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Ruihan Wang

Publications and source records attributed to Ruihan Wang.

7 recordsLinked to original sources
arXiv2026

Room-temperature ferroelectrically switchable quantum geometry in few-layer WTe2 for complementary in-memory computing

Quantum geometry, describing the inherent geometric structure of electron wavefunctions in momentum space, transcends the traditional charge degree of freedom and provides a novel physical basis for information encoding and processing. The key to such new computing paradigms is the non-volatile electrical programming of quantum geometric states at room temperature, which, however, has not been established. Here, we demonstrate ferroelectrically switchable quantum geometry in few-layer WTe2, which uniquely enables complementary convolutional processing. By employing the intrinsic coupling between ferroelectric polarization and quantum geometry in few-layer WTe2, we show that the second- and third-order nonlinear anomalous Hall effects (NLAHE) can be deterministically and electrically switched in a nonvolatile and correlated manner. The switching is robust at room temperature for ~104 cycles and retention of ~105 s. Furthermore, leveraging the opposite switching behaviors of second- and third-order NLAHE at room temperature, we demonstrate complementary in-memory computing and implement a hardware-level complementary convolution kernel. This kernel overcomes the inherent directional specificity of conventional convolutional networks and achieves a texture recognition accuracy of 98%, thereby illustrating a viable pathway towards physics-native computing through exploiting exotic physics in quantum materials.

cond-mat.mtrl-sci↗
arXiv2026

Production-Grade AI Coding System for Client-Side Development

Deploying large language model-based code generation in real-world client-side development remains challenging due to heterogeneous inputs, strict engineering constraints, and complex interaction logic expressed in product requirement documents (PRDs). Existing design-to-code approaches often focus on visual translation or single-shot generation, and struggle to reliably align generated code with production requirements. This paper presents a production-grade AI coding system designed for client-side development under realistic industrial constraints. The system adopts a structured, multi-stage pipeline that integrates Figma designs, natural-language PRDs, and domain-specific engineering knowledge into explicit intermediate artifacts, enabling controlled planning and incremental code generation. By grounding PRD understanding in concrete UI components, the system improves alignment between product requirements and implementation. We evaluate the system on proprietary but realistic datasets derived from production client-side projects. Results show that domain-specific adaptation significantly improves PRD understanding accuracy, while end-to-end evaluations demonstrate high UI fidelity and robust implementation of interaction logic in real-world cases. These findings suggest that structured, artifact-centric pipelines provide a practical foundation for production-grade AI coding systems.

cs.SE↗
arXiv2026

Multi-dimensional Assessment and Explainable Feedback for Counselor Responses to Client Resistance in Text-based Counseling with LLMs

Effectively addressing client resistance is a sophisticated clinical skill in psychological counseling, yet practitioners often lack timely and scalable supervisory feedback to refine their approaches. Although current NLP research has examined overall counseling quality and general therapeutic skills, it fails to provide granular evaluations of high-stakes moments where clients exhibit resistance. In this work, we present a comprehensive pipeline for the multi-dimensional evaluation of human counselors' interventions specifically targeting client resistance in text-based therapy. We introduce a theory-driven framework that decomposes counselor responses into four distinct communication mechanisms. Leveraging this framework, we curate and share an expert-annotated dataset of real-world counseling excerpts, pairing counselor-client interactions with professional ratings and explanatory rationales. Using this data, we perform full-parameter instruction tuning on a Llama-3.1-8B-Instruct backbone to model fine-grained evaluative judgments of response quality and generate explanations underlying. Experimental results show that our approach can effectively distinguish the quality of different communication mechanisms (77-81% F1), substantially outperforming GPT-4o and Claude-3.5-Sonnet (45-59% F1). Moreover, the model produces high-quality explanations that closely align with expert references and receive near-ceiling ratings from human experts (2.8-2.9/3.0). A controlled experiment with 43 counselors further confirms that receiving these AI-generated feedback significantly improves counselors' ability to respond effectively to client resistance.

cs.CL↗
arXiv2026

CritiqueCrew: Orchestrating Multi-Perspective Conversational Design Critique

UI designers face growing cognitive load and cross functional friction at the intersection of user needs, business goals, and engineering constraints. Existing automated tools often deliver static "problem lists", lacking actionable repair paths and disrupting creative flow. We introduce CritiqueCrew, a Figma tool that supports designers through conversational critique. CritiqueCrew generates multi-faceted insights by implementing a multi-perspective orchestration of distinct expert roles (UX, PM, Engineer). It translates abstract critiques into concrete actions via in context feedback and interactive remediation. Across two independent controlled studies (Total N=48), CritiqueCrew significantly improved both design quality and subjective experience compared to a traditional static checker. Furthermore, our results confirm that the structured orchestration of expert roles-rather than a unified model-is key to fostering trust and creativity support. Our work demonstrates how AI can shift from a "problem auditor" to a "solution co-creator" by integrating multi-perspective dialogue with interactive repair, offering design implications for future creative tools.

cs.HC↗
arXiv2016

Positronium energy levels at order $m α^7$: Product contributions in the two-photon-annihilation channel

Ongoing improvements in the measurement of positronium transition intervals motivate the calculation of the $O(m α^7)$ corrections to these intervals. In this work we focus on corrections to the spin-singlet parapositronium energies involving virtual annihilation to two photons in an intermediate state. We have evaluated all contributions to the positronium S-state energy levels that can be written as the product of a one-loop correction on one side of the annihilation event and another one-loop correction on the other side. These effects contribute $ΔE = -0.561971(25) m α^7/π^3$ to the parapositronium ground state energy.

hep-ph↗
arXiv2014

Positronium energy levels at order $m α^7$: light-by-light scattering in the two-photon-annihilation channel

Recent and ongoing experimental work on the positronium spectrum motivates new efforts to calculate positronium energy levels at the level of three loop corrections. We have obtained results for one set of such corrections involving light-by-light scattering of the photons produced in a two-photon virtual annihilation process. Our result is an energy shift $1.58377(8) m α^7/π^3$ for the n=1 singlet state, correcting the ground state hyperfine splitting by -6.95 kHz. We also obtained a new and more precise result for the light-by-light scattering correction to the real decay of parapositronium into two photons.

hep-ph↗