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Shijie Xu

Publications and source records attributed to Shijie Xu.

At least 19 recordsLinked to original sources
arXiv2026

RecSys Factory: Bounding LLM Agent Autonomy to Decision Points in the Industrial Recommender Lifecycle

Deploying LLM agents into industrial recommender operations exposes a three-way tension we frame as the autonomy-determinism-efficiency trilemma: general autonomy (interpreting operator intent, generating glue code zero-shot), industrial determinism (schema-conforming feature extraction, non-crashing A/B, zero compliance-path hallucination), and end-to-end efficiency. Any two can be maximized against the third. We present RecSys Factory, an LLM-agent platform deployed for 78 days across three heterogeneous Tencent recommender business lines. The design principle is autonomy at decision points, not over pipelines, made concrete through three deconstructions that each discharge one vertex of the trilemma. Runtime is deconstructed into three host-emitted event sources (Claude Code Stop hooks, corporate-IM webhooks, workflow scheduler APIs): the platform carries no long-running daemon during the wait phase and consumes zero CPU during the 94% of wall-clock spent waiting on Spark or GPU jobs. Capability is deconstructed into a 29-file skill ecosystem (8,971 lines of SKILL.md) whose per-skill pitfall tables mechanically compile into a 400-entry PitfallStore, confining autonomy to bounded typed decision surfaces inside pre-committed pipelines. Deployment spans three business lines with disjoint label semantics, A/B layer topologies, and operator personas; an onboarding-time compression is observed on two of the three and is reported as a case-study observation, not a generalization claim, and not measured against a controlled pre-platform baseline. The human is retained at the diagnostic-versus-execution boundary via a human-in-the-loop card protocol, deployed as an audit-trail primitive (schema-validated, idempotent, replayable) and reported from an 8-day 16-run pilot. Across the 78-day window the platform recorded 1,624 CLI-tool dispatches at a 78.6% aggregate success rate.

cs.AI↗
arXiv2026

Quantum Many-Body Principles of Localized-State Ensemble Luminescence

Localized electron states induced by various disorders, including defects and impurities, usually exist in solids. Their electrical properties have been extensively investigated and well documented, while their optical properties such as localized-state ensemble (LSE) luminescence remain poorly understood. In particular, a microscopic quantum many-body (MB) theory has not yet been established for LSE luminescence so far. In this Letter, we attempt to fill this void via developing a quantum MB luminescence theory taking into account both electron-phonon (e-p) and electron-electron (e-e) interactions. Abnormal thermal behaviors such as redshift and subsequent blueshift of peak position, narrowing and succeeding broadening of linewidth, decline in intensity, and variation in lifetime can be quantitatively interpreted. Within the framework of the MB-LSE theory, moreover, Varshni's empirical formula for bandgap temperature dependence and Huang-Rhys factor for e-p coupling, and other key formulas are further derived and discussed.

quant-ph↗
arXiv2026

SkinCLIP-VL: Consistency-Aware Vision-Language Learning for Multimodal Skin Cancer Diagnosis

The deployment of vision-language models (VLMs) in dermatology is hindered by the trilemma of high computational costs, extreme data scarcity, and the black-box nature of deep learning. To address these challenges, we present SkinCLIP-VL, a resource-efficient framework that adapts foundation models for trustworthy skin cancer diagnosis. Adopting a frozen perception, adaptive reasoning paradigm, we integrate a frozen CLIP encoder with a lightweight, quantized Qwen2.5-VL via low-rank adaptation (LoRA). To strictly align visual regions with clinical semantics under long-tailed distributions, we propose the Consistency-aware Focal Alignment (CFA) Loss. This objective synergizes focal re-weighting, semantic alignment, and calibration. On ISIC and Derm7pt benchmarks, SkinCLIP-VL surpasses 13B-parameter baselines by 4.3-6.2% in accuracy with 43% fewer parameters. Crucially, blinded expert evaluation and out-of-distribution testing confirm that our visually grounded rationales significantly enhance clinical trust compared to traditional saliency maps.

cs.CV↗
arXiv2026

Wavelet-Aware Anomaly Detection in Multi-Channel User Logs via Deviation Modulation and Resolution-Adaptive Attention

Insider threat detection is a key challenge in enterprise security, relying on user activity logs that capture rich and complex behavioral patterns. These logs are often multi-channel, non-stationary, and anomalies are rare, making anomaly detection challenging. To address these issues, we propose a novel framework that integrates wavelet-aware modulation, multi-resolution wavelet decomposition, and resolution-adaptive attention for robust anomaly detection. Our approach first applies a deviation-aware modulation scheme to suppress routine behaviors while amplifying anomalous deviations. Next, discrete wavelet transform (DWT) decomposes the log signals into multi-resolution representations, capturing both long-term trends and short-term anomalies. Finally, a learnable attention mechanism dynamically reweights the most discriminative frequency bands for detection. On the CERT r4.2 benchmark, our approach consistently outperforms existing baselines in precision, recall, and F1 score across various time granularities and scenarios.

cs.LG↗
arXiv2026

Causal-SAM-LLM: Large Language Models as Causal Reasoners for Robust Medical Segmentation

The clinical utility of deep learning models for medical image segmentation is severely constrained by their inability to generalize to unseen domains. This failure is often rooted in the models learning spurious correlations between anatomical content and domain-specific imaging styles. To overcome this fundamental challenge, we introduce Causal-SAM-LLM, a novel framework that elevates Large Language Models (LLMs) to the role of causal reasoners. Our framework, built upon a frozen Segment Anything Model (SAM) encoder, incorporates two synergistic innovations. First, Linguistic Adversarial Disentanglement (LAD) employs a Vision-Language Model to generate rich, textual descriptions of confounding image styles. By training the segmentation model's features to be contrastively dissimilar to these style descriptions, it learns a representation robustly purged of non-causal information. Second, Test-Time Causal Intervention (TCI) provides an interactive mechanism where an LLM interprets a clinician's natural language command to modulate the segmentation decoder's features in real-time, enabling targeted error correction. We conduct an extensive empirical evaluation on a composite benchmark from four public datasets (BTCV, CHAOS, AMOS, BraTS), assessing generalization under cross-scanner, cross-modality, and cross-anatomy settings. Causal-SAM-LLM establishes a new state of the art in out-of-distribution (OOD) robustness, improving the average Dice score by up to 6.2 points and reducing the Hausdorff Distance by 15.8 mm over the strongest baseline, all while using less than 9% of the full model's trainable parameters. Our work charts a new course for building robust, efficient, and interactively controllable medical AI systems.

cs.CV↗
arXiv2025

Xiaomi MiMo-VL-Miloco Technical Report

We open-source MiMo-VL-Miloco-7B and its quantized variant MiMo-VL-Miloco-7B-GGUF, a pair of home-centric vision-language models that achieve strong performance on both home-scenario understanding and general multimodal reasoning. Built on the MiMo-VL-7B backbone, MiMo-VL-Miloco-7B is specialized for smart-home environments, attaining leading F1 scores on gesture recognition and common home-scenario understanding, while also delivering consistent gains across video benchmarks such as Video-MME, Video-MMMU, and Charades-STA, as well as language understanding benchmarks including MMMU-Pro and MMLU-Pro. In our experiments, MiMo-VL-Miloco-7B outperforms strong closed-source and open-source baselines on home-scenario understanding and several multimodal reasoning benchmarks. To balance specialization and generality, we design a two-stage training pipeline that combines supervised fine-tuning with reinforcement learning based on Group Relative Policy Optimization, leveraging efficient multi-domain data. We further incorporate chain-of-thought supervision and token-budget-aware reasoning, enabling the model to learn knowledge in a data-efficient manner while also performing reasoning efficiently. Our analysis shows that targeted home-scenario training not only enhances activity and gesture understanding, but also improves text-only reasoning with only modest trade-offs on document-centric tasks. Model checkpoints, quantized GGUF weights, and our home-scenario evaluation toolkit are publicly available at https://github.com/XiaoMi/xiaomi-mimo-vl-miloco to support research and deployment in real-world smart-home applications.

cs.CV↗
arXiv2025

Quantum Optics Nature of the Elementary Excitations in Few-Layer WSe2 Semiconductors

A fully quantized description of a two-level system resonantly coupled with an electromagnetic field (light) is among the central topics of quantum electrodynamics, which is theorized by the quantum Rabi model. It is also a fundamental issue of light-matter interactions. The rapid development of two-dimensional (2D) transition-metal dichalcogenide (TMDC) atomically-thin semiconductors brings excellent great chance to test and demonstrate some basic predictions by quantum optics theory in textbook, i.e., by the quantum Rabi model. Such test and demonstration are of both scientific and technological significance because of the quick emergence of the second quantum revolution. In this Letter, we show a quantum optics demonstration of the variable-temperature optical responses of the elementary excitations in few-layer WSe2 flakes. It is unraveled that the variable-temperature reflectance and fluorescence patterns of the elementary excitations (i.e., the band-edge excitons) of monolayer, bilayer and hBN capped WSe2 match well with the predictions by the quantum Rabi model under the rotating wave approximation. Decoherence times, Rabi frequencies, and transition matrix elements of the elementary excitations in these few-layer WSe2 flakes are found to be all negatively correlated with temperature, and to show dependence on layer number and capping layer. These findings may provide a novel perspective for comprehending the fundamental quantum physical properties of two-dimensional materials.

physics.optics↗
arXiv2025

Odd-Parity Selection in Parity-Forbidden Electronic Transitions Revealed by Mn4+ Fluorescence Spectroscopy

Mn4+-doped fluoride phosphors represent a significant class of narrow band red-emitting materials, whose luminescent properties are profoundly influenced by electron-phonon coupling. However, the parity-forbidden nature of these electronic transition systems is incompatible with the conventional Condon approximation, which is widely adopted in the classic theories such as the Huang-Rhys theory, a framework established on the assumption of parity-allowed electric dipole transitions. This results in a critical knowledge gap regarding the principles governing the phonon sidebands of parity-forbidden electronic transitions. This study experimentally reveals a pronounced parity-dependent intensity distribution in the phonon sidebands of these systems: significantly suppressed even-order sidebands and normally observed odd-order sidebands. To elucidate the phenomenon, we extend the Huang-Rhys theory to parity-forbidden systems by incorporating the Herzberg-Teller approximation into the treatment of the transition matrix elements. The improved theory successfully uncovers the physical mechanism behind the strong suppression of the even-order sidebands in the parity-forbidden systems, in which the Huang-Rhys factor is derived as S=((2I_3)/(9I_1 ))^(1/2). This work not only reveals new findings regarding the phonon sidebands of the parity-forbidden electronic transition systems, but also establishes an improved theoretical framework for understanding the electron-phonon coupling mechanisms of color centers in solids.

cond-mat.mtrl-sci↗
arXiv2025

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning

To improve the training efficiency of federated learning (FL), previous research has employed low-rank decomposition techniques to reduce communication overhead. In this paper, we seek to enhance the performance of these low-rank decomposition methods. Specifically, we focus on three key issues related to decomposition in FL: what to decompose, how to decompose, and how to aggregate. Subsequently, we introduce three novel techniques: Model Update Decomposition (MUD), Block-wise Kronecker Decomposition (BKD), and Aggregation-Aware Decomposition (AAD), each targeting a specific issue. These techniques are complementary and can be applied simultaneously to achieve optimal performance. Additionally, we provide a rigorous theoretical analysis to ensure the convergence of the proposed MUD. Extensive experimental results show that our approach achieves faster convergence and superior accuracy compared to relevant baseline methods. The code is available at https://github.com/Leopold1423/fedmud-icml25.

cs.LG↗
arXiv2025

Temperature dependence of quasi-localized phonons-mediated non-Markovianity dynamics of SiV^- centers in diamond

Here we investigate the temperature-dependent non-Markovian dynamics of the SiV^- center in diamond, focusing on the roles of low- and high-frequency quasi-localized phonon modes. Low-frequency phonons exhibit stronger electron-phonon coupling, leading to long-lived dephasing rate, while high-frequency phonons induce rapid attenuation of oscillatory dephasing rate facilitating a persistent memory effect. The non-Markovianity measure N_C shows memory effects persisting at low temperatures but diminishing at high temperatures due to enhanced damping. The temperature dependence of N_C follows a monotonic decay, from which a transition temperature T_NM=110 K is determined. These results highlight the interplay between phonon activation and damping in shaping quantum coherence, offering insights for optimizing solid-state quantum systems.

physics.optics↗
arXiv2025

Brightening dark trions in WS2 monolayers via introducing atomic sulfur vacancies

Understanding the effects of atomic defects on the optical functionality of two-dimensional (2D) layered materials is critical to develop novel optical and optoelectronic applications of these ultimate materials. Herein, we correlate sulfur vacancies (VS) and luminescence properties of dark trions in monolayer WS2 through introducing VS defects and conducting a systematic optical spectroscopic characterization at cryogenic and room temperatures. It is unraveled that the VS defects can brighten the dark trions via introducing a stronger spin-orbit coupling due to the space inversion symmetry broken by the defects. Furthermore, the wavefunction localization of the dark trions bound at VS defects results in significant enhancement of the phonon scattering from the K2 valley phonons and hence makes the K2 phonon replica dominant in the emission spectrum. Theoretical calculations of the temperature-dependent photoluminescence spectra with quantum mechanics-based multimode Brownian oscillator model show strong support for the above arguments. Brightening the dark excitons not only sheds light on the understanding of the intriguing excitonic properties of 2D semiconductors, but also may open a way for regulating the optoelectronic performance of two-dimensional semiconductors.

cond-mat.mtrl-sci↗
arXiv2025

Tunability of Robust Exciton-Trion Polaritons in Atomically Thin WS2 Monolayers

Herein, we present an experimental demonstration of the robust exciton-trion polaritons (ETPs) by measuring and simulating the resonance reflectance spectra of various configurational WS2 monolayers with different dielectric screenings. Moreover, the oscillator strength and decoherent behavior of such hybrid ETPs can be tuned via utilizing dielectric screening effect. The effect is attributed to the regulation of the Coulomb coupling between excitons and trions by changing the surrounding dielectric constant. The demonstration and tunability of the robust ETPs offers a novel pathway for researching novel phases of quantum matter in a quantum many-body physics regime.

cond-mat.mtrl-sci↗
arXiv2024

Neural refractive index field: Unlocking the Potential of Background-oriented Schlieren Tomography in Volumetric Flow Visualization

Background-oriented Schlieren tomography (BOST) is a prevalent method for visualizing intricate turbulent flows, valued for its ease of implementation and capacity to capture three-dimensional distributions of a multitude of flow parameters. However, the voxel-based meshing scheme leads to significant challenges, such as inadequate spatial resolution, substantial discretization errors, poor noise immunity, and excessive computational costs. This work presents an innovative reconstruction approach termed neural refractive index field (NeRIF) which implicitly represents the flow field with a neural network, which is trained with tailored strategies. Both numerical simulations and experimental demonstrations on turbulent Bunsen flames suggest that our approach can significantly improve the reconstruction accuracy and spatial resolution while concurrently reducing computational expenses. Although showcased in the context of background-oriented schlieren tomography here, the key idea embedded in the NeRIF can be readily adapted to various other tomographic modalities including tomographic absorption spectroscopy and tomographic particle imaging velocimetry, broadening its potential impact across different domains of flow visualization and analysis.

physics.flu-dyn↗
arXiv2024

Mixed-Precision Embeddings for Large-Scale Recommendation Models

Embedding techniques have become essential components of large databases in the deep learning era. By encoding discrete entities, such as words, items, or graph nodes, into continuous vector spaces, embeddings facilitate more efficient storage, retrieval, and processing in large databases. Especially in the domain of recommender systems, millions of categorical features are encoded as unique embedding vectors, which facilitates the modeling of similarities and interactions among features. However, numerous embedding vectors can result in significant storage overhead. In this paper, we aim to compress the embedding table through quantization techniques. Given that features vary in importance levels, we seek to identify an appropriate precision for each feature to balance model accuracy and memory usage. To this end, we propose a novel embedding compression method, termed Mixed-Precision Embeddings (MPE). Specifically, to reduce the size of the search space, we first group features by frequency and then search precision for each feature group. MPE further learns the probability distribution over precision levels for each feature group, which can be used to identify the most suitable precision with a specially designed sampling strategy. Extensive experiments on three public datasets demonstrate that MPE significantly outperforms existing embedding compression methods. Remarkably, MPE achieves about 200x compression on the Criteo dataset without comprising the prediction accuracy.

cs.IR↗
arXiv2024

Explicit local density bounds for Itô-processes with irregular drift

We find explicit upper bounds for the density of marginals of continuous diffusions where we assume that the diffusion coefficient is constant and the drift is solely assumed to be progressively measurable and locally bounded. In one dimension we extend our result to the case that the diffusion coefficient is a locally Lipschitz-continuous function of the state. Our approach is based on a comparison to a suitable doubly reflected Brownian motion whose density is known in a series representation.

math.PR↗
arXiv2024

Masked Random Noise for Communication Efficient Federated Learning

Federated learning is a promising distributed training paradigm that effectively safeguards data privacy. However, it may involve significant communication costs, which hinders training efficiency. In this paper, we aim to enhance communication efficiency from a new perspective. Specifically, we request the distributed clients to find optimal model updates relative to global model parameters within predefined random noise. For this purpose, we propose Federated Masked Random Noise (FedMRN), a novel framework that enables clients to learn a 1-bit mask for each model parameter and apply masked random noise (i.e., the Hadamard product of random noise and masks) to represent model updates. To make FedMRN feasible, we propose an advanced mask training strategy, called progressive stochastic masking (PSM). After local training, each client only need to transmit local masks and a random seed to the server. Additionally, we provide theoretical guarantees for the convergence of FedMRN under both strongly convex and non-convex assumptions. Extensive experiments are conducted on four popular datasets. The results show that FedMRN exhibits superior convergence speed and test accuracy compared to relevant baselines, while attaining a similar level of accuracy as FedAvg.

cs.LG↗
arXiv2024

Efficient generation of multiqubit entanglement states using rapid adiabatic passage

We propose the implementation of a rapid adiabatic passage (RAP) scheme to generate entanglement in Rydberg atom-array systems. This method transforms a product state in a multi-qubit system into an entangled state with high fidelity and robustness. By employing global and continuous driving laser fields, we demonstrate the generation of two-qubit Bell state and three-qubit W state, via sequential RAP pulses within the Rydberg blockade regime. As an illustrative example, applying this technique to alkali atoms, we predict fidelities exceeding 0.9995 for two-qubit Bell and three-qubit W state, along with excellent robustness. Furthermore, our scheme can be extended to generate entanglement between weakly coupled atoms and to create four-qubit Greenberger- Horne-Zeilinger states through spatial correlations. Our approach holds the potential for extension to larger atomic arrays, offering a straightforward and efficient method to generate high-fidelity entangled states in neutral atom systems.

quant-ph↗