Nüshu is an endangered phonetic script historically used by women in Jiangyong County, southern Hunan, China. While existing computational studies of Nüshu mainly focus on textual digitization and visual recognition, the acoustic reconstruction of its authentic pronunciation remains largely unexplored. Building a Nüshu text-to-speech (TTS) system is particularly challenging because available recordings are extremely limited and mostly consist of isolated syllable-level pronunciations rather than natural sentence-level utterances. In this work, we introduce NüshuVoice, the first TTS benchmark for Nüshu. We construct a sentence-level Nüshu text-to-audio dataset that aligns standardized Unicode Nüshu text, phonetic transcriptions, standard Chinese translations, and archival recordings. To synthesize speech under this extreme low-resource setting, we propose Nüshu-PitchVITS, an -conditioned VITS framework that leverages Nüshu’s five-level pitch notation as an explicit prosodic inductive bias. Experimental results show that Nüshu-PitchVITS outperforms strong TTS baselines in spectral fidelity, pitch reconstruction, and human-rated intelligibility. We publicly release the dataset and code at: https://anonymous.4open.science/r/Nvshu-TTS-2EB6.

Users across enterprises increasingly rely on LLM agents to query their data through natural language. However, building reliable data agents remains difficult because real enterprise data is fragmented across many heterogeneous database systems, with duplicated and inconsistent data, and key information often buried in unstructured text—requiring agents to go beyond just writing SQL or data science scripts to answer questions. Existing benchmarks tackle only individual pieces of this end-to-end workflow—e.g., text-to-SQL over a single database—and are increasingly saturated and contaminated. We present a new benchmark for LLM agents, the Data Agent Benchmark (DAB), grounded in a study of enterprises building production data agents across six industries. DAB comprises 104 queries across 17 datasets and 4 database management systems. On DAB, the best state-of-the-art agent achieves only 57% pass@1. We analyze agent failure modes and distill takeaways for future data-agent development. Our benchmark and experiment code are published at [URL redacted for anonymous review].