXiv:2604. 16706v2 Announce Type: replace Abstract: Automated evaluation of tool-using large language model (LLM) agents is widely assumed to be reliable, yet this assumption is rarely validated against human annotation.
By Bhaskar Gurram
arXiv:2607. 06411v1 Announce Type: cross Abstract: Developers increasingly delegate real maintenance work to product-grade coding agents, and many state tasks in their native language, in the style of a customer request rather than a curated English issue.
By Evgeny Shilov (Independent Researcher)
The paper introduces BRIA (BRanch-Inspect-Adopt), an agent that isolates tool‑candidate investigations into separate branches, executing each candidate independently and then inspecting the execution evidence to adopt only useful results. BRIA dynamically retrieves additional tools as new needs arise during task execution and is evaluated on PlanBench‑XL and MCP‑Atlas with a library of 1,897 tools, outperforming or matching the strongest retrieval baselines. On MCP‑Atlas, BRIA achieves an average claim score of 89.77 versus 75.40 for MCP‑Zero, and it lies on the Pareto frontier for task performance and input‑token cost.
By Anna Mazhar, Seiji Maekawa, Nikita Bhutani
When a tool-using agent is given the same task in a different language, does it still take the same steps? Multilingual evaluation rarely asks: it compares final answers and discards the actions.
Developers increasingly delegate real maintenance work to product-grade coding agents, and many state tasks in their native language, in the style of a customer request rather than a curated English issue. Existing repository-level agentic benchmarks do not measure this setting: their task statements are English by design.