arXiv subjects

Qiang Li

Publications and source records attributed to Qiang Li.

At least 55 records · Page 3Linked to original sources

arXiv2026

RESTOR: Automated Test Oracle Generation for RESTful APIs via Reinforcement Learning

Modern REST API testing faces a critical challenge in defining reliable test oracles, particularly in agile industrial environments where formal specifications (e.g., OpenAPI) are frequently missing or outdated, and historical execution logs are unavailable for newly deployed endpoints. In this paper, we present Restor (Reinforcement Enhanced Single-Traffic Oracle generator for REST APIs), a framework that generates executable test assertions from a single observed request-response pair in a black-box setting. Unlike existing approaches that rely on rule-based templates or massive training logs, Restor utilizes a novel data augmentation pipeline to fine-tune a lightweight Large Language Model (LLM) via Group Relative Policy Optimization (GRPO). This training process enables the model to internalize testing "common sense" by optimizing a reward function that jointly encourages: (i) the selection of stable, semantically meaningful fields for validation and the avoidance of dynamic noise (e.g., timestamps or trace IDs); (ii) the generation of robust assertions that withstand logic variations. We evaluate Restor on an industrial dataset comprising over 2,300 API traces across 246 real-world services. Comprehensive experiments demonstrate that Restor significantly outperforms prompt-engineered baselines and generalist models, achieving a superior $F_1$ score of 85.42% in key field identification and increasing the proportion of semantically accurate assertions. Furthermore, deployment in a production CI/CD workflow at ByteDance confirms its practical value: the system raised the adoption rate of automatically generated test cases from 74.1% to over 96%, substantially reducing manual Quality Assurance (QA) effort while ensuring high execution stability.

cs.SE↗

arXiv2026

RadioTrace: Transmitter-Aware Diffusion for Radio Map Estimation without Deployment-Time Fine-Tuning

Radio map (RM) estimation aims to reconstruct the spatial distribution of wireless signal characteristics, such as received signal strength (RSS), from sparse measurements, a task that is critical for spectrum management, interference mitigation, and localization in modern wireless networks. Traditional approaches, including interpolation and deep learning, either struggle to capture complex propagation effects or require large-scale retraining for each new sampling pattern, which limits their generalization. More recently, prior-based methods have combined pre-trained generative models with measurements to reduce the need for deployment-time model fine-tuning, but they typically treat the prior as a simple regularizer and lack explicit transmitter-aware integration. In this paper, we propose RadioTrace, a novel RM estimation framework without deployment-time fine-tuning that tightly integrates sparse RSS measurements with a frozen pre-trained diffusion prior. RadioTrace incorporates transmitter (Tx) location estimation directly into the denoising loop, iteratively refining Tx coordinates based on reconstruction quality to guide the generative process. To further enhance robustness, we introduce a propagation-guided K-means initialization that mitigates poor local minima in the Tx update and provides a geometry-consistent starting point. Moreover, we provide a stochastic stability analysis for the Tx-coordinate refinement component, showing that the Tx update remains stable under perturbations induced by diffusion sampling and Tx-map relaxation. Extensive experiments demonstrate that RadioTrace achieves competitive performance with state-of-the-art learning-based methods under random sampling, and maintains strong reconstruction quality under restricted-area sampling, highlighting its adaptability, robustness, and practical relevance.

eess.SP↗

arXiv2026

Single event effects in the HCCStar ASICs for ITk strip upgrade

Triple Modular Redundancy (TMR) technology has been implemented in the design of the HCCStar to reduce digital state changes and ensure reliable operation. We tested the effectiveness of the protection by placing HCCStar chips in a proton beam. We studied corrected bit flips in registers and actual single event effects in the LCB and LP paths under different proton energies. Our estimate is that the LP data-loss fraction relative to the 400 kHz readout is $O(10^{-10})$ during normal operation and there will be $O(10)$ corrected bit flips per HCCStar bit per year at the HL-LHC.

physics.ins-det↗

arXiv2026

IBoxCLA: Towards Robust Box-supervised Segmentation of Polyp via Improved Box-dice and Contrastive Latent-anchors

Box-supervised polyp segmentation attracts increasing attention for its cost-effective potential. Existing solutions often rely on learning-free methods or pretrained models to laboriously generate pseudo masks, triggering Dice constraint subsequently. In this paper, we found that a model guided by the simplest box-filled masks can accurately predict polyp locations/sizes, but suffers from shape collapsing. In response, we propose two innovative learning fashions, Improved Box-dice (IBox) and Contrastive Latent-Anchors (CLA), and combine them to train a robust box-supervised model IBoxCLA. The core idea behind IBoxCLA is to decouple the learning of location/size and shape, allowing for focused constraints on each of them. Specifically, IBox transforms the segmentation map into a proxy map using shape decoupling and confusion-region swapping sequentially. Within the proxy map, shapes are disentangled, while locations/sizes are encoded as box-like responses. By constraining the proxy map instead of the raw prediction, the box-filled mask can well supervise IBoxCLA without misleading its shape learning. Furthermore, CLA contributes to shape learning by generating two types of latent anchors, which are learned and updated using momentum and segmented polyps to steadily represent polyp and background features. The latent anchors facilitate IBoxCLA to capture discriminative features within and outside boxes in a contrastive manner, yielding clearer boundaries. We benchmark IBoxCLA on five public polyp datasets. The experimental results demonstrate the competitive performance of IBoxCLA compared to recent fully-supervised polyp segmentation methods, and its superiority over other box-supervised state-of-the-arts with a relative increase of overall mDice and mIoU by at least 6.5% and 7.5%, respectively.

cs.CV↗

arXiv2026

Optical Magnetic Switching in Odd-Parity Magnets with Spin-Orbit Coupling

$p$-wave magnets exhibit odd-parity spin polarization in momentum space, with spin splitting that reverses under $\vec{k}\rightarrow -\vec{k}$, while preserving zero net magnetization. Here we show that, in odd-parity magnets with spin-orbit coupling, elliptically polarized light generates a momentum-independent spin-dependent term that dynamically switches a zero-net-magnetization $p$-wave state into a finite spin-polarized state. The Floquet-engineered bands also acquire a nonzero Chern number whose sign is controlled by the light polarization. For $f$-wave magnets, circularly polarized light induces a net out-of-plane magnetization, offering a direct experimental signature. Our results establish light as an efficient means of controlling magnetic states, with potential applications in spintronics and quantum information.

cond-mat.str-el↗

arXiv2026

When will T Coronae Borealis next erupt as a nova? Constraints from recurrence, orbital phase, and accretion-state evolution

T Coronae Borealis (T CrB) is the nearest symbiotic recurrent nova and is now intensively monitored for its next eruption. We combine three constraints on the eruption time: historical recurrence, orbital phase, and recent accretion-state evolution. Conditioning on no eruption by 2026 July 11, the three effective historical intervals give illustrative survival-conditioned probabilities of 30.2\% for the rest of 2026 and 56.9\% within the following year; these are empirical indicators, not physical prediction probabilities. The four adopted historical eruption phases do not select a unique ignition phase, but form two loose pairs near $ϕ\simeq0.44$ and $ϕ\simeq0.62$, used here only as monitoring windows. The 1946 pre-eruption dip is difficult to explain by either a pure accretion-rate decline or standard dust extinction, and may have involved both accretion restructuring and source-dependent obscuration. If the renewed 2026 decline is the true pre-1946 analogue, an eruption around 2026 December remains plausible. Conversely, if post-2024 brightness remains below the 2014--2023 high state, an accretion-deficit estimate gives an earliest lower limit near 2029 May. The data therefore support conditional monitoring windows, not a unique date. These are conditional diagnostic scenarios rather than competing point predictions; the eventual eruption epoch will test their underlying assumptions.

astro-ph.HE↗

arXiv2026

Physics-grounded generative design of inherently stable, novel and controllable crystal structures

Generative inverse design is reshaping the discovery of functional crystalline materials. Yet current generative models face challenges in simultaneously achieving stability, novelty, and precise controllability in a single trained model. We address these challenges with a key physical insight: the diversity of crystals is governed by their crystallographic information (CI), namely composition, space group and lattice, whereas only a few stable atomic configurations remain once the CI is fixed. Built on this insight, we introduce SCGEN (stable and controllable crystal structure generation), a physics-grounded generative model with two components. A variational autoencoder samples diverse, physically plausible CI, and a symmetry- and Wyckoff-position-constrained optimizer locates stable atomic positions via universal machine-learning potentials. Benchmarked on roughly two million structures, SCGEN reaches state-of-the-art stability while preserving comparable novelty, and it satisfies any specified composition, space group, lattice or joint constraint with 100% success and no task-specific retraining. Applied to photocatalytic water splitting, property-guided optimization with SCGEN generates 200,000 candidate structures and identify top 22 stable, active and synthesizable photocatalysts. By decoupling CI generation from coordinate optimization, SCGEN establishes a physics-grounded inverse-design paradigm that yields synthesis-ready crystals on demand, rather than structures requiring post hoc repair, relaxation, or retraining.

cond-mat.mtrl-sci↗

arXiv2026

Nuisance-Aware Muon Tomography

Cosmic-ray muon scattering tomography can image dense, shielded, or inaccessible objects without an artificial radiation source. In a compact magnet-free tracker, however, each accepted muon provides only a few hit positions and no event-by-event momentum measurement. The downstream hit residual is therefore a compound observable: target scattering, muon momentum, detector resolution, support material, air scattering, and track extrapolation all enter the same measured displacement. We introduce Nuisance-Aware Muon Tomography (NAMT), a residual-likelihood reconstruction method for magnet-free trackers. The upstream hits define the incident track, downstream hit residuals carry the scattering signal, and a radiation-length density field $λ=1/X_0$ predicts their material-induced variance through a path integral. NAMT marginalizes the unmeasured momentum with a shared event-level scattering scale and uses open-field blank scans to fix detector and environmental residuals before object reconstruction. On eight Geant4 benchmark scenes spanning strong, weak, and negative scattering contrast, NAMT-4P reaches a mean area under the ROC curve (AUC) of $0.916$ at $120$k effective muons and $1$ mm hit error, compared with $0.784$ for ASR, $0.749$ for MLS-EM, and $0.643$ for PoCA. NAMT-3P uses one downstream hit plane in reconstruction and still reaches $0.909$ mean AUC at the reference setting, while giving the highest reference mean contrast-to-noise ratio (CNR) and the best mean AUC at $30$k muons.

physics.ins-det↗

arXiv2026

A Large-Scale Dataset and a New Method for RemoteSensing Traffic Object Segmentation

Remote sensing imagery plays a crucial role in evaluating regional transportation capacity. However, existing segmentation datasets often lack diversity in object categories and scenes, limiting the ability of models to comprehensively evaluate trans portation capacity in real-world scenes. To alleviate this gap, we construct a large-scale and diverse dataset for transportation object segmentation, named as NWPU-Traffic. This dataset encompass four traffic object categories (car, airplane, ship, and train) and a wide range of scenes from 49 cities across 7 countries, with instance-level annotations to ensure precise segmentation of individual objects, which bridges critical shortcomings in resolution and scene diversity in existing datasets. Leveraging this dataset, we establish a benchmark with several popular segmentation networks. Furthermore, we propose a novel segmentation method that leverages spatial-channel preserving feature interaction and an adaptive feature decoder, enabling robust segmentation across varying scales and complex environments. Extensive experiments and ablation studies validate the effectiveness of our approach. The dataset and code are publicly available at https://github.com/CVer-Yang/NWPU-Traffic.

cs.CV↗

arXiv2026

Quantum entanglement of photon pairs at proton-proton colliders

Diphoton systems, with photon polarizations measurable at low energies, serve as ideal qubits and were first used to demonstrate quantum entanglement. However, due to the current absence of dedicated polarimeters at high-energy colliders, the entanglement properties of diphoton systems remain largely unexplored at the high-energy frontier. Studying quantum entanglement at the high-energy frontier, where particle colliders serve as a natural relativistic laboratory, helps us better understand the quantum nature and seek new physics. In this letter, we propose a novel method to measure the entanglement of diphoton systems at proton-proton colliders. The photon spin analyzing power arises from the Bethe-Heitler process occurring in the tracker, where photons scatter off nuclei to produce electron-positron pairs whose joint angular distribution encodes the polarization of the diphoton system. Simulation results show that, under HL-LHC conditions, the statistical significance of quantum entanglement in the Higgs $\to γγ$ process is $0.007σ$, while measuring the continuum diphoton process $q\bar{q} \to γγ$ alone can reach about $1.5σ$.

hep-ph↗

arXiv2026

Mask to Concept: Auto-Promptable SAM3 via Efficient Test-Time Concept Embedding Search for Few-Shot Annotation

Transforming foundation segmentation models from human-prompted tools into auto-promptable annotators is critical for scalable medical data annotation. Current methods commonly depend on external feature matchers or auxiliary networks to automate geometric prompting, but introducing architectural overhead and limiting performance scalability. Although SAM3 natively supports concept segmentation via reusable text prompts, its direct use in medical imaging is hindered by a lack of fine-grained clinical knowledge and the ambiguity of human-written descriptions. In this work, we propose Mask to Concept (M2C), an efficient framework that adapts SAM3 for medical few-shot annotation without external modules, parameter retraining, or manual text engineering. Using only a few labeled images, M2C enables SAM3 to automatically search for transferable visual concepts entirely within its frozen architecture: it initializes a learnable concept embedding, uses it to prompt segmentation, and updates the embedding by gradients of minimizing the concept segmentation error. We further introduce a Hybrid Uncertainty Estimation (HUE) module that calculates the prediction entropy and maps concept predictions back to the box prompts, measuring concept-geometry prompting inconsistency. Highly uncertain samples are flagged actively for human correction, and the corrected masks are then fed back to M2C to continuously search for more precise concept embeddings, forming a self-enhancing annotation loop with minimal expert effort. Experiments on medical segmentation benchmarks show that our method achieves SOTA few-shot segmentation performance and outstanding annotation efficiency, offering a practical and efficient pathway toward scalable medical image labeling. Codes are at https://github.com/Huster-Hq/M2C.

cs.CV↗

arXiv2026

Lack of Significant Orbital-Phase Locking in the Active Phases of the Recurrent Nova T CrB

T Coronae Borealis (T CrB) is a symbiotic recurrent nova (RN) that exhibits both nova eruptions and long-term active phases resembling superoutbursts and normal outbursts. Motivated by proposed connections between these events and the binary orbit, we test whether the onset, maximum, or termination of the active phases is locked to orbital phase. We use long-term optical $B$- and $V$-band light curves from the American Association of Variable Stars Observers (AAVSO) International Database and historical photometry from the literature. We measure the onset, maximum, and termination times of superoutbursts and normal outbursts and convert these times to orbital phase. We test the resulting circular distributions with Kuiper and Watson statistics. We find no statistically significant orbital-phase locking. The onset phases and maxima are consistent with a uniform phase distribution. The smallest probabilities occur for the 13 measurable termination phases ($p_{\rm MC}=0.043$ for the Kuiper statistic and $p_{\rm MC}=0.048$ for the Watson statistic), but this result is only marginal in an uncorrected $p<0.05$ sense, far from a $3σ$ detection, and insufficient to establish robust phase locking. The four historical nova eruptions likewise do not provide robust evidence for a unique ignition phase once the small sample size, historical date uncertainties, and long-term period changes are considered. The two known secondary eruptions occurred at similar phases, but two events are insufficient to establish an orbital-geometry connection. Overall, the active phases of T CrB appear to be governed primarily by accretion-disk physics rather than by a fixed binary phase.

astro-ph.HE↗

arXiv2026

Gravitational effect of plasma particles on the shadow of Schwarzschild black holes

Considering a Schwarzschild black hole surrounded by a fully ionized hydrogen plasma, we study the effect of the gravitational field of the plasma particles on the shadow. We take a formalism in which this effect is unified with the refractive effect of the plasma medium studied previously, but the two effects are characterized by two independent parameters. For semi-realistic values of parameters, we find their corrections to the shadow radius are both negligible, and the gravitational correction can overtake the refractive correction for active galactic nuclei of masses larger than $10^9M_{\odot}$. With unrealistically large values of parameters, we illustrate the two effects on the light trajectories and the intensity map.

gr-qc↗

arXiv2026

"Hadron-in-fat-jet'' AI Tagging to Detect Rare Decays Such as $W^{\pm}\toπ^{\pm}γ$

We investigate a novel class of boosted-object signatures at the LHC, where a high-$p_{\text{T}}$ fat-jet contains an identifiable hadron or quarkonium state originating from rare or semi-exclusive decays. Unlike conventional boosted jet studies, which focus on multi-prong partonic substructure, our approach probes hybrid configurations such as $W^{\pm}\toπ^{\pm}γ$, where a localized hadronic or quarkonium signal is embedded within a collimated jet. By fine-tuning the signature-oriented, pre-trained Sophon AI model optimized for large-radius jets, and combining it with an event-level BDT and a soft-drop-mass shape fit, we obtain an expected 95\% CL upper limit of ${\cal B}(W^{\pm}\toπ^{\pm}γ)<2.78\times10^{-5}$ for $450\,\mathrm{fb}^{-1}$ in our nominal setup. This study serves as a first proof-of-principle demonstration of the ``hadron-in-fat-jet'' paradigm; substantial gains in sensitivity are expected from improved trigger strategies, additional production channels, and dedicated taggers, while the methodology itself is broadly applicable to a wide range of rare Standard Model processes and searches for light or exotic resonances at present and future collider experiments.

hep-ph↗

arXiv2026

Learning to Trim: End-to-End Causal Graph Pruning with Dynamic Anatomical Feature Banks for Medical VQA

Medical Visual Question Answering (MedVQA) models often exhibit limited generalization due to reliance on dataset-specific correlations, such as recurring anatomical patterns or question-type regularities, rather than genuine diagnostic evidence. Existing causal approaches are typically implemented as static adjustments or post-hoc corrections. To address this issue, we propose a Learnable Causal Trimming (LCT) framework that integrates causal pruning into end-to-end optimization. We introduce a Dynamic Anatomical Feature Bank (DAFB), updated via a momentum mechanism, to capture global prototypes of frequent anatomical and linguistic patterns, serving as an approximation of dataset-level regularities. We further design a differentiable trimming module that estimates the dependency between instance-level representations and the global feature bank. Features highly correlated with global prototypes are softly suppressed, while instance-specific evidence is emphasized. This learnable mechanism encourages the model to prioritize causal signals over spurious correlations adaptively. Experiments on VQA-RAD, SLAKE, SLAKE-CP and PathVQA demonstrate that LCT consistently improves robustness and generalization over existing debiasing strategies.

cs.CV↗

arXiv2026

Ask4VG: Risk-Aware Question Selection for Reducing Prior-Driven Answers in Medical VQA

Medical visual question answering requires models to ground their responses in image evidence, because visually unsupported answers can mislead downstream interpretation. However, many medical VQA questions are generic, template-like, or highly similar in form, which can encourage models to learn question-answer shortcuts instead of image-dependent reasoning and thereby increase the risk of hallucinated responses. We propose Ask4VG, a label-free pilot framework for risk-aware question selection. Ask4VG estimates question-induced hallucination risk through counterfactual visual probing: the same question is asked under the original image, a perturbed image, a blank image, and a mismatched image, and the resulting answer relations are converted into weak supervision for a counterfactual risk estimator. The learned estimator then reranks candidate question rewrites to favor intent-preserving questions that are less invariant to missing or mismatched visual evidence before final answer generation. On VQA-RAD with Qwen2-VL-2B-Instruct, prompt-only rewriting increases counterfactual risk, whereas predicted-risk reranking reduces held-out risk from 0.658 to 0.623 and improves exact accuracy from 0.337 to 0.356. A 300-sample PMC-VQA external check shows the same direction of risk reduction with a small accuracy gain. These results suggest that question selection is a promising complement to response-level hallucination mitigation for reliable medical VQA.

cs.CV↗

arXiv2026

Pure Spin Photocurrent in Altermagnetic Photovoltaic Battery

Altermagnets, featuring momentum-dependent spin splitting without net magnetization, provide a promising platform for spintronic functionalities beyond conventional ferromagnets and antiferromagnets. Here, we propose an altermagnetic spin photovoltaic battery consisting of a nonmagnetic semiconducting layer sandwiched between two altermagnetic electrodes. Using first-principles quantum-transport simulations, we show that a V2Te2O/ZnSe/V2Te2O junction supports a pure spin photocurrent for opposite Néel vectors in the two altermagnetic electrodes, with spin-up and spin-down photocurrents equal in magnitude and opposite in sign. The effect persists under both linearly and circularly polarized light and remains tunable with photon energy and polarization angle. Our results establish a realistic route toward light-driven pure spin-current generation in altermagnetic junctions.

cond-mat.mes-hall↗

arXiv2026

SinFormer: A Tailored Transformer for Robust Radio Frequency Fingerprint Identification

With the rapid proliferation of wireless and Internet of Things (IoT) devices, ensuring secure and reliable device identification has become a significant challenge. Traditional security techniques, such as IP or MAC address-based authentication, are susceptible to spoofing, whereas Radio Frequency Fingerprint Identification (RFFI) offers a more secure alternative by exploiting the unique hardware imperfections in devices' RF signals. In this paper, we propose a novel deep learning-based framework for RFFI that enhances both accuracy and reliability in challenging RF environments. The core of our approach is the Signal Inception Transformer (SinFormer), which leverages a specialized multi-scale self-attention mechanism to effectively capture both large-scale and fine-grained fingerprints in signals, significantly improving identification accuracy. To further enhance robustness and reliability, we introduce a two-stage training strategy that enables the model to learn general signal features and maintain performance under adverse conditions, such as low Signal-to-Noise Ratio (SNR) or channel variations. The effectiveness of the proposed method is validated using a real-world dataset. Experimental results show that the SinFormer framework consistently outperforms existing methods in accuracy and robustness across diverse and challenging scenarios.

cs.IT↗