arXiv subjects

Hui Chen

Publications and source records attributed to Hui Chen.

At least 37 records · Page 2Linked to original sources

arXiv2026

Realization of Air-Stable Two-Dimensional Superconductor Nb2Pd3Te5 With Quasi-One-Dimensional Pair Density Modulation

Two-dimensional (2D) superconductors provide a fertile platform for exploring reduced-dimensional superconductivity and emergent quantum phenomena. Incorporating quasi-one-dimensional (quasi-1D) structural motifs into 2D superconductors offers a powerful route to engineer strong electronic anisotropy, enabling unconventional superconducting states and anisotropic superconducting transport functionalities. However, such systems remain rarely realized. Here we report the realization of a 2D superconductor Nb2Pd3Te5, exhibiting an intrinsic quasi-1D pair density modulation. Monolayer and bilayer Nb2Pd3Te5 is synthesized via van-der-Waals epitaxy. Using ultralow-temperature scanning tunneling microscopy/spectroscopy, we observe the quasi-1D crystal structure and superconductivity below ~0.6 K with a pronounced quasi-1D pair density modulation. Remarkably, both monolayer and bilayer Nb2Pd3Te5 show strong air stability. Our findings establish atomically 2D Nb2Pd3Te5 as a robust and promising platform for exploring novel low-dimensional quantum phenomena and anisotropy-enabled superconducting devices.

cond-mat.mtrl-sci↗

arXiv2026

Sci-Surf: Navigating Scientific Literature Discovery through Human Feedback and Intelligent Summarization

The rapid growth of scientific publications makes it increasingly difficult for researchers to identify relevant new studies and effectively comprehend them. Existing academic discovery platforms typically rely on static topic subscriptions or embedding-based similarity and provide only abstracts or short summaries, offering limited support for nuanced intent modeling and in-depth paper summarization. We present Sci-Surf, an intent-centric knowledge discovery system that integrates feedback-driven personalized recommendation with multi-modal blog-style paper digestion. Our approach refines user intent representations through LLM-based user profiling, while generating structured summaries that synthesize textual and visual information from full papers. The demo presents an end-to-end academic discovery pipeline and demonstrates measurable improvements in both recommendation quality and digestion quality through real-user evaluations. Specifically, the integration of verbalized profiles led to a 10.4% average improvement in predictive alignment with real-world user preferences throughout a month-long online evaluation.

cs.IR↗

arXiv2026

The Lift Spectrum: How Measurement-to-Space Adaptivity Shapes Robustness in Image-Free Single-Pixel Sensing

Single-pixel sensing encodes a scene as a short sequence of coded measurements, and image-free methods infer the task directly from that sequence. We show that removing image reconstruction relocates the central design problem to the lift: how 1D measurements become a 2D task representation. We organize this choice as a lift spectrum from a fixed-physics inverse, through a learned static projection, to content-adaptive retrieval. These are not interchangeable forms of reconstruction: the fixed-physics route reconstructs an image consumed at inference, whereas our spatiotemporal soft-fusion (STSF) network lifts measurements directly into task features, and task-prioritized loss scheduling (TPLS) uses a separate learned reconstruction branch only as scheduled training supervision. A probe-selected recurrent encoder and a parameter-matched lift ablation identify the STSF design. In simulation, STSF+TPLS exceeds the prior image-free baseline on three datasets at 3.13% sampling (+3.2 to +9.9 pp foreground mIoU) and remains competitive down to 0.39%. The strongest clean-trained reconstruct-then-segment baseline wins without measurement noise, but measurement noise reverses the ranking: the reconstructed task input carries a 20-70x larger normalized relative perturbation than the measurements themselves. Stressed to failure, the three lift regions exhibit distinct dominant signatures--collapse, imprinting, and coarsening. STSF+TPLS transfers without fine-tuning to a real single-pixel bench, where the reversal reappears as a proof of concept; inference takes about 14 ms per mask on an RTX 4090. Within the tested fixed-acquisition regime, measurement-to-space adaptivity therefore organizes both the clean-to-noisy operating envelope and the failure a system encounters. Code and pretrained weights: https://github.com/Hanyuyuan6/STSF-TPLS.

eess.IV↗

arXiv2026

PanDent: Toward Comprehensive Tooth-Level Structure-Language Consistency in Dental Radiology

Accurate evaluation of multimodal large language models (MLLMs) in dental panoramic radiography (orthopantomogram, OPG) is limited by the lack of fine-grained, clinically reliable benchmarks that reflect expert interpretation. This work introduces PanDent, a large-scale, clinically grounded OPG benchmark built upon fine-grained, expert-validated tooth-level annotations. The dataset comprises 9,524 high-quality OPGs, each associated with comprehensive structured annotations produced by experienced dentists and further validated by an oral and maxillofacial radiologist, providing clinically reliable supervision for tooth-level diagnosis and reasoning. Clinically consistent radiology reports are constructed from expert-validated findings using clinician-defined reporting logic, establishing explicit correspondence between structured clinical evidence and free-text descriptions. This design enables evaluation of whether MLLMs generate reports that are not only linguistically coherent but also clinically consistent with expert-validated tooth-level findings. Experiments are conducted on diverse MLLMs, including state-of-the-art (SOTA) proprietary models, general-domain open-source models, and medical-specific models. Results show that current MLLMs can generate fluent reports, yet fail to produce clinically consistent descriptions, exhibiting substantial errors in fine-grained localization and tooth-level diagnosis. Fine-tuning on PanDent significantly improves structure-language consistency, substantially enhancing visual localization accuracy and diagnostic correctness, and bringing model outputs closer to expert dental interpretation. These results establish PanDent as a rigorous benchmark for evaluating tooth-level clinical reasoning in MLLMs and a valuable resource for clinically grounded dental AI.

cs.CV↗

arXiv2026

On-Site Beam Calibration for RIS-Aided Positioning Systems

High precision positioning is a key enabler for next-generation communication applications such as smart transportation and augmented reality. Reconfigurable intelligent surface (RIS) technology can enhance positioning by providing additional angular information and improving coverage under obstructed propagation conditions. However, true RIS beams can differ significantly from the simplified or ideal beam response models commonly used in RIS-aided positioning, leading to beam model mismatch and an elevated positioning error floor. This paper proposes an on-site RIS beam calibration framework that reduces this error floor by estimating a realistic 3D RIS beam response model from on-site measurements. The proposed calibration algorithm first extracts the RIS-reflected channel response from signals received by a calibration agent sampling the angular range of interest, using delay-domain sparse recovery, and then estimates the beam model parameters with a gradient-based estimator. To validate the proposed framework, 3D beam patterns under 66 phase modulations were measured and incorporated into simulations. With an angular sampling step of 1 deg, the calibrated model achieves an average beam response similarity of 88.5% with respect to the ground truth, compared with 43.7% for the ideal model. The probability that the absolute lower bound of the positioning error is below 0.5m increases from 0.52 without calibration to 0.74 after calibration, showing that on-site RIS beam calibration effectively reduces the positioning error floor caused by true beam model mismatch.

eess.SP↗

arXiv2026

Generative AI-enhanced Probabilistic Multi-Fidelity Surrogate Modeling Via Transfer Learning

The performance of machine learning surrogates is critically dependent on data quality and quantity. This presents a major challenge, as high-fidelity (HF) data is often scarce and computationally expensive to acquire, while low-fidelity (LF) data is abundant but less accurate. To address this data-scarcity problem, we propose a probabilistic multi-fidelity surrogate modeling framework that integrates transfer learning with generative modeling. We employ a normalizing flow (NF) generative model as the backbone, which is trained in two phases: (i) the NF is first pretrained on a large LF dataset to learn a probabilistic forward model; (ii) the pretrained model is then fine-tuned on a small HF dataset, allowing it to correct for LF--HF discrepancies via knowledge transfer. To relax the dimension-preserving constraint of standard bijective NFs, we integrate surjective (dimension-reducing) layers with standard coupling blocks. This architecture enables learned dimension reduction while preserving the ability to train with exact likelihoods. The resulting surrogate provides fast probabilistic predictions with quantified uncertainty and significantly outperforms LF-only baselines while using fewer HF evaluations. We validate the approach for two benchmark systems: a rail-sleeper-ballast and a reinforced concrete slab. For both applications, we combine many coarse-mesh (LF) simulations with a limited set of fine-mesh (HF) simulations. The proposed model achieves probabilistic predictions with HF accuracy, demonstrating a practical path toward data-efficient, generative AI-driven surrogates for complex engineering systems.

cs.LG↗

arXiv2026

HoloTrace: a Location Privacy-Preserving Framework for mmWave MIMO-OFDM Systems

The technological innovation towards 6G cellular networks introduces unprecedented capabilities for user equipment (UE) localization, but it also raises serious concerns about physical layer location privacy. This paper introduces HoloTrace, a signal-level privacy preservation framework that relies on user-side spoofing of localization-relevant features to prevent the extraction of precise location information from the signals received by a base station (BS) in a mmWave MIMO-OFDM system. Spoofing is performed by the user on location parameters such as angle of arrival (AoA), angle of departure (AoD), and time difference of arrival (TDoA). Without requiring any protocol modification nor network-side support, our method strategically perturbs pilot transmissions to prevent a BS from performing non-consensual UE localization. The methodology allows the UE to spoof its position, keeping the precoder unchanged. We formulate spoofing as a unified rank-constrained projection problem, and provide closed-form solutions under varying levels of channel state information (CSI) at the UE, including scenarios with and without CSI knowledge. Simulation results confirm that the proposed approach enables the UE to deceive the BS, inducing significant localization errors, while the impact on link capacity varies depending on the spoofed position. Our findings establish HoloTrace as a practical and robust privacy-preserving solution for future 6G networks.

eess.SP↗

arXiv2026

Active Sensing for RIS-Aided Tracking and Power Control: A Hybrid Neuroevolution and Supervised Learning Approach

This paper studies energy efficient tracking of power-limited mobile users with the assistance of a Reconfigurable Intelligent Surface (RIS). Since localization pilot transmissions dominate the energy budget of power-constrained devices, we introduce a low-overhead feedback link from the Base Station (BS) to the user to enable dynamic uplink power control. To navigate the discrete and decentralized nature of this active sensing problem, we propose a novel Dual-Agent (DA) deep learning framework that jointly optimizes the discrete RIS phase profiles and the UE's transmit power in real time. Specifically, our approach employs a hybrid training methodology integrating the neuroevolution paradigm with supervised learning, effectively overcoming the non-differentiability of discrete phase responses from the RIS unit elements and the strict information bottleneck of single-bit feedback messages for pilot power control. The proposed DA active sensing framework can be applied with both single- and multi-antenna BSs, the latter with only minor modifications in the structure of one NN: an additional output branch with appropriate structure is included for the latter case to select a valid digital combiner from a finite set. Extensive numerical simulations demonstrate that the proposed scheme achieves highly accurate and robust tracking across diverse target motion models, outperforming extended Kalman and particle filters, as well as, machine learning-based trackers. Furthermore, in static localization, it is shown to significantly outperform traditional fingerprinting schemes, deep reinforcement learning baselines, and standard backpropagation-based estimators.

cs.IT↗

arXiv2026

HORIZON: Recoverability-Governed Curriculum for Physical-Domain Scaling

Scaling robust robot policies requires more than broader randomization, because physical-domain experience must remain organized and learnable throughout training. We study when a policy can benefit from harder physics and identify recoverability as a central constraint in on-policy physical-domain scaling. In on-policy training, new dynamics are useful only insofar as they remain close enough to the current policy to generate corrective on-policy data, rather than collapsing rollouts into unrecoverable failures. Using quadruped locomotion as a physically demanding benchmark for embodied generalization, we introduce HORIZON, a checkpointed frontier curriculum that expands physical domains only within the current policy's recoverable boundary. HORIZON uses rollback and boundary refinement to govern each expansion step, turning fixed randomization into a continual process of physical-domain growth. Experiments reveal three regularities of physical-domain expansion. First, direct domain widening is uneven across physical axes and often unlearnable without staged ordering. Second, domain composition is non-monotonic, and adding more domains beyond a compact core can dilute recoverable joint samples and reduce overall robustness. Third, offline distillation of isolated experts cannot substitute for the joint interaction generated by on-policy curriculum. Together, these results frame physical-domain generalization as a continual growth problem for embodied control, with recoverability as the organizing principle for on-policy expansion.

cs.RO↗

arXiv2026

Transformer-Guided Content-Adaptive Graph Learning for Hyperspectral Unmixing

Hyperspectral unmixing (HU) targets to decompose each mixed pixel in remote sensing images into a set of endmembers and their corresponding abundances. Despite significant progress in this field using deep learning, most methods fail to simultaneously characterize global dependencies and local consistency, making it difficult to preserve both long-range interactions and boundary details. This letter proposes a novel transformer-guided content-adaptive graph unmixing framework (T-CAGU), which overcomes these challenges by employing a transformer to capture global dependencies and introducing a content-adaptive graph neural network to enhance local relationships. Unlike previous work, T-CAGU integrates multiple propagation orders to dynamically learn the graph structure, ensuring robustness against noise. Furthermore, T-CAGU leverages a graph residual mechanism to preserve global information and stabilize training. Experimental results demonstrate its superiority over the state-of-the-art methods. Our code is available at https://github.com/xianchaoxiu/T-CAGU.

cs.CV↗

arXiv2026

RA-LWLM: Retrieval-Augmented In-Context Localization with Wireless Foundation Models

Wireless localization is a fundamental capability of sixth-generation (6G) networks. Conventional model-based methods require accurate modeling of the propagation environment and degrade in complex multipath and non-line-of-sight scenarios, while learning-based methods couple model parameters tightly to the training scene, requiring costly retraining whenever the base station (BS) configuration or propagation environment changes. In this paper, we propose RA-LWLM, a retrieval-augmented in-context localization framework that achieves training-free cross-scene adaptation by externalizing scene-specific information into a per-scene fingerprint database rather than encoding it in model weights. The framework consists of three components: a frozen wireless foundation model (FM) encoder that maps raw channel state information into a scene-agnostic representation; a retrieval module that selects the most informative references from the per-scene database via similarity search in the representation space; and a transformer-based in-context learning (ICL) module that fuses the query with the retrieved references to predict the user equipment (UE) position. To accommodate varying retrieval quality and propagation complexity across queries, the ICL module adopts a mixture-of-experts design in which experts specialize in different context sizes and are softly combined by a learnable selector. Extensive ray-tracing-based experiments across heterogeneous scenes with diverse BS configurations show that RA-LWLM achieves nearly identical accuracy on seen and unseen scenes without any per-scene retraining, substantially outperforming end-to-end and FM-based baselines. These results validate the proposed retrieval-augmented in-context paradigm as a scalable solution for cross-scene localization in 6G networks.

eess.SP↗

arXiv2026

Multicast Capacity of XL-RIS Assisted Hybrid Near- and Far-Field mmWave Communications

Multicast transmission in millimeter-wave (mmWave) networks is fundamentally limited by the weakest user, and blockages further exacerbate this problem. Large-scale reconfigurable intelligent surfaces (XL-RIS) offer a promising solution by providing high array gain to overcome blockages. However, the large aperture of XL-RIS significantly expands the near-field region, creating a hybrid-field scenario where some users lie in the near-field while others remain in the far-field. Existing hybrid-field studies on XL-RIS have primarily focused on channel estimation and deployment optimization, leaving multicast capacity analysis unexplored. This paper investigates the fundamental capacity limits of XL-RIS-assisted multicast communications in hybrid-field scenarios. For the fundamental two-user case consisting of one near-field and one far-field user, we derive the optimal closed-form covariance matrix and optimize the RIS phase shifts via manifold optimization. We establish that the multicast capacity scales as $Θ(\log_2(MN))$ as the number of transmit antennas M and/or RIS elements N grow large, and prove this scaling is order-tight. Numerical results validate the bounds and show the impact of M, $N$, and distance on the multicast rate.

eess.SP↗

arXiv2026

Flexible Rate-Splitting for Joint Unicast and Multi-Group Multicast Transmission in RIS-Assisted mmWave Networks

Joint unicast and multi-group multicast transmission with RIS and RSMA is a promising enabler for 6G services. However, existing RSMA schemes for such scenarios split only unicast messages while leaving multicast messages intact, limiting the degree of freedom of interference management. To this end, we propose a joint rate splitting framework that splits both unicast and multicast information and two RSMA schemes. The common-common fusion (CCF-RSMA) scheme encodes the unicast common part into the global multicast common stream, while the private-common fusion (PCF-RSMA) scheme merges it with the group-specific multicast private part. For each scheme, we formulate energy efficiency (EE) maximization problems under both perfect and imperfect channel state information, and jointly optimize active beamforming, RIS phase shifts and rate allocation parameters. Simulation results demonstrate that the proposed schemes significantly outperform the comparative schemes in terms of EE, thereby proving the effectiveness of the proposed framework. Moreover, CCF-RSMA is more favorable in scenarios with larger groups and higher unicast QoS demands, whereas PCF-RSMA is better suited for scenarios with smaller groups and higher multicast QoS.

eess.SP↗

arXiv2026

BeyondSWE: Can Current Code Agent Survive Beyond Single-Repo Bug Fixing?

Current code-agent benchmarks primarily evaluate localized issue resolution within a single target repository, leaving under-tested many software engineering tasks that require external knowledge or broader repository-level changes. We introduce BeyondSWE, a 500-instance benchmark drawn from 246 real-world GitHub repositories to evaluate code agents beyond single-repository bug fixing. BeyondSWE covers four representative settings: cross-repository issue resolution, domain-specific issue resolution, dependency-driven migration, and document-to-repository generation, spanning both broader knowledge scope and broader resolution scope. Our evaluation shows that BeyondSWE remains far from saturated: the best OpenHands-based agent reaches 46.12 average score, while the strongest Codex harness with GPT-5.4 (xhigh) reaches 56.65 under a search-aware prompt. To study whether external information access closes this gap, we use SearchSWE as a controlled diagnostic baseline for search-augmented coding. Search access improves most models and substantially helps some tasks, but the gains remain limited and uneven, showing that current agents still struggle to convert retrieved information into precise, version-compatible, and locally actionable code changes. These results suggest that deep search for coding remains an open problem: progress requires agents that can reliably combine external evidence with repository-local reasoning and execution-based verification.

cs.CL↗

arXiv2026

Multi-Domain Security for 6G ISAC: Challenges and Opportunities in Transportation

Integrated sensing and communication (ISAC) will be central to 6G-enabled transportation, providing both seamless connectivity and high-precision sensing. However, this tight integration exposes attack points not encountered in pure sensing and communication systems. In this article, we identify unique ISAC-induced security challenges and opportunities in three interrelated domains: cyber-physical (where manipulation of sensors and actuators can mislead perception and control), physical-layer (where over-the-air signals are vulnerable to spoofing and jamming) and protocol (where complex cryptographic protocols cannot detect lower-layer attacks). Building on these insights, we put forward a multi-domain security vision for 6G transportation and propose an integrated security framework that unifies protection across domains by leveraging existing ISAC measurements for lightweight cross-checks.

cs.CR↗

arXiv2026

Bifurcation of the quasi-stationary velocity of strongly discrete transition waves driven by gravity

Transition waves are common in multistable mechanical metamaterials, and the dynamics of weakly discrete transition waves under driving forces have been extensively discussed. However, as lattice effects become more pronounced, strongly discrete transition waves may exhibit dynamics that cannot be predicted by the continuum limit. Here, by tilting a bistable chain, we introduce a gravitational perturbation term into the dynamical equations, under which the transition waves are continuously accelerated. In the strongly discrete regime, we find that transition waves under gravitational driving possess quasi-stationary velocity plateaus (QSVPs), and the number of these plateaus first increases and then decreases as the tilt angle increases. We theoretically elucidate that the emergence of the velocity plateaus originates from the balance between gravitational driving and phonon radiation. In further analysis, the theoretical model reveals that the balance point undergoes a bifurcation at the radiation resonance, which leads to a change in the number of velocity plateaus. Our study extends the investigation of transition waves into the strongly discrete regime, and the emergence of multiple velocity plateaus opens up new possibilities for programmable solitary waves.

nlin.PS↗

arXiv2026

FastOCR: Dynamic Visual Fixation via KV Cache Pruning for Efficient Document Parsing

Vision-Language Models (VLMs) have shown strong promise on Optical Character Recognition (OCR), yet the sheer number of visual tokens required to encode dense documents incurs prohibitive inference cost. Existing pruning methods rely on physical eviction, e.g., permanently discarding visual tokens during the prefill stage. While effective for natural images, this strategy fundamentally breaks down on OCR, where virtually every visual token may correspond to a character or structural element, and any irreversible loss leads to catastrophic accuracy degradation. We observe that, although document images appear globally dense and seemingly unprunable, the model's attention to them is in fact temporally sparse: at each decoding step it concentrates on a small region that shifts gradually across steps, much as a human reader fixates on successive words rather than perceiving an entire page at once. Motivated by this Dynamic Visual Fixation phenomenon, we recast the intractable global pruning problem as a tractable local, dynamic one and propose FastOCR, a training-free framework with two complementary modules. Specifically, Focal-Guided Pruning identifies a small set of focal layers and selects the most task-relevant visual tokens from them at each step, while Cross-Step Fixation Reuse exploits the gradual shift of fixation to warm-start each step from the previous one. By dynamically adjusting which tokens are attended rather than evicting any from the cache, FastOCR avoids permanent information loss. Extensive experiments show that FastOCR serves as a plug-and-play acceleration module, generalizing consistently across five VLMs of varying sizes and architectures. On Qwen2.5-VL, FastOCR retains 98% of the unpruned model's accuracy while attending to only 5% of the visual tokens per decoding step, reducing attention latency by 3.0$\times$.

cs.CV↗

arXiv2026

Discovery of a nonsymmorphic superconductor with spontaneous rotational symmetry breaking and nontrivial zero modes

Topological superconductivity has attracted great interest due to its fundamental significance for realizing Majorana quasiparticles and fault-tolerant quantum computation. Nonsymmorphic superconductors, with symmetry-protected nontrivial electronic structures, offer a promising route to exotic topological superconducting states, yet experimental realizations remain scarce. Here we identify nonsymmorphic compound PtPb4 as a robust platform hosting superconductivity with spontaneous rotational symmetry breaking and nontrivial zero-energy modes. PtPb4 crystallizes in a frustrated Shastry-Sutherland lattice and exhibits nontrivial band topology. By combining in-plane and out-of-plane resistivity measurements, pronounced twofold anisotropy is observed in both the superconducting state and the upper critical field, evidencing spontaneous rotational symmetry breaking. Scanning tunneling microscopy/spectroscopy further reveal twofold-symmetric magnetic vortices, providing direct real-space evidence for the symmetry-broken superconducting state. Notably, a robust zero-energy vortex bound state emerges and persists without spatial splitting over extended distances, consistent with the characteristics expected for Majorana bound state. These findings uncover an exotic superconducting state in PtPb4 and establish a promising platform for exploring topological superconductivity and superconducting quantum devices.

cond-mat.supr-con↗