arXiv subjects

Jin Hu

Publications and source records attributed to Jin Hu.

At least 19 recordsLinked to original sources

arXiv2026

Impact of nuclear triaxial deformation on electromagnetic fields in relativistic $^{129}\mathrm{Xe}+^{129}\mathrm{Xe}$ collisions

Electromagnetic fields produced in relativistic heavy-ion collisions depend sensitively on the initial spatial distribution of nuclear charge. Using the Li\'enard--Wiechert potential with a triaxially deformed Woods--Saxon density, we calculate the transverse electric and magnetic field distributions in $^{129}\mathrm{Xe}+{}^{129}\mathrm{Xe}$ collisions at $\sqrt{s_{NN}}=5.44\text{ TeV}$. We systematically examine how the triaxiality angle $\gamma$ modifies the field structure at the collision time ($t=0$) in semi-central events, using spherical nuclear collisions as a baseline. The results show that nuclear triaxiality causes distinct spatial redistributions of both electric and magnetic fields in the transverse plane. These findings indicate that initial electromagnetic fields encode key information on intrinsic nuclear shapes, offering an additional constraint on nuclear deformation and its consequences for field-sensitive observables in heavy-ion collisions.

nucl-th↗

arXiv2026

Quantum simulation of bottomonium dynamics in the quark-gluon plasma via the Lindblad equation

Quantum computing provides a powerful framework for simulating real-time dynamics in open quantum systems, offering key advantages for modeling heavy-quarkonium transport in high-energy nuclear collisions. In this work, we perform quantum simulations of the isotropic next-to-leading-order Lindblad equation for bottomonium in the quark-gluon plasma using a reduced spherical coordinate representation. We discretize operators and wavefunctions, map the physical state onto qubits, and execute time evolution via parameterized quantum gate operations. By extracting the $\Upsilon(1S)$ survival probability, we quantitatively isolate the color-octet contribution, demonstrating that its overall impact is small in the final production of the bottomonium ground state $\Upsilon(1S)$ in the hot QCD medium at temperatures accessible at the Large Hadron Collider. Additionally, we have further optimized the quantum simulation algorithm for the Lindblad equation. The improved algorithm requires only a single ancillary qubit to realize the Lindblad evolution, thereby minimizing the circuit significantly.

nucl-th↗

arXiv2026

BDIP-Net: Dual-Interaction Graph Learning for Property Prediction of Bilayer Materials

Stacked bilayer materials exhibit rich stacking-dependent properties driven by the interplay between strong intra-layer bonding and weak inter-layer van der Waals interactions. The computational discovery of such materials is challenging because accurate structure generation typically relies on expensive DFT-based optimization, while existing machine-learning models often fail to explicitly distinguish different interaction types during property prediction. To address these challenges, we propose a machine-learning framework for efficient construction and property prediction of stacked bilayer materials. The framework employs a MatterSim-D3-based structural optimization workflow to generate DFT-quality bilayer structures from monolayer building blocks and stacking configurations at substantially reduced computational cost. For property prediction, we introduce BDIP-Net (Bilayer Dual-Interaction Potential Network), a graph neural network that explicitly models intra-layer and inter-layer interactions through interaction-specific potential representations and adaptive message fusion. We evaluate the proposed framework on BiDB, HetDB, and SAMBA, encompassing homobilayers, heterobilayers, and twisted bilayer systems. Results show that the MatterSim-D3-based workflow closely reproduces DFT-PBE-D3 optimized structures, while BDIP-Net consistently outperforms existing graph neural network and potential-based approaches for bilayer property prediction.

cs.LG↗

arXiv2026

Technical Report on the CVPR 2026@AdvML Workshop Challenge

Vision-language agents (VLAs) are increasingly used to interpret complex driving scenes and support safety-critical reasoning. This report presents the CVPR 2026@AdvML Workshop Challenge on adversarial multimodal attacks against autonomous-driving VLAs. Built on DriveLM-style multi-view visual question answering, the challenge represents each scene with six synchronized camera images and a structured collection of driving-related question-answer pairs. Participants generate adversarial images and suffix-only textual perturbations that induce model responses to deviate from reference answers while preserving image fidelity and limiting textual cost. The competition comprises two phases, with Phase II adding a hidden black-box model to assess transferability. We describe the task design, submission rules, evaluation protocol, and leaderboard results, and then examine five leading submissions for which technical reports were available. Across these reports, several recurring patterns emerge: image-side attacks are favored by the suffix penalty; scene-level, multi-view optimization is more effective than treating views in isolation; QA types and graph structure provide useful priors for allocating attack budget; feature-space objectives can improve black-box transfer; and typographic content embedded in camera images exposes a persistent vulnerability in driving VLAs. These findings provide a practical reference for future robustness evaluation and defense design in multimodal autonomous-driving systems.

cs.CV↗

arXiv2026

Retarded Correlators of Charge Transport in a Magnetic Field

We study charge transport in a magnetized relativistic plasma using kinetic theory within the relaxation-time approximation. By exactly solving the linearized Boltzmann equation in a uniform magnetic field, we obtain an analytic solution for the distribution function in terms of Bessel functions. Using this solution, we compute the full set of retarded current-current correlators and verify the Ward identities. In the hydrodynamic limit, we extract the charge diffusion modes, demonstrating that the transverse diffusion coefficient is strongly suppressed by the magnetic field, scaling as $1/B_0^2$ in the strong-field regime, while the longitudinal diffusion remains unaffected. Furthermore, we analyze the non-hydrodynamic branch cuts in the complex frequency plane, determining their kinematic thresholds and identifying the underlying wave-particle interactions as longitudinal Landau damping and transverse cyclotron damping.

hep-ph↗

arXiv2026

Engineering magnetic anisotropy and ferromagnetism in topological Kagome metal GdV6Sn6 via Nd substitution

Kagome metals with the formula RM6X6 (R = rare-earth, M = 3d transition metal, and X = Sn/Ge) provide a rich platform for exploring magnetic and electronic phenomena, with tunable properties enabled by the combination of rare-earth elements and transition metals. In this study, we report the structural, electrical and magnetic properties of Kagome metal (NdxGd1-x)V6Sn6. We demonstrate that substituting lighter Nd atoms at the Gd site tunes the complex magnetic ground state of GdV6Sn6 into a ferromagnetic-like one. Moreover, the isotropic magnetization of GdV6Sn6 becomes anisotropic, with the c-axis emerging as the easy axis. Transport measurements reveal a strong coupling between magnetism and electronic properties, with negative magnetoresistance observed at low magnetic fields in all compositions. In addition, a fourfold anisotropy component tends to emerge in end compounds at higher magnetic fields. These findings highlight the role of rare-earth substitution in tuning magnetic anisotropy and magneto-transport behaviour in RM6X6 compounds, featuring a non-magnetic Kagome layer, with potential implications for spintronic and topological applications.

cond-mat.mtrl-sci↗

arXiv2026

Graduate Training in Quantum Information Science and Engineering: Lessons, Challenges, and a Roadmap from the NSF Research Traineeship Programs

Since 2019, eighteen NSF Research Traineeship (NRT) awards in quantum information science and engineering (QISE) and adjacent fields have been funded, constituting the largest NSF-coordinated investment in graduate QISE training in the United States. Synthesizing lessons from our programs, we work through the central tensions that every QISE graduate program must negotiate: between depth in a home discipline and breadth across the field, between structured instruction and open-ended experiential and hands-on learning, and between training individual specialists and cultivating teams that collectively cover all areas of QISE. We describe the structural and pedagogical innovations the NRT programs have developed in response, assess what is working and what remains unresolved, and sketch 12 open problems the community will need to address as QISE graduate education scales beyond the well-resourced research universities where it has up till now been mainly concentrated. Eight concrete recommendations follow: (1) adopt the startup model of team-based training as an organizing philosophy; (2) invest immediately in sensing and communication curriculum development; (3) build student agency into program governance, not just activities; (4) establish structural mechanisms for industrial engagement rather than depending on goodwill; (5) design for sustainability from year one; (6) develop graduate-level textbooks spanning all three QISE pillars: computing, sensing, and communications; (7) establish shared outcome assessment instruments across programs; and (8) develop structured mechanisms for faculty professional development in QISE.

physics.ed-ph↗

arXiv2026

AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report

This report presents an overview of the AIM 2025 RipSeg Challenge, a competition designed to advance techniques for automatic rip current segmentation in still images. Rip currents are dangerous, fast-moving flows that pose a major risk to beach safety worldwide, making accurate visual detection an important and underexplored research task. The challenge builds on RipVIS, the largest available rip current dataset, and focuses on single-class instance segmentation, where precise delineation is critical to fully capture the extent of rip currents. The dataset spans diverse locations, rip current types, and camera orientations, providing a realistic and challenging benchmark. In total, $75$ participants registered for this first edition, resulting in $5$ valid test submissions. Teams were evaluated on a composite score combining $F_1$, $F_2$, $AP_{50}$, and $AP_{[50:95]}$, ensuring robust and application-relevant rankings. The top-performing methods leveraged deep learning architectures, domain adaptation techniques, pretrained models, and domain generalization strategies to improve performance under diverse conditions. This report outlines the dataset details, competition framework, evaluation metrics, and final results, providing insights into the current state of rip current segmentation. We conclude with a discussion of key challenges, lessons learned from the submissions, and future directions for expanding RipSeg.

cs.CV↗

arXiv2026

Normal mode analysis within relativistic massive transport

In this paper, we address the normal mode analysis on the linearized Boltzmann equation for massive particles in the relaxation time approximation. One intriguing feature of massive transport is the coupling of the secular equations between the sound and heat channels. This coupling vanishes as the mass approaches zero. By utilizing the argument principle in complex analysis, we determine the existence condition for collective modes and find the onset transition behavior of collective modes previously observed in massless systems. We numerically determine the critical wavenumber for the existence of each mode under various values of the scaled mass. Within the range of scaled masses considered, the critical wavenumbers for the heat and shear channels decrease with increasing scaled mass, while that of the sound channel exhibits a non-monotonic dependence on the scaled mass. In addition, we analytically derive the dispersion relations for these collective modes in the long-wavelength limit. Notably, kinetic theory also incorporates collisionless dissipation effects, known as Landau damping. We find that the branch cut structure responsible for Landau damping differs significantly from the massless case: whereas the massless system features only two branch points, the massive system exhibits an infinite number of such points forming a continuous branch cut.

hep-ph↗

arXiv2026

Chalcogen Doping Effect on the Insulator-to-Metal Transition in GdPS

Topological semimetals offer a rich platform for exploring massless fermion physics and realizing exotic properties with potential technological applications. GdPS, a magnetic semiconductor derived from the nodal-line semimetal ZrSiS family, exhibits a field-induced insulator-to-metal transition driven by exchange splitting. This transition is accompanied by an unusual, isotropic, and gigantic negative magnetoresistance, attributed to negligible magnetic anisotropy resulting from the weak spin-orbit coupling of half-filled Gd3+ 4f orbitals and light S atoms. In this work, we investigate Se substitution, which is expected to enhance spin-orbit coupling. Indeed, we observe slightly increased magnetic anisotropy in magnetotransport. Moreover, Se substitution suppresses the field-induced insulator-to-metal transition, likely due to an enlarged band gap that demands a higher exchange splitting to close. These findings provide deeper insights into the interplay between spin-orbit coupling, magnetic anisotropy, and transport behavior in GdPS, offering guidance for future materials design for desired functionalities.

cond-mat.mtrl-sci↗

arXiv2026

Ferromagnetic Spin Glass State and Anomalous Hall Effect in Topological Semimetal Candidate Mn2Sb2Te5

Materials that intrinsically possess both magnetism and topological states represent a key frontier of quantum materials research. Recently, Mn2(Bi/Sb)2Te5 has emerged as a promising candidate for hosting topological surface states coupled with intrinsic magnetic order, making it a potential magnetic Weyl semimetal. In this study, we investigate the magnetic and transport properties of Mn2Sb2Te5 single crystals. The magnetization measurements reveal a spin glass state with field-induced ferromagnetism. Although heat capacity measurement indicates the absence of long-range order, the intrinsic magnetization in Mn2Sb2Te5 significantly affects its electrical properties, as demonstrated by the anomalous Hall effect. This work provides valuable insights into the magnetism and the electronic properties of Mn2Sb2Te5, establishing Mn2(Bi/Sb)2Te5 system as a compelling platform for exploring the interplay between magnetism and non-trivial band topology, enabling emergent quantum phases and novel transport responses not accessible in non-magnetic systems.

cond-mat.mtrl-sci↗

arXiv2026

Let It Flow: Agentic Crafting on Rock and Roll, Building the ROME Model within an Open Agentic Learning Ecosystem

Agentic crafting requires LLMs to operate in real-world environments over multiple turns by taking actions, observing outcomes, and iteratively refining artifacts. Despite its importance, the open-source community lacks a principled, end-to-end ecosystem to streamline agent development. We introduce the Agentic Learning Ecosystem (ALE), a foundational infrastructure that optimizes the production pipeline for agentic model. ALE consists of three components: ROLL, a post-training framework for weight optimization; ROCK, a sandbox environment manager for trajectory generation; and iFlow CLI, an agent framework for efficient context engineering. We release ROME, an open-source agent grounded by ALE and trained on over one million trajectories. Our approach includes data composition protocols for synthesizing complex behaviors and a novel policy optimization algorithm, Interaction-Perceptive Agentic Policy Optimization (IPA), which assigns credit over semantic interaction chunks rather than individual tokens to improve long-horizon training stability. Empirically, we evaluate ROME within a structured setting and introduce Terminal Bench Pro, a benchmark with improved scale and contamination control. ROME demonstrates strong performance across benchmarks like SWE-bench Verified and Terminal Bench, proving the effectiveness of ALE.

cs.AI↗

arXiv2026

Macroscopic entanglement distribution with atomic ensembles

The distribution of entanglement is a crucial task for quantum communication towards realizing a globe-spanning quantum internet. Recently a protocol for deterministic long-distance distribution of macroscopic entanglement over a network of ensembles of qubits was introduced [Adv. Quantum Technol. 2025, 8, 2400524]. It was shown that this protocol allows for the propagation of macroscopic amounts of entanglement with a protocol complexity that is independent on the ensemble size. However, questions remained on whether the scheme is viable, particularly for a large particle number, which is the case for realistic atomic ensembles. Here we develop improved numerical techniques that allow calculation of realistic ensemble sizes up to 10^6 with a negligible loss of accuracy. We find that moderate dephasing leaves the entanglement largely intact at the magic times, whereas stronger noise monotonically suppresses the entanglement. Our results demonstrate that the protocol retains its functionality towards the macroscopic regime and provides quantitative benchmarks for its robustness under a realistic level of decoherence.

quant-ph↗

arXiv2026

Observation of a structurally driven, reversible topological phase transition in a distorted square net material

Topological materials hold immense promise for exhibiting exotic quantum phenomena, yet achieving controllable topological phase transitions remains challenging. Here, we demonstrate a structurally driven, reversible topological phase transition in the distorted square net material GdPS, induced via in situ potassium dosing. Using angle-resolved photoemission spectroscopy and first principles calculations, we demonstrate a cascade of topological phases in the sub-surface P layer: from a large, topologically trivial band gap to a gapless Dirac cone state with a 2 eV dispersion, and finally to a two-dimensional topological insulator as inferred from theory. This evolution is driven by subtle structural distortions in the first P layer caused by potassium adsorption, which in turn contribute to the band gap closure and topological phase transition. Furthermore, the ability to manipulate the topology of a sub-surface layer in GdPS offers a unique route for exploring and controlling topological states in bulk materials.

cond-mat.mtrl-sci↗

arXiv2026

Learning More from Less: Unlocking Internal Representations for Benchmark Compression

The prohibitive cost of evaluating Large Language Models (LLMs) necessitates efficient alternatives to full-scale benchmarking. Prevalent approaches address this by identifying a small coreset of items to approximate full-benchmark performance. However, existing methods must estimate a reliable item profile from response patterns across many source models, which becomes statistically unstable when the source pool is small. This dependency is particularly limiting for newly released benchmarks with minimal historical evaluation data. We argue that discrete correctness labels are a lossy view of the model's decision process and fail to capture information encoded in hidden states. To address this, we introduce RepCore, which aligns heterogeneous hidden states into a unified latent space to construct representative coresets. Using these subsets for performance extrapolation, RepCore achieves precise estimation accuracy with as few as ten source models. Experiments on five benchmarks and over 200 models show consistent gains over output-based baselines in ranking correlation and estimation accuracy. Spectral analysis further indicates that the aligned representations contain separable components reflecting broad response tendencies and task-specific reasoning patterns.

cs.AI↗

arXiv2026

Relaxation time approximation revisited and non-analytical structure in retarded correlators

In this paper, we give a rigorous mathematical justification for the relaxation time approximation (RTA) model. We find that only the RTA with an energy-independent relaxation time can be justified in the case of hard interactions. Accordingly, we propose an alternative approach to restore the collision invariance lacking in traditional RTA. Besides, we provide a general statement on the non-analytical structures in the retarded correlators within the kinetic description. For hard interactions, hydrodynamic poles are the long-lived modes. Whereas for soft interactions, commonly encountered in relativistic kinetic theory, the gapless eigenvalue spectrum of linearized collision operator leads to gapless branch-cuts. We note that particle mass and inhomogeneous perturbations would complicate the above-mentioned non-analytical structures.

hep-ph↗

arXiv2025

Rectifying Latent Space for Generative Single-Image Reflection Removal

Single-image reflection removal is a highly ill-posed problem, where existing methods struggle to reason about the composition of corrupted regions, causing them to fail at recovery and generalization in the wild. This work reframes an editing-purpose latent diffusion model to effectively perceive and process highly ambiguous, layered image inputs, yielding high-quality outputs. We argue that the challenge of this conversion stems from a critical yet overlooked issue, i.e., the latent space of semantic encoders lacks the inherent structure to interpret a composite image as a linear superposition of its constituent layers. Our approach is built on three synergistic components, including a reflection-equivariant VAE that aligns the latent space with the linear physics of reflection formation, a learnable task-specific text embedding for precise guidance that bypasses ambiguous language, and a depth-guided early-branching sampling strategy to harness generative stochasticity for promising results. Extensive experiments reveal that our model achieves new SOTA performance on multiple benchmarks and generalizes well to challenging real-world cases.

cs.CV↗

arXiv2025

Fermi-liquid view of viscosity in cold and dense nucleon matter

We develop a framework to calculate transport properties in cold, dense relativistic quasiparticle system within the Fermi-liquid theory at the mean-field level. Building on our previous study J. Li \emph{et al.} [Phys. Rev. C \textbf{111}, 044904 (2025)], we start from the linearized relativistic Boltzmann equation tailored to quasiparticles with medium-dependent dispersion relation and implement Landau matching conditions, proving that the bulk viscosity is manifestly nonnegative. A low-temperature expansion then yields leading-order ($T/\mu^*$) expressions for the shear ($\eta$) and bulk ($\zeta$) viscosities, where the behavior $\zeta/\eta \propto (T/\mu^*)^4$ in the degenerate regime is found to be robust against quasiparticle mass correction. We couple the kinetic framework to a Walecka-type mean-field equation of state and compute $\eta$ and $\zeta$ for cold, dense nucleon matter. The transport properties of nucleonic matter in the degenerate regime can be relevant for intermediate beam-energy nuclear experiments.

nucl-th↗