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Researcher profile

Weiqi Zhang

Weiqi Zhang contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
eess.SYInformation Retrievalmath.OCSystems and Control

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Published work

2 published item(s)

preprint2026arXiv

LLM Agents Enable User-Governed Personalization Beyond Platform Boundaries

Personalization today is fundamentally platform-centric: services build user representations from the behavioral fragments they observe. Yet no platform can construct a complete picture of the user, as competitive incentives, legal constraints, user privacy concerns, and epistemic limits create persistent data barriers. This paper argues for a shift from platform-centric personalization to user-governed personalization, where only the user can integrate fragmented contexts across platforms and the offline world. The key asymmetry lies in data access: only users can aggregate their own cross-platform and offline information. Large language model (LLM) agents make such integration practically feasible for the first time by enabling reasoning over heterogeneous personal data and transforming users' cross-context information into actionable personalization capabilities. We provide proof-of-concept evidence that users equipped with cross-platform data exports and an off-the-shelf LLM agent can outperform single-platform personalization baselines. We conclude by outlining a research agenda for building scalable user-governed personalization systems.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Pricing and Remunerating Electricity Storage Flexibility Using Virtual Links

Ambitious renewable portfolio standards motivate the incorporation of energy storage resources (ESR) as sources of flexibility. While the United States government aims to promote ESR participation in electricity markets, work on market designs for properly remunerating ESRs is still lacking. In this paper, we propose a new energy market clearing framework that incorporates ESR systems. The new market design is computationally attractive in that it avoids mixed-integer formulations and formulations with complementarity constraints. Moreover, compared to previous market designs, our market decomposes the operations of ESRs using the concept of virtual links, which capture the transfer of energy across time. The virtual link representation reveals economic incentives available for ESR operations and sheds light into how electricity markets should remunerate ESRs. We also explore the role of ESR physical parameters on market behavior; we show that, while energy and power capacity defines the amount of flexibility each ESR can provide, storage charge/discharge efficiencies play a fundamental role in ESR remuneration and in mitigating market price volatility. We use our proposed framework to analyze the interplay between ESRs and independent system operators (ISOs) and to provide insights into optimal deployment strategies of ESRs in power grids.

0 citations0 reviewsNo code linkedOpen access