arXiv subjects

Lili Zhou

Publications and source records attributed to Lili Zhou.

4 recordsLinked to original sources

arXiv2026

Self-evolving Agentic Customer Support System at LinkedIn

Enterprise support agents operate in rapidly changing environments where policies, product capabilities, and knowledge bases evolve continuously, making static assistants brittle and costly to maintain. We present LinkedIn's self-evolving agentic support system, which integrates retrieval-augmented generation with evolutionary auto-prompting and a modular, production-aligned evaluation framework to enable safe, continuous improvement without retraining foundation models. The system treats prompts, retrieval, and evaluation as a closed-loop, versioned workflow with operational guardrails. Offline simulations and ablations show clear quality gains over vanilla RAG and baseline agents, including reduced hallucinations and improved response completeness. In a two-week user-randomized A/B test on LinkedIn's production support traffic, the integrated self-evolved workflow increased QA self-serve by 9.0 percentage points, cancellation self-serve by 4.8 points, and routing accuracy by 30.6 points. These results demonstrate a practical path to scalable, self-evolving AI agents in real-world enterprise settings.

cs.AI↗

arXiv2026

Superconducting proximity effect in a strongly correlated charge-transfer insulator

Proximity-induced superconductivity in strongly correlated insulators provides a versatile route for engineering quantum states of matter and artificial systems with tailored functionalities. However, microscopic interplay between superconductivity and correlated insulating states remains poorly understood. Here we use ultralow-temperature scanning tunnelling microscopy (STM) to systemically investigate superconducting proximity effects in a charge-transfer insulator. Via STM tip manipulation, atomically sharp lateral junctions composed of superconducting monolayer H-NbSe2 and charge-transfer insulating monolayer T-NbSe2 are constructed, enabling direct access to tunable coupling regimes. In the weak-coupling regime, there is a robust proximity-induced superconducting gap in T-NbSe2, with a reduced gap value relative to that of H-NbSe2. Upon entering the strong-coupling regime, T-NbSe2 exhibits a superconducting gap comparable to that of H-NbSe2, accompanied by pronounced particle-hole-symmetric in-gap bound states, consistent with Yu-Shiba-Rusinov-like excitations. These findings establish monolayer H/T-NbSe2 lateral junctions as a model platform for elucidating superconducting proximity effects in strongly correlated charge-transfer insulators.

cond-mat.supr-con↗

arXiv2026

Realization and manipulation of spiral charge density waves in a two-dimensional metal

Nearly degenerate charge-density-wave (CDW) states play a central role in the competition among collective phenomena. In real materials, however, these states are often intertwined by disorder, hindering their disentanglement and control. Here we show that strain can lift this near-degeneracy and spatially separate distinct CDW states in NbSe2. Using van der Waals (vdW) interactions, we stabilize a micron-scale strain network that produces spatially inhomogeneous strain fields. Within this landscape, the intrinsic 3 * 3 CDW superlattice of pristine NbSe2 transforms into an isolated unidirectional 4 * 1 order under 1D-confined compression, and into a 2 * 2 order under biaxial tension. The 4 * 1 CDW has a multiband origin and exhibits markedly enhanced thermal stability, persisting up to 70 K. At strain-network nodes, it further develops into chiral spiral textures, which can be melted by voltage pulses. These results establish strain as a powerful approach to disentangle, stabilize and manipulate competing electronic orders.

cond-mat.mes-hall↗

arXiv2023

Brain-inspired Computing Based on Deep Learning for Human-computer Interaction: A Review

The continuous development of artificial intelligence has a profound impact on biomedicine and other fields, providing new research ideas and technical methods. Brain-inspired computing is an important intersection between multimodal technology and biomedical field. Focusing on the application scenarios of decoding text and speech from brain signals in human-computer interaction, this paper presents a comprehensive review of the brain-inspired computing models based on deep learning (DL), tracking its evolution, application value, challenges and potential research trends. We first reviews its basic concepts and development history, and divides its evolution into two stages: recent machine learning and current deep learning, emphasizing the importance of each stage in the research of brain-inspired computing for human-computer interaction. In addition, the latest progress of deep learning in different tasks of brain-inspired computing for human-computer interaction is reviewed from five perspectives, including datasets and different brain signals, and the application of key technologies in the model is elaborated in detail. Despite significant advances in brain-inspired computational models, challenges remain to fully exploit their capabilities, and we provide insights into possible directions for future academic research. For more detailed information, please visit our GitHub page: https://github.com/ultracoolHub/brain-inspired-computing.

cs.AI↗