When autonomous coding agents synthesize implementation code, formal specifications, and mechanical proofs simultaneously, the software engineering hierarchy inverts: the formal specification becomes the primary human-reviewed artifact, while source code and proof scripts become disposable build outputs. This paper presents an end-to-end architecture for agent-authored, formally verified software engineering. First, we establish a logic-agnostic foundation combining a spec-first stack inversion, a zero-code-review workflow gated by automated continuous integration (CI) checks, and a minimal Verification Intermediate Representation (VIR) lowered via a thin, verified translator. Second, we pair an extensional Computational Type Theory (CTT) specification layer with Higher-Order Abstract Syntax (HOAS) and linear resource typing in the VIR--eliminating rewrite friction for synthesis agents while defining an experiment, its protocol frozen before the first reader, to measure whether removing boundary coercions improves human specification readability. Third, we scale collaboration across multi-team organizations using Personal Spec-Copilots and a Shared Synthesis Fleet that detects cross-team Semantic Merge Conflicts and fuses modular contracts into zero-copy binaries. We illustrate this architecture on a zero-copy authenticated message router and distinguish our existing empirical baselines (salt, SaltBench, x86lean, and MetaPRL) from newly proposed components.
The advancement of speech technology has predominantly favored high-resource languages, creating a significant digital divide for speakers of most Sub-Saharan African languages. To address this gap, we introduce WAXAL, a large-scale, openly accessible speech dataset for 24 languages representing over 100 million speakers. The collection consists of two main components: an Automated Speech Recognition (ASR) dataset containing approximately 1,250 hours of transcribed, natural speech from a diverse range of speakers, and a Text-to-Speech (TTS) dataset with around 235 hours of high-quality, single-speaker recordings reading phonetically balanced scripts. This paper details our methodology for data collection, annotation, and quality control, which involved partnerships with four African academic and community organizations. We provide a detailed statistical overview of the dataset and discuss its potential limitations and ethical considerations. The WAXAL datasets are released at https://huggingface.co/datasets/google/WaxalNLP under the permissive CC-BY-4.0 license to catalyze research, enable the development of inclusive technologies, and serve as a vital resource for the digital preservation of these languages.
Shreya Agrawal, Mohammed Alewi Hassen, Emmanuel Asiedu Brempong, Boris Babenko, Fred Zyda, Olivia Graham, Di Li, Samier Merchant 等 19 位作者
Precipitation nowcasting, which predicts rainfall up to a few hours ahead, is a critical tool for vulnerable communities in the Global South frequently exposed to intense, rapidly developing storms. Timely forecasts provide a crucial window to protect lives and livelihoods. Traditional numerical weather prediction (NWP) methods suffer from high latency, low spatial and temporal resolution, and significant gaps in accuracy across the world. Recent machine learning-based nowcasting methods, common in the Global North, cannot be extended to the Global South due to extremely sparse radar coverage. We present Global MetNet, an operational global machine learning nowcasting model. It leverages the Global Precipitation Mission's CORRA dataset, geostationary satellite data, and global NWP data to predict precipitation for the next 12 hours. The model operates at a high resolution of approximately 0.05° (~5km) spatially and 15 minutes temporally. Global MetNet significantly outperforms industry-standard hourly forecasts and achieves significantly higher skill, making forecasts useful over a much larger area of the world than previously available. Our model demonstrates better skill in data-sparse regions than even the best high-resolution NWP models achieve in the US. Validated using ground radar and satellite data, it shows significant improvements across key metrics like the critical success index and fractions skill score for all precipitation rates and lead times. Crucially, our model generates forecasts in under a minute, making it readily deployable for real-time applications. It is already deployed for millions of users on Google Search. This work represents a key step in reducing global disparities in forecast quality and integrating sparse, high-resolution satellite observations into weather forecasting.