arXiv subjects

Jia Liu

Publications and source records attributed to Jia Liu.

At least 91 records · Page 5Linked to original sources

arXiv2026

Direct Detection of Dark Photon Dark Matter with the James Webb Space Telescope

In this study, we propose an investigation into dark photon dark matter (DPDM) within the infrared frequency band, utilizing highly sensitive infrared light detectors commonly integrated into space telescopes, such as the James Webb Space Telescope (JWST). The presence of DPDM induces electron oscillations in both the reflectors and the interior of the detectors. Consequently, these oscillating electrons can emit monochromatic electromagnetic waves with a frequency almost equivalent to the mass of DPDM. By employing the stationary phase approximation, we can demonstrate that when the size of the reflector significantly exceeds the wavelength of the electromagnetic wave, the contribution to the electromagnetic wave field at a given position primarily stems from the surface unit perpendicular to the relative position vector. This simplification results in the reduction of electromagnetic wave calculations to ray optics. Through a careful analysis of photon generation induced by DPDM on the various optical elements of JWST, we find that the contribution of these photons to the detected signal is negligible. Nevertheless, we propose a modified configuration of the JWST mirrors that would enable the DPDM-induced photons to be focused onto the detector. This approach can be applied to future space telescopes during their ground-testing phases. Using the JWST parameters as a representative example, the achievable upper limits on the DPDM-photon mixing constant are $ε\sim 10^{-12}-10^{-14}$ in the frequency range $10-500$~THz at the 95\% confidence level. This reveals the strong potential of future space telescopes for DPDM detection during ground testing, with sensitivities exceeding current limits by 1 to 2 orders of magnitude compared with the XENON1T result and the solar cooling bound.

hep-ph↗

arXiv2026

Coexistence of Antiferromagnetic Spin Fluctuations and Superconductivity in La2SmNi2O7 Thin Films

The interplay between magnetic fluctuations and superconductivity is fundamental for understanding unconventional high-temperature superconductors. In the recently discovered Ruddlesden-Popper phase nickelates-which achieve superconducting transition temperatures up to ~100 K-this connection has been theoretically predicted but experimentally unverified. Using compressively strained La2SmNi2O7 thin films, we report direct evidence for this interplay. We observe a characteristic 'Mexican hat'-shaped magnetoresistance, a signature of superconductivity coexisting with emergent antiferromagnetic (AFM) fluctuations. This distinctive feature arises from a competition between the magnetic field's suppression of AFM fluctuation-driven scattering and its suppression of superconducting fluctuations. We quantify these AFM fluctuations by a crossover field, B*, whose magnitude decreases with increasing temperature and vanishes near the superconducting onset-behavior contrasting sharply with that in high-Tc cuprates. Our results establish not merely coexistence, but also a direct and novel correlation between AFM instability and superconductivity in nickelates, offering crucial insight into their pairing mechanism.

cond-mat.supr-con↗

arXiv2026

RAPID: Reconfigurable, Adaptive Platform for Iterative Design

Developing robotic manipulation policies is iterative and hypothesis-driven: researchers test tactile sensing, gripper geometries, and sensor placements through real-world data collection and training. Yet even minor end-effector changes often require mechanical refitting and system re-integration, slowing iteration. We present RAPID, a full-stack reconfigurable platform designed to reduce this friction. RAPID is built around a tool-free, modular hardware architecture that unifies handheld data collection and robot deployment, and a matching software stack that maintains real-time awareness of the underlying hardware configuration through a driver-level Physical Mask derived from USB events. This modular hardware architecture reduces reconfiguration to seconds and makes systematic multi-modal ablation studies practical, allowing researchers to sweep diverse gripper and sensing configurations without repeated system bring-up. The Physical Mask exposes modality presence as an explicit runtime signal, enabling auto-configuration and graceful degradation under sensor hot-plug events, so policies can continue executing when sensors are physically added or removed. System-centric experiments show that RAPID reduces the setup time for multi-modal configurations by two orders of magnitude compared to traditional workflows and preserves policy execution under runtime sensor hot-unplug events. The hardware designs, drivers, and software stack are open-sourced at https://rapid-kit.github.io/ .

cs.RO↗

arXiv2026

Cavity, lumped-circuit, and spin-based detection of axion dark matter: differences and similarities

Axions and axion-like particles are compelling candidates for ultralight bosonic dark matter, forming coherent oscillating fields that can be probed by experiments known as haloscopes. A broad range of haloscope concepts has been developed, including resonant cavity haloscopes, lumped-element circuit detectors, and spin-based experiments, each sensitive to different axion couplings and mass ranges. Rather than attempting an exhaustive survey of all existing approaches, this comparative review provides a unified framework for the major haloscope classes, establishing a common language for the descriptions of signal generation, noise properties, data analysis, and scanning strategies. Key properties of ultralight bosonic dark matter relevant for detection are summarized first, including coherence time, spectral linewidth, and stochasticity under the standard halo model. The discussion then compares cavity, Earth-scale, lumped-element, and spin haloscopes, focusing on expected signal shapes, dominant noise sources, and statistical frameworks for axion searches. Particular emphasis is placed on consistent definitions of signal-to-noise ratio and on how detector bandwidth, axion coherence, and noise characteristics determine optimal scan strategies. By systematically comparing operating principles and performance metrics across these detector families, this framework clarifies shared concepts as well as the essential differences that govern sensitivity in different mass and coupling regimes. The resulting perspective synthesizes current search methodologies and offers guidance for optimizing future haloscope experiments.

hep-ph↗

arXiv2026

Human-AI Co-Embodied Intelligence for Scientific Experimentation and Manufacturing

Scientific experimentation and manufacturing rely on prolonged protocol development and complex, multi-step implementation, which require continuous human expertise for precise execution and decision-making, limiting interpretability and scalability. Here, we introduce human-artificial intelligence (AI) co-embodied intelligence, a new form of physical AI that unites human researchers, agentic AI, and wearable hardware. In this paradigm, humans provide precise execution, while agentic AI contributes contextual reasoning, adaptive planning, and analysis. The wearable interface continuously captures experimentation and manufacturing, facilitating seamless communication between humans and AI. We instantiate this paradigm in a microfabrication cleanroom, leading to the agentic-physical experimentation (APEX) system which understands fabrication procedure with accuracy 51% higher than state-of-the-art multimodal large language models/vision language models (LLMs/VLMs), detects and corrects fabrication errors in real-time, and transfers procedural expertise to novice users. Critically, APEX system enables the co-development of fabrication protocols in cleanrooms, overcoming the incompatibility of elastomeric materials in standard microfabrication processes and enabling previously unattainable fabrication outcomes, as demonstrated by the wafer-scale realization of brain-level soft neural probe capable of single-unit-resolution neural recording. These results establish the human-AI co-embodied intelligence that extends agentic reasoning beyond computation into the physical domain, transforming scientific experimentation and manufacturing into autonomous, traceable, interpretable and scalable processes.

cs.AI↗

arXiv2026

AtPatch: Debugging Transformers via Hot-Fixing Over-Attention

Transformer-based deep neural networks (DNNs) affected by backdoor attacks and unfairness typically exhibit anomalous attention patterns, leading to over-attend to backdoor triggers or protected attributes. Existing neuron-editing mitigation strategies often struggle to handle such situation and most of them lack flexibility and tend to distort feature representations. Motivated by such over-attention phenomenon and software engineering paradigms such as delta debugging and hot patching, we propose AtPatch, a hot-fix method that dynamically redistributes attention maps during model inference. Specifically, for a given input, AtPatch first extracts the attention map from the model's inference process. Then, it uses a pre-trained detector to identify anomalous columns and replace them with unified benign attention. Then, AtPatch rescales other columns to mitigate the impact of over-attention. Finally, AtPatch returns the redistributed attention map to the model for continued inference. Notably, if the detector does not report any anomalous columns, AtPatch directly returns the original attention map to the model. Unlike existing techniques, AtPatch selectively redistributes the attention map, making it better at preserving the model's original functionality. Furthermore, AtPatch's on-the-fly nature allows it to work without modifying model parameters or retraining, making it better suited for deployed models. We conducted extensive experiments to validate AtPatch. Experimental results show that, compared to existing methods, AtPatch can more effectively mitigate backdoor attacks and unfairness while better preserving the model's original functionality.

cs.SE↗

arXiv2026

Implications of the KM3NeT Ultrahigh-energy Event on Neutrino Self-interactions

Neutrino self-interactions ($ν$SI) mediated by light bosonic particles can produce characteristic spectral dips in astrophysical neutrino fluxes, thereby altering the expected energy spectrum. The high-energy astrophysical neutrino spectrum has been extensively used to probe $ν$SI models through these distinctive features. The recent detection of the ultrahigh-energy event KM3-230213A presents a new opportunity to explore $ν$SI phenomenology at extreme energies. In this work, we investigate two implications of this observation, assuming the event originates from a diffuse power-law spectrum. First, we find that $ν$SI-induced spectral distortions can mildly alleviate the tension between the KM3-230213A detection and the previous non-observation of PeV-scale neutrinos in IceCube data. Second, we derive the strongest constraints on the $τ$-flavored $ν$SI coupling strength for mediator masses around 100 MeV. Our analysis shows that neutrino telescopes can surpass existing collider bounds in this mass range. In the near future, IceCube-Gen2 is expected to significantly enhance $ν$SI sensitivity, including regions relevant to alleviating the Hubble and neutrino mass tensions.

hep-ph↗

arXiv2026

May the Feedback Be with You! Unlocking the Power of Feedback-Driven Deep Learning Framework Fuzzing via LLMs

Deep Learning (DL) frameworks have served as fundamental components in DL systems over the last decade. However, bugs in DL frameworks could lead to catastrophic consequences in critical scenarios. A simple yet effective way to find bugs in DL frameworks is fuzz testing (Fuzzing). Existing approaches focus on test generation, leaving execution results with high semantic value (e.g., coverage information, bug reports, and exception logs) in the wild, which can serve as multiple types of feedback. To fill this gap, we propose FUEL to effectively utilize the feedback information, which comprises two Large Language Models (LLMs): analysis LLM and generation LLM. Specifically, analysis LLM infers analysis summaries from feedback information, while the generation LLM creates tests guided by these summaries. Furthermore, based on multiple feedback guidance, we design two additional components: (i) a feedback-aware simulated annealing algorithm to select operators for test generation, enriching test diversity. (ii) a program self-repair strategy to automatically repair invalid tests, enhancing test validity. We evaluate FUEL on the two most popular DL frameworks, and experiment results show that FUEL can improve line code coverage of PyTorch and TensorFlow by 4.48% and 9.14% over four state-of-the-art baselines. By the time of submission, FUEL has detected 104 previously unknown bugs for PyTorch and TensorFlow, with 93 confirmed as new bugs, 53 already fixed. 14 vulnerabilities have been assigned CVE IDs, among which 7 are rated as high-severity with a CVSS score of "7.5 HIGH". Our artifact is available at https://github.com/NJU-iSE/FUEL

cs.SE↗

arXiv2025

Charged Dirac perturbations on Reissner-Nordström black holes in a cavity: quasinormal modes with Robin boundary conditions

We investigate charged Dirac quasinormal spectra on Reissner-Nordström black holes in a mirror-like cavity. For this purpose, we first derive charged Dirac equations, and \textit{two} sets of Robin boundary conditions following the vanishing energy flux principle. The Dirac spectra are then computed both analytically and numerically. Our results reveal a symmetry hidden in the Dirac spectra between two boundary conditions. Moreover, when the cavity is placed close to the event horizon $r_+$, we identify that, in the neutral background the Dirac spectra asymptote to $-(3/8+N/2)i$ [$-(1/8+N/2)i$] for the first [second] boundary condition; while in the charged background the real part of charged Dirac spectra asymptote to $qQ/r_+$ for both boundary conditions; where $N$ is the overtone number, $q$ and $Q$ are charges for the field and for the background. In particular, we uncover a striking anomalous decay pattern, $i.e.$ the excited modes decay \textit{slower} than the fundamental mode, when the charge coupling $qQ$ is large. Our results further illustrate the robustness of vanishing energy flux principle, which are applicable not only to anti-de Sitter black holes but also to black holes in a cavity.

gr-qc↗

arXiv2025

Ultimate Quantum Precision Limit at Colliders: Conditions and Case Studies

We investigate whether collider experiments can reach the quantum limit of precision, defined by the quantum Fisher information (QFI), using only classical observables such as particle momenta. As a case study, we focus on the $τ^+τ^-$ system and the decay channel $τ\to πν$, which offers maximal spin-analyzing power and renders the decay a projective measurement. We develop a general framework to determine when collider measurements can, in principle, saturate the QFI in an entangled biparticle system, and this framework extends naturally to other such systems. Within this framework, QFI saturation occurs if and only if the symmetric logarithmic derivative (SLD) commutes with a complete set of orthonormal separable projectors associated with collider-accessible measurements. This separability condition, reflecting the independence of decay amplitudes, is highly nontrivial. To meet this condition, a key requirement is that the spin density matrix be rank-deficient, allowing the SLD sufficient freedom. We show that the classical Fisher information asymptotically saturates the QFI for magnetic dipole moments and CP-violating Higgs interactions in selected phase-space regions, but not for electric dipole moments. These results bridge quantum metrology and collider physics, providing a systematic method to identify quantum-optimal sensitivity in collider experiments.

hep-ph↗

arXiv2025

Impact of Simulation Box Size for Weak Lensing: Replication and Super-Sample Effects

We quantify the bias caused by small simulation box size on weak lensing observables and covariances, considering both replication and super-sample effects for a range of higher-order statistics. Using two simulation suites -- one comprising large boxes ($3750\,h^{-1}{\rm Mpc}$) and another constructed by tiling small boxes ($625\,h^{-1}{\rm Mpc}$) -- we generate full-sky convergence maps and extract $10^\circ \times 10^\circ$ patches via a Fibonacci grid. We consider biases in the mean and covariance of the angular power spectrum, bispectrum (up to $\ell=3000$), PDF, peak/minima counts, and Minkowski functionals. By first identifying lines of sight that are impacted by replications, we find that replication causes a O$(10\%)$ bias in the PDF and Minkowski functionals, and a O$(1\%)$ bias in other summary statistics. Replication also causes a O$(10\%)$ bias in the covariances, increasing with source redshift and $\ell$, reaching $\sim25\%$ for $z_s=2.5$. We additionally show that replication leads to heavy biases (up to O$(100\%)$ at high redshift) when performing gnomonic projection on a patch that is centered along a direction of replication. We then identify the lines of sight that are minimally affected by replication, and use the corresponding patches to isolate and study super-sample effects, finding that, while the mean values agree to within $1\%$, the variances differ by O$(10\%)$ for $z_s\leq2.5$. We show that these effects remain in the presence of noise and smoothing scales typical of the DES, KiDS, HSC, LSST, Euclid, and Roman surveys. We also discuss how these effects scale as a function of box size. Our results highlight the importance of large simulation volumes for accurate lensing statistics and covariance estimation.

astro-ph.CO↗

arXiv2025

Observation of Quantum-Criticality-Class Crossover at the LaAlO$_3$/KTaO$_3$ (111) Interface

In two-dimensional (2D) limit, the quantum fluctuation is significantly enhanced which could induce a quantum phase transition. Investigating the quantum criticality is an effective approach to elucidate the underlying physics of 2D superconductivity. Here we report the observation of different universality classes of quantum criticality at the superconducting LaAlO$_3$/KTaO$_3$ (111) interface, i.e. the normal quantum Griffiths singularity (QGS) and the anomalous QGS. The switch of the quantum criticality class is observed from the normal QGS in a low $T_c$ sample with weaker spin-orbit coupling (SOC) to the anomalous QGS in a high $T_c$ sample with stronger SOC. Moreover, owing to the remarkable SOC strength, the high $T_c$ sample exhibits an unprecedentedly enhanced quantum fluctuation at an unusually elevated temperature approaching the Berezinskii-Kosterlitz-Thouless (BKT) transition temperature $T_{BKT}$. This work provides comprehensive recognition of the different quantum criticality classes in the same superconducting LaAlO$_3$/KTaO$_3$ (111) system, which deepens the understanding of the superconducting mechanism of interfacial superconductors.

cond-mat.supr-con↗

arXiv2025

Interplay of spin-orbit coupling and trigonal crystal field enhances superconductivity in $LaAlO_3/KTaO_3$ (111)

In conventional superconductors, bulk physical properties typically degrade as the film thickness approaches the two-dimensional (2D) limit. Here in the (111) oriented LaAlO3/KTaO3 (LAO/KTO) heterostructure, we demonstrate experimental evidence that reducing the conducting layer thickness at the interface significantly enhances superconducting transition temperature Tc, in direct contrast to conventional wisdom. From the sum frequency generation (SFG) spectroscopy and superconducting upper-critical field measurements, both the trigonal symmetry and spin orbit scattering are enhanced with the increased Tc. We attribute the enhanced superconductivity (SC) to the synergic interplay between spin-orbit coupling (SOC) and trigonal crystal field, resulting in an enhanced electron-phonon coupling. Furthermore, we show the existence of unconventional SC: the approaching linear temperature dependence of normal state resistance with increasing Tc and the existence of a quantum critical point (QCP) near the superconducting phase. Our findings provide important insight into the underlying mechanism of the strong orientation-dependent KTO interface SC.

cond-mat.supr-con↗

arXiv2025

Resource Efficient Sleep Staging via Multi-Level Masking and Prompt Learning

Automatic sleep staging plays a vital role in assessing sleep quality and diagnosing sleep disorders. Most existing methods rely heavily on long and continuous EEG recordings, which poses significant challenges for data acquisition in resource-constrained systems, such as wearable or home-based monitoring systems. In this paper, we propose the task of resource-efficient sleep staging, which aims to reduce the amount of signal collected per sleep epoch while maintaining reliable classification performance. To solve this task, we adopt the masking and prompt learning strategy and propose a novel framework called Mask-Aware Sleep Staging (MASS). Specifically, we design a multi-level masking strategy to promote effective feature modeling under partial and irregular observations. To mitigate the loss of contextual information introduced by masking, we further propose a hierarchical prompt learning mechanism that aggregates unmasked data into a global prompt, serving as a semantic anchor for guiding both patch-level and epoch-level feature modeling. MASS is evaluated on four datasets, demonstrating state-of-the-art performance, especially when the amount of data is very limited. This result highlights its potential for efficient and scalable deployment in real-world low-resource sleep monitoring environments.

cs.LG↗

arXiv2025

Multi-Objective Bilevel Learning

As machine learning (ML) applications grow increasingly complex in recent years, modern ML frameworks often need to address multiple potentially conflicting objectives with coupled decision variables across different layers. This creates a compelling need for multi-objective bilevel learning (MOBL). So far, however, the field of MOBL remains in its infancy and many important problems remain under-explored. This motivates us to fill this gap and systematically investigate the theoretical and algorithmic foundation of MOBL. Specifically, we consider MOBL problems with multiple conflicting objectives guided by preferences at the upper-level subproblem, where part of the inputs depend on the optimal solution of the lower-level subproblem. Our goal is to develop efficient MOBL optimization algorithms to (1) identify a preference-guided Pareto-stationary solution with low oracle complexity; and (2) enable systematic Pareto front exploration. To this end, we propose a unifying algorithmic framework called weighted-Chebyshev multi-hyper-gradient-descent (WC-MHGD) for both deterministic and stochastic settings with finite-time Pareto-stationarity convergence rate guarantees, which not only implies low oracle complexity but also induces systematic Pareto front exploration. We further conduct extensive experiments to confirm our theoretical results.

cs.LG↗

arXiv2025

EcoLoRA: Communication-Efficient Federated Fine-Tuning of Large Language Models

To address data locality and privacy restrictions, Federated Learning (FL) has recently been adopted to fine-tune large language models (LLMs), enabling improved performance on various downstream tasks without requiring aggregated data. However, the repeated exchange of model updates in FL can result in prohibitively high communication costs, hindering the distributed learning process. To address this challenge, we propose EcoLoRA, a novel communication-efficient federated fine-tuning framework for LLMs. Leveraging the modular structure, we propose a round-robin segment sharing scheme, where each client uploads only a complementary LoRA segment per round to reduce network bandwidth. It is further combined with adaptive sparsification methods tailored to LoRA's training dynamics and lossless encoding techniques. We conduct extensive evaluations on both question-answering and value-alignment tasks across multiple datasets and models. The results show that EcoLoRA significantly reduces communication overhead without compromising performance. For instance, it reduces communication time by up to 79% and total training time by up to 65%.

cs.DC↗

arXiv2025

Every Activation Boosted: Scaling General Reasoner to 1 Trillion Open Language Foundation

We introduce Ling 2.0, a series reasoning-oriented language foundation built upon the principle that every activation boosts reasoning capability. Designed to scale from tens of billions to one trillion parameters under a unified Mixture-of-Experts (MoE) paradigm, Ling 2.0 emphasizes high sparsity, cross-scale consistency, and efficiency guided by empirical scaling laws. The series includes three non-thinking (instruct) models - Ling-mini-2.0, Ling-flash-2.0, and Ling-1T - ranging from 16B to 1T total parameters and achieving up to 7-fold active-compute efficiency compared with dense counterparts. Ling 2.0 integrates coordinated innovations across model architecture, pre-training, post-training, and infrastructure: a high-sparsity MoE with MTP for efficient reasoning, reasoning-oriented data and mid-training CoT activation, reinforcement-based fine-tuning (DFT, Evo-CoT), and full-scale FP8 training with fine-grained heterogeneous pipelines. At the trillion scale, Ling-1T establishes a new Pareto frontier of reasoning accuracy versus computational efficiency, demonstrating that sparse activation, when properly aligned with reasoning objectives, enables scalable and efficient intelligence. Collectively, Ling 2.0 provides a coherent, open, and efficient foundation for advancing future reasoning and thinking models, including the Ring series built upon the same base.

cs.CL↗

arXiv2025

Accurate humidity and pH synchronized measurement with temperature compensation based on polarization maintaining fiber

Real-time and accurate monitoring of humidity and pH is of great significance in daily life and industrial production. Existing humidity and pH measurement suffer from limitations such as low sensitivity, signal crosstalk, complex system structures, and inability to achieve real-time monitoring. In this work, the surface of a polarization maintaining fiber (PMF) was functionalized with a composite humidity-sensitive polymer composed of polyvinyl alcohol (PVA) and carbon nanosheets (CNs). A humidity-sensitive film with a microporous structure was prepared on the PMF cladding through high-temperature rapid film formation and laser processing, enhancing humidity sensitivity and stability. To enable pH sensing, poly(allylamine hydrochloride) (PAH) and poly (acrylic acid) (PAA) were successively adsorbed onto the PMF surface via electrostatic self-assembly, forming a pH-sensitive nanofilm structure. By connecting a temperature-compensated PMF within the same Sagnac loop and combining it with a multi-wavelength matrix, simultaneous real-time monitoring of humidity, pH, and temperature was achieved, effectively solving the issue of temperature crosstalk and extending toward a universal optical fiber multi-parameter measurement platform.

physics.app-ph↗