arXiv subjects

Jie Lu

Publications and source records attributed to Jie Lu.

At least 55 records · Page 3Linked to original sources

arXiv2026

Bandwidth-constrained Variational Message Encoding for Cooperative Multi-agent Reinforcement Learning

Graph-based multi-agent reinforcement learning (MARL) enables coordinated behavior under partial observability by modeling agents as nodes and communication links as edges. While recent methods excel at learning sparse coordination graphs-determining who communicates with whom-they do not address what information should be transmitted under hard bandwidth constraints. We study this bandwidth-limited regime and show that naive dimensionality reduction consistently degrades coordination performance. Hard bandwidth constraints force selective encoding, but deterministic projections lack mechanisms to control how compression occurs. We introduce Bandwidth-constrained Variational Message Encoding (BVME), a lightweight module that treats messages as samples from learned Gaussian posteriors regularized via KL divergence to an uninformative prior. BVME's variational framework provides principled, tunable control over compression strength through interpretable hyperparameters, directly constraining the representations used for decision-making. Across SMACv1, SMACv2, and MPE benchmarks, BVME achieves comparable or superior performance while using 67--83% fewer message dimensions, with gains most pronounced on sparse graphs where message quality critically impacts coordination. Ablations reveal U-shaped sensitivity to bandwidth, with BVME excelling at extreme ratios while adding minimal overhead.

cs.LG↗

arXiv2026

Robust and High-Fidelity Controlled Two-Qubit Gates via Asymmetric Parallel Resonant Excitation

Implementing high-fidelity controlled two-qubit gates in dipole-dipole interacting systems, such as rare-earth-ion crystals, in hindered by spectral inhomogeneity and weak coupling. Existing method often rely on detuned pulses, making them susceptible to frequency errors and AC Stark shifts. We propose a robust resonant scheme for arbitrary controlled two-qubit gates that utilizes asymmetric excitation and pulse engineering to achieve decoupled, parallel qubit control. Simulations on rare-earth-ion ensemble qubits demonstrate gate fidelities exceeding 99% within a 170 kHz detuning range with off-resonant excitation below 0.2%. This approach offers a robust, scalable route for quantum computing in spectrally crowded systems.

quant-ph↗

arXiv2026

Interband optical conductivities in two-dimensional tilted Dirac bands revisited within the tight-binding model

Within the framework of linear response theory, we theoretically investigated the interband longitudinal optical conductivities (LOCs) in two-dimensional (2D) tilted Dirac bands using a tight-binding (TB) model, incorporating the effects of band tilting and Dirac-point shifting. We identified three characteristic critical frequencies in the interband LOCs of the TB model: the partner frequencies, the sharp- peak frequency, and the cutoff frequency. In contrast to conventional critical frequencies, these three types are consistently absent in the corresponding linearized $k\cdot p$ model. Notably, the sharp-peak frequency and cutoff frequency remain robust against variations in band tilting and Dirac-point shifting. By employing analytical expressions derived via the Lagrange multiplier method, we elucidate the origins of the conventional critical frequencies and their partner counterparts. In contrast, the sharp-peak frequency and cutoff frequency are associated with interband optical transitions at high-symmetry points of the energy bands, arising from the Pauli exclusion principle and the finite boundaries of the Brillouin zone. Our theoretical predictions are intended to guide future experimental studies on tilt-dependent optical phenomena in 2D tilted Dirac systems.

cond-mat.mes-hall↗

arXiv2026

Systematic analysis of the form factors of $B_{c}$ to $P$-wave charmonia and corresponding weak decays

In this article, the vector, axial vector and tensor form factors of $B_{c}\to χ_{cJ}$ ($J=0,1,2$) and $B_{c}\to h_{c}$ are analyzed within the framework of three-point QCD sum rules. With the calculated vector and axial vector form factors, we directly study the decay widths and branching ratios of semileptonic decays $B_{c}^{-}\to χ_{cJ}l \barν_l, h_{c}l \barν_l$ $(l=e, μ$ and $τ)$ and analyze the nonleptonic decays $B_{c}^{-}\to χ_{cJ}π^{-}, χ_{cJ}K^{-}, χ_{cJ}ρ^{-}, χ_{cJ}K^{*-}$, $B_{c}^{-}\to h_{c}π^{-}, h_{c}K^{-}, h_{c}ρ^{-}, h_{c}K^{*-}$ by using the naive factorization approach (NFA). These results can provide more information to understand the properties of $B_{c}$ meson and $P$-wave charmonia and to study the heavy quark dynamics.

hep-ph↗

arXiv2026

Generalized Incremental Learning under Concept Drift across Evolving Data Streams

Real-world data streams exhibit inherent non-stationarity characterized by concept drift, posing significant challenges for adaptive learning systems. While existing methods address isolated distribution shifts, they overlook the critical co-evolution of label spaces and distributions under limited supervision and persistent uncertainty. To address this, we formalize Generalized Incremental Learning under Concept Drift (GILCD), characterizing the joint evolution of distributions and label spaces in open-environment streaming contexts, and propose a novel framework called Calibrated Source-Free Adaptation (CSFA). First, CSFA introduces a training-free prototype calibration mechanism that dynamically fuses emerging prototypes with base representations, enabling stable new-class identification without optimization overhead. Second, we design a novel source-free adaptation algorithm, i.e., Reliable Surrogate Gap Sharpness-aware (RSGS) minimization. It integrates sharpness-aware perturbation loss optimization with surrogate gap minimization, while employing entropy-based uncertainty filtering to discard unreliable samples. This mechanism ensures robust distribution alignment and mitigates generalization degradation caused by uncertainties. Thus, CSFA establishes a unified framework for stable adaptation to evolving semantics and distributions in open-world streaming scenarios. Extensive experiments validate the superior performance and effectiveness of CSFA compared to SOTA approaches.

cs.LG↗

arXiv2026

Delving into Spectral Clustering with Vision-Language Representations

Spectral clustering is known as a powerful technique in unsupervised data analysis. The vast majority of approaches to spectral clustering are driven by a single modality, leaving the rich information in multi-modal representations untapped. Inspired by the recent success of vision-language pre-training, this paper enriches the landscape of spectral clustering from a single-modal to a multi-modal regime. Particularly, we propose Neural Tangent Kernel Spectral Clustering that leverages cross-modal alignment in pre-trained vision-language models. By anchoring the neural tangent kernel with positive nouns, i.e., those semantically close to the images of interest, we arrive at formulating the affinity between images as a coupling of their visual proximity and semantic overlap. We show that this formulation amplifies within-cluster connections while suppressing spurious ones across clusters, hence encouraging block-diagonal structures. In addition, we present a regularized affinity diffusion mechanism that adaptively ensembles affinity matrices induced by different prompts. Extensive experiments on \textbf{16} benchmarks -- including classical, large-scale, fine-grained and domain-shifted datasets -- manifest that our method consistently outperforms the state-of-the-art by a large margin.

cs.CV↗

arXiv2026

A Unifying Primal-Dual Proximal Framework for Distributed Nonconvex Optimization

We consider distributed nonconvex optimization over an undirected network, where each node privately possesses its local objective and communicates exclusively with its neighboring nodes, striving to collectively achieve a common optimal solution. To handle the nonconvexity of the objective, we linearize the augmented Lagrangian function and introduce a time-varying proximal term. This approach leads to a Unifying Primal-Dual Proximal (UPP) framework that unifies a variety of existing first-order and second-order methods. Building on this framework, we further derive two specialized realizations with different communication strategies, namely UPP-MC and UPP-SC. We prove that both UPP-MC and UPP-SC achieve stationary solutions for nonconvex smooth problems at a sublinear rate. Furthermore, under the additional Polyak-Łojasiewics (P-Ł) condition, UPP-MC is linearly convergent to the global optimum. These convergence results provide new or improved guarantees for many existing methods that can be viewed as specializations of UPP-MC or UPP-SC. To further optimize the mixing process, we incorporate Chebyshev acceleration into UPP-SC, resulting in UPP-SC-OPT, which attains an optimal communication complexity bound. Extensive experiments across diverse network topologies demonstrate that our proposed algorithms outperform state-of-the-art methods in both convergence speed and communication efficiency.

math.OC↗

arXiv2026

Integrating Health Sensing into Cellular Networks: Human Sleep Monitoring Using 5G Signals

Cellular networks offer a unique opportunity to enable device-free and wide-area health monitoring by exploiting the sensitivity of radio-frequency (RF) propagation to human physiological activities. In this paper, we present the first experimental study of human sleep monitoring using realistic 5G signals collected from commercial cellular infrastructure. We investigate a practical scenario in which a smartphone is placed near a bed, and a 5G base station periodically configures uplink sounding reference signal (SRS) transmissions to obtain fine-grained channel state information (CSI). Leveraging uplink CSI measurements, we design a lightweight signal processing pipeline for respiration rate estimation and a CNN model for sleep body movement classification. Through extensive experiments conducted on an indoor private 5G network, our system achieves over 91.2% accuracy in respiration rate estimation and 85.5% accuracy in sleep movement classification.

cs.NI↗

arXiv2026

Robust Distributed Nonconvex Optimization Enabling Communication Acceleration and Privacy Protection

This paper addresses a distributed nonconvex optimization problem over multi-agent networks, where each agent exchanges its local information solely with its neighbors. Given that most existing distributed nonconvex optimization algorithms are susceptible to information leakage during inter agent communications, we propose a Robust Proximal Primal dual algorithm, referred to as RPP, to enhance the security of information transmission. In contrast to many existing approaches that directly transmit local variables throughout the network, we introduce carefully designed random noises to obfuscate sensitive local information. This not only preserves privacy but also demonstrates the noise robustness of our proposed algorithm. We establish a sublinear rate at which RPP converges to a stationary solution. Moreover, by incorporating Chebyshev acceleration, an accelerated variant of RPP is developed and achieves the optimal communication complexity bound for the algorithms that allow for exchanging local deci sions at each iteration. The superior convergence performance of RPP is validated through a few numerical experiments, which also indicate that, within an appropriate range, the introduced perturbations do not impede the convergence speed of RPP.

math.OC↗

arXiv2026

EExApp: GNN-Based Reinforcement Learning for Radio Unit Energy Optimization in 5G O-RAN

With over 3.5 million 5G base stations deployed globally, their collective energy consumption (projected to exceed 131 TWh annually) raises significant concerns over both operational costs and environmental impacts. In this paper, we present EExAPP, a deep reinforcement learning (DRL)-based xApp for 5G Open Radio Access Network (O-RAN) that jointly optimizes radio unit (RU) sleep scheduling and distributed unit (DU) resource slicing. EExAPP uses a dual-actor-dual-critic Proximal Policy Optimization (PPO) architecture, with dedicated actor-critic pairs targeting energy efficiency and quality-of-service (QoS) compliance. A transformer-based encoder enables scalable handling of variable user equipment (UE) populations by encoding all-UE observations into fixed-dimensional representations. To coordinate the two optimization objectives, a bipartite Graph Attention Network (GAT) is used to modulate actor updates based on both critic outputs, enabling adaptive trade-offs between power savings and QoS. We have implemented EExAPP and deployed it on a real-world 5G O-RAN testbed with live traffic, commercial RU and smartphones. Extensive over-the-air experiments and ablation studies confirm that EExAPP significantly outperforms existing methods in reducing the energy consumption of RU while maintaining QoS. The source code is available at https://github.com/EExApp/EExApp.

eess.SY↗

arXiv2026

From Newborn to Impact: Bias-Aware Citation Prediction

As a key to accessing research impact, citation dynamics underpins research evaluation, scholarly recommendation, and the study of knowledge diffusion. Citation prediction is particularly critical for newborn papers, where early assessment must be performed without citation signals and under highly long-tailed distributions. We identify two key research gaps: (i) insufficient modeling of implicit factors of scientific impact, leading to reliance on coarse proxies; and (ii) a lack of bias-aware learning that can deliver stable predictions on lowly cited papers. We address these gaps by proposing a Bias-Aware Citation Prediction Framework, which combines multi-agent feature extraction with robust graph representation learning. First, a multi-agent x graph co-learning module derives fine-grained, interpretable signals, such as reproducibility, collaboration network, and text quality, from metadata and external resources, and fuses them with heterogeneous-network embeddings to provide rich supervision even in the absence of early citation signals. Second, we incorporate a set of robust mechanisms: a two-stage forward process that routes explicit factors through an intermediate exposure estimate, GroupDRO to optimize worst-case group risk across environments, and a regularization head that performs what-if analyses on controllable factors under monotonicity and smoothness constraints. Comprehensive experiments on two real-world datasets demonstrate the effectiveness of our proposed model. Specifically, our model achieves around a 13% reduction in error metrics (MALE and RMSLE) and a notable 5.5% improvement in the ranking metric (NDCG) over the baseline methods.

cs.SI↗

arXiv2026

Choosing How to Remember: Adaptive Memory Structures for LLM Agents

Memory is critical for enabling large language model (LLM) based agents to maintain coherent behavior over long-horizon interactions. However, existing agent memory systems suffer from two key gaps: they rely on a one-size-fits-all memory structure and do not model memory structure selection as a context-adaptive decision, limiting their ability to handle heterogeneous interaction patterns and resulting in suboptimal performance. We propose a unified framework, FluxMem, that enables adaptive memory organization for LLM agents. Our framework equips agents with multiple complementary memory structures. It explicitly learns to select among these structures based on interaction-level features, using offline supervision derived from downstream response quality and memory utilization. To support robust long-horizon memory evolution, we further introduce a three-level memory hierarchy and a Beta Mixture Model-based probabilistic gate for distribution-aware memory fusion, replacing brittle similarity thresholds. Experiments on two long-horizon benchmarks, PERSONAMEM and LoCoMo, demonstrate that our method achieves average improvements of 9.18% and 6.14%.

cs.AI↗

arXiv2026

Designing and Evaluating an AI-enhanced Immersive Multidisciplinary Simulation (AIMS) for Interprofessional Education

Interprofessional education has long relied on case studies and the use of standardized patients to support teamwork, communication, and related collaborative competencies among healthcare professionals. However, traditional approaches are often limited by cost, scalability, and inability to mimic the dynamic complexity of real-world clinical scenarios. To address these challenges, we designed and developed AIMS (AI-enhanced Immersive Multidisciplinary Simulations), a virtual simulation that integrates a large language model (Gemini-2.5-Flash), a Unity-based virtual environment engine, and a character creation pipeline to support synchronized, multimodal interactions between the user and the virtual patient. AIMS was designed to enhance collaborative clinical reasoning and health promotion competencies among students from pharmacy, medicine, nursing, and social work. A formal usability testing session was conducted in which participants assumed professional roles on a healthcare team and engaged in a mix of scripted and unscripted conversations. Participants explored the patient's symptoms, social context, and care needs. Usability issues were identified (e.g., audio routing, response latency) and used to guide subsequent refinements. Findings suggest that AIMS supports realistic, profession-specific, and contextually appropriate conversations. We discuss technical innovations of AIMS and conclude with future directions.

cs.ET↗

arXiv2026

HetGCoT: Heterogeneous Graph-Enhanced Chain-of-Thought LLM Reasoning for Academic Question Answering

Academic question answering (QA) in heterogeneous scholarly networks presents unique challenges requiring both structural understanding and interpretable reasoning. While graph neural networks (GNNs) capture structured graph information and large language models (LLMs) demonstrate strong capabilities in semantic comprehension, current approaches lack integration at the reasoning level. We propose HetGCoT, a framework enabling LLMs to effectively leverage and learn information from graphs to reason interpretable academic QA results. Our framework introduces three technical contributions: (1) a framework that transforms heterogeneous graph structural information into LLM-processable reasoning chains, (2) an adaptive metapath selection mechanism identifying relevant subgraphs for specific queries, and (3) a multi-step reasoning strategy systematically incorporating graph contexts into the reasoning process. Experiments on OpenAlex and DBLP datasets show our approach outperforms all sota baselines. The framework demonstrates adaptability across different LLM architectures and applicability to various scholarly question answering tasks.

cs.SI↗

arXiv2026

Analysis of the semileptonic decays of $Ξ_{cc}$ and $Ω_{cc}$ baryons in QCD sum rules

We firstly carry out a systematic analysis on the spin $\frac{1}{2}^{+}\rightarrow\frac{3}{2}^{+}$ weak transition process in the framework of three-point QCD sum rules, where the initial and final states are doubly and singly charmed baryons. In the phenomenological side, all possible couplings of interpolating current to hadronic states are considered. In doing operator production expansion at QCD side, the contributions of the perturbative part, vacuum condensate terms of $\langle{\bar qq}\rangle$, $\langle g_{s}^{2}GG\rangle$, $\langle \bar q g_{s}σGq\rangle$ and $g_{s}^{2}\langle{\bar qq}\rangle^{2}$ are all considered. After the form factors in space-like region ($Q^2>0$) are obtained, the numerical results are extrapolated into time-like region ($Q^2<0$) by a fitting function. Using the predicted form factors, we finally analyze the semileptonic decays of $Ξ_{cc}^{++}\rightarrow Σ_{c}^{*+}l^{+}ν_{l}$, $Ξ_{cc}^{++}\rightarrow Ξ_{c}^{\prime*+}l^{+}ν_{l}$, $Ω_{cc}^{+}\rightarrowΞ_{c}^{\prime*0}l^{+}ν_{l}$ and $Ω_{cc}^{+}\rightarrow Ω_{c}^{*0}l^{+}ν_{l}$ with $l=e,μ$. The predictions in this work can deepen our understanding of the dynamics in the decay processes of doubly heavy baryons and provide useful information to explore the possibility of new physics in heavy baryonic decay channels.

hep-ph↗

arXiv2026

Near-Real-Time Resource Slicing for QoS Optimization in 5G O-RAN using Deep Reinforcement Learning

Open-Radio Access Network (O-RAN) has become an important paradigm for 5G and beyond radio access networks. This paper presents an xApp called xSlice for the Near-Real-Time (Near-RT) RAN Intelligent Controller (RIC) of 5G O-RANs. xSlice is an online learning algorithm that adaptively adjusts MAC-layer resource allocation in response to dynamic network states, including time-varying wireless channel conditions, user mobility, traffic fluctuations, and changes in user demand. To address these network dynamics, we first formulate the Quality-of-Service (QoS) optimization problem as a regret minimization problem by quantifying the QoS demands of all traffic sessions through weighting their throughput, latency, and reliability. We then develop a deep reinforcement learning (DRL) framework that utilizes an actor-critic model to combine the advantages of both value-based and policy-based updating methods. A graph convolutional network (GCN) is incorporated as a component of the DRL framework for graph embedding of RAN data, enabling xSlice to handle a dynamic number of traffic sessions. We have implemented xSlice on an O-RAN testbed with 10 smartphones and conducted extensive experiments to evaluate its performance in realistic scenarios. Experimental results show that xSlice can reduce performance regret by 67% compared to the state-of-the-art solutions. Source code is available at https://github.com/xslice-5G/code.

eess.SY↗

arXiv2025

History-Augmented Contrastive Learning With Soft Mixture of Experts for Blind Super-Resolution of Planetary Remote Sensing Images

Blind Super-Resolution (BSR) in planetary remote sensing constitutes a highly ill-posed inverse problem, characterized by unknown degradation patterns and a complete absence of ground-truth supervision. Existing unsupervised approaches often struggle with optimization instability and distribution shifts, relying on greedy strategies or generic priors that fail to preserve distinct morphological semantics. To address these challenges, we propose History-Augmented Contrastive Mixture of Experts (HAC-MoE), a novel unsupervised framework that decouples kernel estimation from image reconstruction without external kernel priors. The framework is founded on three key innovations: (1) A Contrastive Kernel Sampling mechanism that mitigates the distribution bias inherent in random Gaussian sampling, ensuring the generation of plausible kernel priors via similarity constraints; (2) A History-Augmented Contrastive Learning strategy that leverages historical model states as negative self-priors. We provide a theoretical analysis demonstrating that this mechanism induces strong convexity in the feature space, thereby stabilizing the unsupervised optimization trajectory and preventing overfitting; and (3) A Morphology-Aware Soft Mixture-of-Experts (MA-MoE) estimator that dynamically modulates spectral-spatial features to adaptively reconstruct diverse planetary topographies. To facilitate rigorous evaluation, we introduce Ceres-50, a benchmark dataset encapsulating diverse geological features under realistic degradation simulations. Extensive experiments demonstrate that HAC-MoE achieves state-of-the-art performance in reconstruction quality and kernel estimation accuracy, offering a solution for scientific observation in data-sparse extraterrestrial environments.

cs.CV↗

arXiv2025

Autonomous Source Knowledge Selection in Multi-Domain Adaptation

Unsupervised multi-domain adaptation plays a key role in transfer learning by leveraging acquired rich source information from multiple source domains to solve target task from an unlabeled target domain. However, multiple source domains often contain much redundant or unrelated information which can harm transfer performance, especially when in massive-source domain settings. It is urgent to develop effective strategies for identifying and selecting the most transferable knowledge from massive source domains to address the target task. In this paper, we propose a multi-domain adaptation method named \underline{\textit{Auto}}nomous Source Knowledge \underline{\textit{S}}election (AutoS) to autonomosly select source training samples and models, enabling the prediction of target task using more relevant and transferable source information. The proposed method employs a density-driven selection strategy to choose source samples during training and to determine which source models should contribute to target prediction. Simulteneously, a pseudo-label enhancement module built on a pre-trained multimodal modal is employed to mitigate target label noise and improve self-supervision. Experiments on real-world datasets indicate the superiority of the proposed method.

cs.LG↗