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Jessie Wang

Publications and source records attributed to Jessie Wang.

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Certified Quotient Calibration with Weighted-Projective Orbits and Finite-Shot Guarantees

Periodic controls can make a quantum-calibration scan redundant, but compilation, noise, and measurement can invalidate the symmetry behind that reduction. We introduce Certified Quotient Calibration (CQC), which audits whether circuit amplitudes have a common positive grading and define weighted-projective-space (WPS) control orbits. From pilot counts it bounds the largest proposed within-orbit and smallest audited between-orbit squared Hellinger distances by U_in and L_out. CQC accepts only when these bounds meet a task tolerance, preserve audited alternatives, and imply a net saving after certification cost; otherwise it rejects or defers the reduction. The theorem bounds accepted orbit substitutions and [0,1]-valued calibration objectives on one confidence event. Its cost corollary authorizes a representative-only scan only when its certification-inclusive cost is below that of the full grid. Weighted-projective descent, Hellinger testing rates, and multinomial concentration are standard; the contribution is their combination into a finite-shot approve--reject--defer guarantee for task error and net calibration cost. Gate-level experiments on IQM Garnet and IBM Marrakesh, Kingston, and Fez exercise all three decisions in compiled one-, two-, and three-qubit families. In a preregistered two-qubit Bell-phase experiment on IBM Kingston, the pilot audit found U_in=0.01922<L_out=0.09030, and quotient and full scans selected the same correction orbit for both injected phase offsets. Fresh held-out counts met the prespecified 0.03 noninferiority margin. Including 18,432 pilot shots, the quotient procedure used 36,864 rather than 55,296 total shots, a 33.3% reduction. These results support the CQC decision path for the tested families, but not a native cubic pulse law, a device-independent WPS orbit, or a universal saving rate.

quant-ph

UXAgent: A System for Simulating Usability Testing of Web Design with LLM Agents

Usability testing is a fundamental research method that user experience (UX) researchers use to evaluate and iterate their new designs. But what about evaluating and iterating the usability testing study design itself? Recent advances in Large Language Model-simulated Agent (LLM Agent) research inspired us to design UXAgent to support UX researchers in evaluating and iterating their study design before they conduct the real human-subject study. Our system features a Persona Generator module, an LLM Agent module, and a Universal Browser Connector module to automatically generate thousands of simulated users and to interactively test the target website. The system also provides a Result Viewer Interface so that the UX researchers can easily review and analyze the generated qualitative (e.g., agents' post-study surveys) and quantitative data (e.g., agents' interaction logs), or even interview agents directly. Through a heuristic evaluation with 16 UX researchers, participants praised the innovation of our system but also expressed concerns about the future of LLM Agent usage in UX studies.

cs.CL

AgentA/B: Automated and Scalable Web A/BTesting with Interactive LLM Agents

A/B testing experiment is a widely adopted method for evaluating UI/UX design decisions in modern web applications. Yet, traditional A/B testing remains constrained by its dependence on the large-scale and live traffic of human participants, and the long time of waiting for the testing result. Through formative interviews with six experienced industry practitioners, we identified critical bottlenecks in current A/B testing workflows. In response, we present AgentA/B, a novel system that leverages Large Language Model-based autonomous agents (LLM Agents) to automatically simulate user interaction behaviors with real webpages. AgentA/B enables scalable deployment of LLM agents with diverse personas, each capable of navigating the dynamic webpage and interactively executing multi-step interactions like search, clicking, filtering, and purchasing. In a demonstrative controlled experiment, we employ AgentA/B to simulate a between-subject A/B testing with 1,000 LLM agents Amazon.com, and compare agent behaviors with real human shopping behaviors at a scale. Our findings suggest AgentA/B can emulate human-like behavior patterns.

cs.HC

UXAgent: An LLM Agent-Based Usability Testing Framework for Web Design

Usability testing is a fundamental yet challenging (e.g., inflexible to iterate the study design flaws and hard to recruit study participants) research method for user experience (UX) researchers to evaluate a web design. Recent advances in Large Language Model-simulated Agent (LLM-Agent) research inspired us to design UXAgent to support UX researchers in evaluating and reiterating their usability testing study design before they conduct the real human subject study. Our system features an LLM-Agent module and a universal browser connector module so that UX researchers can automatically generate thousands of simulated users to test the target website. The results are shown in qualitative (e.g., interviewing how an agent thinks ), quantitative (e.g., # of actions), and video recording formats for UX researchers to analyze. Through a heuristic user evaluation with five UX researchers, participants praised the innovation of our system but also expressed concerns about the future of LLM Agent-assisted UX study.

cs.HC