Generative retrieval formulates document retrieval as an identifier generation task. While prevailing methods increasingly adopt Semantic IDs (SIDs), their opaque nature and rigid mappings struggle with the dynamic inventory and strict interpretability requirements of E-commerce search. Furthermore, generating accurate targets from brief queries against noisy, loosely structured item titles remains a practical challenge. To address these issues, we propose a Query-to-Multi-Span generative retrieval framework tailored for E-commerce. Instead of relying on opaque SIDs or raw titles, our method simplifies the process by generating interpretable multispan identifiers from queries. We align the autoregressive model with user preferences using click logs, and employ a constraintbased beam search to isolate key spans for final item retrieval. This approach explicitly bridges generative models with robust constraint matching, ensuring both matching accuracy and transparency. Extensive offline evaluations demonstrate competitive retrieval performance, and online A/B tests confirm its effectiveness in delivering measurable conversion gains in a production environment.

Emergency Demand Response Programs (EDRP) have garnered significant attention for its potential to enhance the safety and reliability of smart grids. However, its effectiveness is often compromised by latency in detecting line outages due to noise in observed sources, limited sensor sampling rates, and constrained communication bandwidth. While advancements in quickest line outage detection have been made, the demand reduction actions initiated only after a detected change point may be too late as the EDRP is also time consuming, thereby potentially resulting in severe failures in smart grids. To address these challenges and attain high-assurance power systems, we present a joint framework that integrates quickest line outage detection with emergency demand response, allowing for simultaneous or parallel change point detection and demand reduction. In particular, a Constrained Markov Decision Process (CMDP) is formulated to maximize the average expected reward that characterizes both the revenue and risk of the smart grid, considering operational constraints related to its demand response capabilities. Both theoretical and numerical results demonstrate that our proposed integrated detection and control architecture outperforms the conventional layered successive implementations, achieving superior profit maximization and risk mitigation.