arXiv subjects

Yangyu Wu

Publications and source records attributed to Yangyu Wu.

2 recordsLinked to original sources

arXiv2026

CivicBench: A Benchmark and Memory Framework for Web-Based Civic Data Analytics

Municipalities publish service requests, geographic boundaries, weather observations, demographic estimates, and operational guidance through independently maintained Web portals. Civic analysis must combine these sources while preserving their schemas, source links, spatial and temporal scopes, and interpretive limits. Tool-using language agents can perform this integration, but existing task benchmarks rarely test whether a computed claim remains supported by the records used. We present CivicBench, a 400-task benchmark over four cities and two years that materializes civic Web data into frozen bundles for reproducible evaluation. Executable contracts score computation, source linkage, coverage, scope, qualified answers, and refusal when the requested conclusion is not supported. We also introduce Task-State Memory (TSM), which compiles tool observations into typed assertions, reconciles records only within compatible scopes, and retrieves a bounded state organized around unresolved constraints. Across nine completed model pairs, eight yield positive mean-score differences with 95\% semantic-contract-cluster bootstrap intervals excluding zero; the ninth interval crosses zero. The benchmark and memory framework make the records behind a claim inspectable at the point of decision.

cs.AI↗

arXiv2026

Learning to Factorize and Adapt: A Versatile Approach Toward Universal Spatio-Temporal Foundation Models

Spatio-Temporal (ST) Foundation Models (STFMs) promise cross-dataset generalization, yet joint ST pretraining is computationally expensive and grapples with the heterogeneity of domain-specific spatial patterns. Substantially extending our preliminary conference version, we present FactoST-v2, an enhanced factorized framework redesigned for full weight transfer and arbitrary-length generalization. FactoST-v2 decouples universal temporal learning from domain-specific spatial adaptation. The first stage pretrains a minimalist encoder-only backbone using randomized sequence masking to capture invariant temporal dynamics, enabling probabilistic quantile prediction across variable horizons. The second stage employs a streamlined adapter to rapidly inject spatial awareness via meta adaptive learning and prompting. Comprehensive evaluations across diverse domains demonstrate that FactoST-v2 achieves state-of-the-art accuracy with linear efficiency - significantly outperforming existing foundation models in zero-shot and few-shot scenarios while rivaling domain-specific expert baselines. This factorized paradigm offers a practical, scalable path toward truly universal STFMs. Code is available at https://github.com/CityMind-Lab/FactoST.

cs.LG↗