arXiv subjects

Wei Feng

Publications and source records attributed to Wei Feng.

At least 19 recordsLinked to original sources

arXiv2026

Nonreciprocal generation of Bell states via cooperative dissipation in a qubit-magnon system

Directionally generating and controlling quantum states is critical in chiral quantum technologies. Here, we propose a scheme for the nonreciprocal generation of Bell states via cooperative dissipation in a qubit-magnon system, consisting of a superconducting transmon qubit and a yttrium iron garnet sphere subject to a bias magnetic field. The cooperative dissipation between them is induced by coupling both to a common transmission-line waveguide. Due to this cooperative dissipation, the system possesses a dark eigenstate within the single-excitation subspace. Under appropriate parameter conditions, the dark eigenstate reduces to a specific Bell state. With the assistance of a drive pulse, the system can be prepared in this Bell state (i.e., the dark eigenstate). In particular, we find that the dark eigenstate corresponds to different Bell states under positive ($+\mathbf{B}_0$) and negative ($-\mathbf{B}_0$) bias fields. Consequently, nonreciprocal Bell-state generation is achieved: $+\mathbf{B}_0$ yields one Bell state, while $-\mathbf{B}_0$ yields the other. Moreover, we define a fidelity-based nonreciprocal rate to quantify this nonreciprocity. Our work establishes cooperative dissipation as a versatile resource for nonreciprocal entangled-state generation, opening a promising route toward chiral quantum technologies.

quant-ph↗

arXiv2026

Joint Antenna Rotation and Polarforming for Satellite Communications

This paper investigates joint antenna rotation and polarforming for low-Earth-orbit (LEO) communications, where a satellite equipped with rotatable dual-polarized antenna elements receives uplink signals from ground terminals (GTs) distributed across multiple clusters. In particular, antenna rotation redistributes directional gains across angularly separated clusters, while polarforming improves the separability of spatially correlated GT channels within each cluster. We formulate a weighted sum-rate maximization problem that jointly optimizes rotatable antenna (RA) orientation trajectories, element-wise polarformers, and digital receive filters under steering-range and inter-epoch slew constraints. To solve the resulting nonconvex problem, we develop a weighted minimum mean-square error (WMMSE)-based alternating algorithm, in which the optimal polarformer for each element is derived in semi-closed form, while an exact boresight parameterization yields convex feasible sets for RA-trajectory optimization, enabling Armijo-based gradient updates. Simulation results demonstrate that the proposed joint design outperforms benchmark schemes and reveal the complementary roles of antenna rotation in directional-gain allocation and polarforming in improving channel separability.

eess.SP↗

arXiv2026

RSure-Agent: Reliable Use of Tool Observations for Remote Sensing Agents

Remote sensing agents rely on perception, measurement, and raster analysis tools to solve Earth observation tasks. We refer to their judgments and quantitative results about ground objects as tool observations. However, these observations are subject to uncertainty and may be incorrect even when tools execute successfully. When agents accept incorrect observations, the errors can propagate through subsequent reasoning and cause task failure. We analyze 1,229 execution trajectories across three remote sensing agent benchmarks. On each benchmark, at least 88.1 percent of tasks depend on tool observations. Among these tasks, at least 22.7 percent contain incorrect observations despite successful tool execution. These errors propagate to the final answer in at least 82.0 percent of affected tasks on each benchmark. To address this problem, we propose RSure-Agent, a framework for verifying tool observations and limiting error propagation. We introduce a verifiable observation protocol that requires tools to return process evidence for the agent to verify their observations. We construct a task-tool reliability prior from offline task feedback. The prior summarizes each tool configuration's past performance across task types and provides a task-specific reference for verification. Using process evidence and this prior, RSure-Agent decides whether to accept an observation, request additional evidence, or reject it. We evaluate RSure-Agent on EarthBench, ThinkGeo, TerraLogic, and CHOICE-420. Across three agent benchmarks, RSure-Agent reduces the error propagation rate by 21.3 to 25.9 percentage points relative to the base configuration with verification and the prior disabled. On CHOICE-420, it improves overall accuracy over direct answering by 5.71 percentage points on average across 11 backbone models. On EarthBench, it reduces the tool-call ratio by 25.9 percent relative to Earth-Agent.

cs.CV↗

arXiv2026

A General-Purpose Dual-Path Interaction Framework for Multi-View Medical Image Segmentation: Alleviating Data Volume Dependency in Small-Sample Regimes

The performance of medical image segmentation depends on a large amount of annotated data, and most segmentation models experience a significant drop in performance in low-sample-size scenarios. Existing semi-supervised and weakly supervised methods rely on specific network architectures, while traditional multi-view-assisted segmentation methods require strictly paired samples, which limits their clinical applications. To address these issues, we propose DPIF, a general-purpose plug-and-play dual-path interaction framework compatible with arbitrary encoder-decoder segmentation networks, which leverages cross-view anatomical complementarity to reduce annotation reliance. DPIF first performs Sinkhorn optimal transport-based cross-view distribution alignment to establish global semantic correspondence without sample-level pairing. It then adopts conditional mutual information-guided feature decoupling to enable bidirectional knowledge transfer only within the shared semantic space, suppressing view-specific noise. Finally, persistent homology-based topological regularization encodes anatomical invariants as unsupervised priors to mitigate overfitting. Experiments were conducted using 24 baseline models on a private multicenter echocardiography dataset and two public datasets. The results show that DPIF-enhanced models outperform single-view baseline models when trained on comparable amounts of data. Notably, most dual-path variants trained on 40\% - 60\% of the data achieve performance comparable to that of single-view models trained on the full dataset. This demonstrates that DPIF provides a general-purpose and scalable solution for data-scarce multi-view medical image segmentation problems.

cs.CV↗

arXiv2026

Evolving Creatives with Clicks: CTR-Driven Advertising image Editing via Attribute Disentanglement and Preference Alignment

The optimization objective for e-commerce advertising creatives has shifted from aesthetic quality to online business metrics like click-through rate (CTR). Recent methods generate appealing ad images from product images by constructing CTR-based reward signals and refining prompt generation via Reinforcement Learning. Such paradigms equally penalize all attributes within an underperforming image, even though some are actually beneficial to clicks. In this work, we propose Evolving Creatives with Clicks (ECC), which reformulates creative optimization as fine-grained editing rather than generation from scratch. ECC selectively refines attributes that hurt CTR while preserving those that contribute positively. Specifically, faced with a rich attribute space across massive images, ECC first identifies 28 attributes significantly correlated with CTR via ANOVA and Random Forest regression. Based on attribute contrasts between images with disparate CTRs, we instruction-tune a prompt model to generate editing suggestions, then refine it with real click preferences since reference-derived suggestions are not uniformly beneficial. Furthermore, we introduce Consistent Preference Optimization (CPO) as ECC's preference optimization strategy, which enforces consistency across semantically equivalent shuffled suggestions to mitigate DPO overfitting and improve preference generalization. Extensive experiments demonstrate that ECC significantly improves the CTR of ad images, outperforming state-of-the-art models by a substantial margin. The complete data pipeline, including attribute extraction and analysis, the resulting dataset, and source code will be released upon acceptance.

cs.CV↗

arXiv2026

Mission Efficiency Optimization in Low-Altitude Economy: Adaptive Power Allocation for Coordinating Heterogeneous Aircraft Swarms

With the rapid development of the low-altitude economy, low-altitude operations are booming, where complex missions require collaborative efforts among multiple heterogeneous low-altitude aircrafts (LAAs). Specifically, different LAAs assume distinct roles: some for sensing, some for communication, some for computing, and others for mission execution, together forming a sensing-communication-computing-control (SC3) closed loop, akin to a reflex arc. To enable efficient coordination in such multi-LAA swarms, we introduce the concept of operational-capability entropy (OCE) to quantify the effective work capability of operational LAAs. Accordingly, by jointly considering heterogeneous OCE and channel conditions among LAAs, we formulate the power allocation problem with the goal of minimizing the linear quadratic regulator (LQR) cost, which serves as a metric for mission efficiency. The resulting complex optimization problem is decomposed into two convex subproblems that are solved iteratively, with closed-form solutions derived for each. Simulation results demonstrate that the proposed mission-oriented adaptive power allocation scheme significantly outperforms traditional ones.

cs.IT↗

arXiv2026

Ego4WAM: What Matters When Scaling Egocentric Human Data for Robot Learning?

Egocentric human data provides a scalable source of experience for robot learning, but varies substantially in human-robot alignment, behavioral coverage, and available supervision. Existing work shows favorable scaling with increasing human data, but it remains unclear which data properties drive downstream robot gains and how to use such data throughout the training pipeline. We present a systematic study of egocentric human data with different alignment and supervision under a unified world-action model framework. With the model backbone fixed, we disentangle the effects of human-robot alignment, data duration and task diversity, action supervision, and data usage strategies. We find that aligned human demonstrations substantially improve out-of-distribution generalization and reduce target-task robot data requirements; data duration and task diversity affect downstream capabilities differently; and video-only supervision remains effective without action labels, providing a strong foundation for subsequent video-action training. We validate these findings through closed-loop policy evaluation on both real robots and RoboDojo. Rather than treating data duration as the sole scaling axis, Ego4WAM shows how alignment, task diversity, available supervision, and usage strategy jointly shape the value of egocentric human data for robot learning.

cs.RO↗

arXiv2026

Fully developed active turbulence defined through a non-equilibrium phase transition

Non-equilibrium systems challenge the standard definitions of phases and phase transitions from equilibrium statistical physics. For example, in active fluids continuous energy injection at the microscale drives the system intrinsically out of equilibrium. This process gives rise to collective turbulent-like flows whose onset remains poorly defined. Here we show that the onset corresponds to an activity-driven phase transition, marked by the emergence of a system-spanning critical backbone of vorticity nodal lines. Combining two independent experiments, microtubule kinesin active nematics and dense suspensions of swimming bacteria, with large-scale simulations of active nematics and fluctuating nematohydrodynamics, we show that this transition occurs at a critical activity threshold. Below this threshold, vorticity structures are fragmented and vortex centres form only finite, mechanically floppy networks. Above this threshold, the nodal lines of the vorticity field percolate and the vortex centres form a rigid, system-spanning cluster. The geometric transition is characterised by the emergence of critical percolation statistics for the vorticity nodal lines, while the mechanical transition reflects the emergence of a vortex configuration that shows rigidity percolation statistics for vortex centres. We show that both transitions are absent in equilibrium systems that obey detailed balance, demonstrating that the transition is intrinsically non-equilibrium. These results establish an experimentally accessible definition of fully developed active turbulence, linking microscopic activity to macroscopic geometry, and mechanics of living systems.

cond-mat.soft↗

arXiv2026

Experimental demonstration of broadband-laser suppression of cross-beam energy transfer

In direct-drive inertial confinement fusion (ICF), cross-beam energy transfer (CBET) redirects a significant fraction of the incident laser energy out of the plasma. We report the experimental demonstration that broadband lasers reduce CBET-enhanced reflected-light return, performed at the low-coherence Kunwu laser facility with two crossed beams of 0.6% bandwidth and up to 550J at $\sim$2.6$\times$10$^{14}$ W/cm$^{2}$. A coupled ray-tracing model shows that the strong CBET amplification of narrowband reflected light is much weaker under broadband illumination. In symmetric incidence condition of two orthogonal beams, the total fractional scattered energy decreases from 7.57% to 4.64%; in asymmetric incidence condition, which separates stimulated Brillouin scattering (SBS) from specular reflection, the SBS fraction decreases from 3.75% to 0.46% and the specular-reflection fraction decreases from approximately 2.2% to 1.3%. These results establish broadband lasers as an effective, experimentally validated approach to reducing energy escape through CBET-enhanced reflection in direct-drive ICF.

physics.plasm-ph↗

arXiv2026

Knowing When to Critique: Task-Adaptive Metacognitive Regulation for Reliable LLM Reasoning

Large language models (LLMs) reason fluently but do not regulate their reasoning: they apply uniform scrutiny to every input, which leaves them vulnerable to adversarial and counterfactual prompts, while indiscriminate critique over-corrects answers that were already sound. We propose MetaCrit, a multi-agent framework grounded in Nelson and Narens' metacognitive regulation theory that calibrates how much critique each task receives. MetaCrit separates regulation into four agents: an object-level generator, a monitoring agent that assesses response validity, a control agent that critiques logical soundness, and a meta-level synthesizer that reconciles their signals into a regulated response. Adaptivity here is input-conditioned intervention strength within a fixed pipeline: all four agents run on every input and what varies is the direction and magnitude of the correction they produce, not which stages execute. Across reasoning, safety, and bias benchmarks, MetaCrit improves truthfulness and logical soundness and reaches zero toxicity on BOLD and HONEST without a reasoning trade-off, whereas the same critique applied indiscriminately degrades performance. The cost is four calls per query, about one sixth of the cost of a dedicated reasoning model of similar accuracy. A writing study shows that MetaCrit is preferred for critical-thinking support, and its agents transfer to existing frameworks without architectural change. Code is available at https://github.com/Paparare/EduThink4AI.

cs.MA↗

arXiv2026

A Novel Dynamic Ray-Tracing Channel Model for 6G LEO Satellite-to-Ground Communication Systems

The sixth generation (6G) communication systems envision a space-air-ground integrated network, making accurate satellite-to-ground (S2G) channel modeling crucial for communication systems design. Currently, the majority of S2G channel models rely on stochastic methods, which cannot provide multipath information based on realistic satellite orbits and environments. In this paper, we propose a novel low Earth orbit (LEO) S2G channel model based on dynamic ray-tracing to overcome the challenges of tracking rays for long-distance propagation by setting a virtual transmission plane (VTP). Moreover, since the line-of-sight propagation mechanism contributes the most power to satellite communications, the VTP is constructed based on the first Fresnel zone to generate parallel rays close to the ground. The realistic satellite orbit and environmental information are used in the proposed LEO S2G ray-tracing channel model to compute channel characteristics. The results show that the proposed LEO S2G ray-tracing channel model can accurately generate the channel characteristics based on the satellite orbits. The channel characteristics of LEO S2G communications computed by the proposed ray-tracing method can well match the channel measurements.

eess.SP↗

arXiv2026

CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection

Medical image anomaly detection is central to timely diagnosis and clinical decision support, yet abnormal samples are costly to collect because of disease rarity, privacy concerns, and expert workload. This motivates unsupervised learning from normal images, where abnormalities are detected as deviations from learned normal patterns. However, medical anomalies are often subtle, local, and intertwined with normal anatomical variations, which complicates reliable normality modeling. Distillation-based methods support normality modeling by using frozen pretrained teachers as stable feature references, yet mismatches between generic teacher priors and student representations adapted to medical images can produce residuals unrelated to abnormalities in conventional distillation pipelines. To address this limitation, we propose the Collaborative Feature Refinement Network, which learns normality through a coupled process of shared feature conditioning before decoding and cross-space consistency after decoding. Shared feature conditioning performs medical-aware conditioning on teacher and student features under common rules, while cross-space consistency constrains each decoded stream with the complementary encoder representation for reciprocal normal reconstruction. The coupled process is further stabilized by the homework set reorganization strategy, which periodically refreshes normal training subsets. Experiments on six medical image benchmarks show competitive anomaly classification and strong anomaly localization performance.

cs.CV↗

arXiv2026

Secure Relay Low-Altitude Networks via Hybrid Fixed-Position and Rotatable Antenna Arrays

In this paper, a relay network with hybrid fixed-position and rotatable antenna arrays is proposed. The deployment of rotatable arrays in conventional relay networks is considered to provide more secure communications for low-altitude economy applications. Specifically, both the base station and the relay station are equipped with fixed-position antenna arrays and rotatable arrays to serve ground users and aerial users, respectively. To address the challenge of multi-user interference, a low-cost reconfigurable intelligent surface is exploited as a candidate path. Accordingly, under constraints on transmit power, user quality of service, rotatable range, and path selection, the objective is to maximize the worst-case secrecy rate (SR) through joint beamforming, power allocation, and rotatable antenna orientation design. First, the SR performance in the \textcolor{black}{single ground-aerial user pair scenario} is investigated, and a step-by-step leakage-based scheme is proposed. Then, the general multi-user scenario is studied, and a Distributional Soft Actor-Critic with Three refinements (DSAC-T)-based learning scheme, which supports hybrid discrete and continuous actions, is proposed to maximize the worst-case SR. Simulation results validate the effectiveness of the proposed schemes. The proposed schemes achieve approximately a twofold improvement in SR performance compared to isotropic antennas. \textcolor{black}{The proposed system can achieve approximately 99.7\% power saving, 55\% antenna saving, and can serve more users.}

eess.SP↗

arXiv2026

CoCoFL: Continual Computing for Federated Learning over Intermittent Satellite-Ground Links

Low earth orbit (LEO) satellite constellations enable geographically distributed ground devices to collaboratively train a global model via federated learning (FL) without sharing raw data, with applications in environmental monitoring and disaster prediction. However, in satellite-assisted FL scenarios, intermittent satellite-ground links allow only a subset of devices to participate in global aggregation within each visibility window, leaving unscheduled devices idle and their local computational and data resources underutilized. Under partial device participation, data heterogeneity among devices may bias the global model toward certain devices, thereby deteriorating learning performance. In this regard, we propose a continual computing based federated learning framework, referred to as CoCoFL, in which scheduled devices participate in the global model aggregation, while unscheduled devices continue updating their local models taking into account model staleness. Guided by the convergence analysis of CoCoFL and subject to visible-window-related time constraints, we jointly optimize the device scheduling and the number of local epochs for scheduled and unscheduled devices. Experimental results demonstrate that CoCoFL achieves faster convergence, lower training loss, and higher test accuracy compared with baselines.

cs.DC↗

arXiv2026

Topological Mixing and Braiding Universality in Polar Active Matter

Connecting the autonomous kinematics of active matter to its emergent macroscopic transport is constrained by the requirement for high-resolution Eulerian velocity fields. Here, we use confined bacterial suspensions as a model active fluid, mapping the sparse Lagrangian trajectories of fluorescent spy cells into (2+1)-dimensional geometric braids that directly encode the spatiotemporal entanglement of the flow. We use the finite-time braiding exponent (FTBE) as a proxy to quantify the topological entropy and chaotic mixing of confined bacterial suspensions. In moderately confined wet systems, we find that hydrodynamic coupling drives a structural transition from a dilute active gas to coherent vortices, and ultimately to active turbulence, revealing three distinct density-dependent regimes of topological mixing. Conversely, truncating the hydrodynamic screening length via extreme confinement drives the system toward a dry active matter limit. In this limit, dense in-plane steric collisions suppress irreducible entanglement and substantially reduce the FTBE at high particle densities. By evaluating the topological complexity generated per encounter, we reveal a transition from a discrete geometric encounter regime to an areal escape mechanism. Finally, we establish a square-root scaling between the FTBE and the effective diffusivity, placing the self-sustained mixing of active fluids into the pathline braiding universality class.

cond-mat.soft↗

arXiv2026

Shared Sky, Shared Spectrum: Coordinated Satellite-5G Networks for Low-Altitude Economy

Driven by both technological development and practical demands, the low-altitude economy relying on low-altitude aircrafts (LAAs) is booming. However, neither satellites nor terrestrial fifth-generation (5G) networks alone can effectively satisfy the communication requirements for ubiquitous lowaltitude coverage. While full integration of satellites and 5G networks offers theoretical benefits, the associated overhead and complexity pose significant challenges for rapid deployment. As a more economical and immediately viable alternative, this paper investigates partially-integrated networks where satellites and 5G systems operate with coarse synchronization yet achieve coordinated spectrum sharing, pooling their capabilities to jointly serve LAAs. Leveraging the inherent position-awareness of LAAs, we propose a framework for joint time-frequency spectrum sharing with an adaptive synchronization time scale, where only large-scale channel state information (CSI) is required. To avoid solving the NP-hard optimization problem directly, link-feature-aided clustering is employed following a divide-andconquer strategy. The proposed framework achieves substantial performance gains with low overhead and complexity, enabling swift advancement of low-altitude applications while paving the way for future integrated satellite-terrestrial network evolution.

cs.IT↗

arXiv2026

VIBE-Bench: Evaluating Personalized Large Language Models When Profiles Don't Mean Preferences

Personalized Large Language Models (PLLMs) aim to tailor responses to individual users, where a central challenge is preference reasoning: inferring query-relevant preferences from user-related history. Existing benchmarks, however, largely assume that such preference can be retrieved from semantically related history. We study an underexplored but practically important regime, profile-preference conceptual misalignment (PRCM), where observable profile cues and query-specific preferences lie in different concept spaces, making semantic retrieval inconsistent for personalization. We introduce VIBE-Bench, a benchmark with two psychology-grounded tasks, 3,504 personas and 12,239 dialogues, including a manually verified gold test set, and requires cross-concept preference reasoning beyond surface semantic overlap. Experiments with several personalization methods show that current PLLMs largely rely on shallow semantic correlations and fail to acquire robust cross-concept mappings. These findings establish PRCM as a distinct failure regime in PLLMs and position VIBE-Bench as a focused testbed for advancing preference reasoning beyond semantic matching.

cs.AI↗

arXiv2026

Intelligent Reflecting Surface Deployment for Low-Altitude Coverage: Illumination Geometry, Directional Characteristics, and Optimization

Terrestrial base stations (BSs) are typically configured with fixed downtilt to serve ground users, resulting in weak illumination of low-altitude airspace even under line-of-sight (LoS) propagation. In this paper, we establish a channel model that incorporates BS and intelligent reflecting surface (IRS) radiation patterns for three-dimensional (3D) low-altitude coverage while preserving the existing BS configuration. We formulate a budget-constrained IRS deployment problem that jointly determines candidate-site selection, IRS orientations, and phase shifts to maximize the worst-case signal-to-noise ratio (SNR) over the 3D low-altitude airspace. The selected sites and optimized IRS parameters remain fixed after deployment, yielding a quasi-static IRS configuration. We characterize the illumination geometry between the fixed-downtilt BS and rooftop candidates by deriving the nonnegative installation-height range satisfying the BS main-lobe condition. The separation between the mapped main-lobe height boundaries grows linearly with horizontal BS-to-site distance and decreases inversely with the number of BS antennas. We further derive an analytical lower bound on the regional worst-case normalized array gain achievable through IRS phase design over served directions with different direction spans. The resulting sufficient direction span decreases inversely with the square root of the number of IRS elements when the same worst-case normalized gain guarantee is maintained. We develop a mixed-integer alternating optimization (AO) algorithm to solve the resulting problem. Simulation results validate the analytical characterizations and show that the proposed scheme achieves higher worst-case SNR than benchmarks across different deployment budgets.

cs.IT↗