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Jinlong Yang

Publications and source records attributed to Jinlong Yang.

At least 19 recordsLinked to original sources
arXiv2026

Spin-Density-Driven Hybrid Spin Splitting in Collinear Compensated Magnets

Nonrelativistic spin splitting in collinear compensated magnets is constrained by spin-group symmetry, with distinct alternating and Zeeman-type splitting typically associated with altermagnets and fully compensated ferrimagnets, respectively. From a spin-density perspective, we propose a class of hybrid compensated magnets in which alternating-like and Zeeman-type spin splitting coexist within a single collinear compensated state. Using a minimal two-sector model, we demonstrate that this coexistence arises from competition between directional sublattice dispersion and the sector-dependent energy biases arising from the same structural inequivalence. First-principles calculations provide a proof of concept in a crossed bilayer of unequal-width Janus graphene nanoribbons, where the structurally inequivalent ribbons retain approximately symmetry-related spin-density distributions. The valence and conduction sectors support direction-selective spin transport and energy-selective spin-polarized states, respectively. These results connect the spatial organization of spin density with the electronic response to structural inequivalence, providing a design principle for hybrid spin splitting in compensated magnets.

cond-mat.mtrl-sci↗
arXiv2026

Realizing Two-Dimensional Altermagnets with 100% Spin Polarization along the [110] Direction in Ambidentate-Ligand Based Square Organometallic Lattices

Altermagnetism, as a novel magnetic paradigm, not only possesses a zero net magnetic moment, but also exhibits a substantial nonrelativistic spin-splitting effect. Currently, developing a simple and experimentally feasible method for designing altermagnets is eagerly pursued. Moreover, achieving 100% spin polarization along certain directions in valence or conduction bands for altermagnets remains challenging. To address these issues, based on a tight-binding model, we propose a viable chemical strategy for constructing organometallic altermagnets by using ambidentate ligands. Furthermore, in these organometallic altermagnets, direction-dependent 100% spin polarization along the [110] direction in band edges can be induced via the coexistence of altermagnetically spin-split dispersive and flat bands, where the flat band suppresses electron transport in one spin channel and the dispersive band enables conduction in the other spin channel. Then, via first-principles calculations, a screening of 90 two-dimensional ambidentate-ligand based square organometallic lattices leads to the identification of three altermagnetic materials, namely Fe(taz)2Cl2 (taz = 1,2,4- triazine), Co(app)2Cl2, and Fe(app)2Cl2 (app = 1,4-azaphosphinine), all of which possess 100% spin polarization along the [110] direction in valence bands, as well as notable piezomagnetic behavior. This study paves the way for achieving direction-dependent fully spin-polarization in altermagnetic materials.

cond-mat.mtrl-sci↗
arXiv2026

Alter-excitons in Altermagnets: Symmetry, Splitting and Dispersion

Magnetism provides a fertile platform for discovering emergent quantum phenomena governed by symmetry. Recent developments of spin-space-group (SSG) theory have revealed altermagnets as a distinct class of nonrelativistic collinear magnets with unconventional spin-dependent electronic structures. Here, we demonstrate that altermagnetic symmetry imposes fundamentally different constraints on electronic and excitonic excitations. Although the quasiparticle and two-body Hamiltonians possess identical SSG symmetries, we find that symmetry-protected degeneracies present in the electronic band structure can be intrinsically lifted in the exciton spectrum at specific momenta. This splitting arises solely from the many-body nature of excitons and does not require spin-orbit coupling or noncollinear magnetic order. Combining symmetry analysis with many-body perturbation theory, we predict a novel class of spin-split excitonic quasiparticles, termed alter-excitons, and reveal their characteristic dispersions in altermagnetic systems. Our results establish excitons as a sensitive probe of hidden spin-space symmetries and uncover a new route toward symmetry-engineered many-body phenomena in collinear magnets.

physics.comp-ph↗
arXiv2026

High-Throughput Discovery of Chiral Carbon-Boron Semimetals with Weyl Topology and Phonon-Mediated Superconductivity

Chiral crystals provide a natural platform for unconventional topological fermions, while light-element carbon--boron frameworks are promising for phonon-mediated superconductivity owing to strong covalent bonding and high phonon frequencies. Here, we combine Wyckoff-position-based structure generation with high-throughput first-principles calculations to search for C--B crystals in selected hexagonal chiral space groups. Starting from 972 generated $\mathrm{C}_m\mathrm{B}_n$ structures ($m,n=1\text{--}4$), successive filters based on energetic stability, dynamical stability, electronic metallicity, topological band features, and thermal stability identify six stable candidates: $(\mathrm{C}_3\mathrm{B}_2)_8$, $(\mathrm{C}\mathrm{B}_2)_3$, $(\mathrm{C}\mathrm{B}_4)_6$, $(\mathrm{C}_3\mathrm{B}_2)_6$, $(\mathrm{C}_2\mathrm{B}_3)_6$, and $(\mathrm{C}\mathrm{B}_2)_6$. These materials host diverse topological semimetal states near the Fermi level, including charge-one, charge-two, and charge-three Weyl points, nodal rings, and nodal-surface states. Notably, $(\mathrm{C}_3\mathrm{B}_2)_8$ exhibits screw-symmetry-protected charge-three Weyl points and strong electron--phonon coupling, yielding a McMillan transition temperature of $31.1\,\mathrm{K}$ for the Coulomb pseudopotential parameter $μ^* = 0.10$. In addition, $(\mathrm{C}\mathrm{B}_2)_3$ hosts multiple charge-one Weyl points and pronounced anisotropic multiband superconductivity revealed by fully anisotropic Eliashberg calculations. These results extend the materials scope of topological semimetals with phonon-mediated superconductivity to chemically designed chiral light-element structures.

cond-mat.mtrl-sci↗
arXiv2026

DCVD: Dual-Channel Cross-Modal Fusion for Joint Vulnerability Detection and Localization

Software vulnerability detection plays a critical role in ensuring system security, where real-world auditing requires not only determining whether a function is vulnerable but also pinpointing the specific lines responsible. However, existing approaches either rely on a single information source -- sequential, structural, or semantic -- failing to jointly exploit the complementary strengths across modalities, or treat statement-level localization merely as a byproduct of function-level detection without explicit line-level supervision. To address these limitations, we propose DCVD (Dual-Channel Cross-Modal Vulnerability Detection), a unified framework that performs joint function-level detection and statement-level localization. DCVD extracts control-dependency and semantic features through two parallel branches and integrates them via contrastive alignment coupled with bidirectional cross-attention, effectively bridging the cross-modal representation gap. It further introduces explicit supervision signals at both the function and statement levels, enabling collaborative optimization across the two granularities. Extensive experiments on a large-scale real-world vulnerability benchmark demonstrate that DCVD consistently outperforms state-of-the-art methods on both function-level detection and statement-level localization. Our code is available at https://github.com/vinsontang1/DCVD.

cs.CR↗
arXiv2026

Cross-Preference Learning for Sentence-Level and Context-Aware Machine Translation

Context-aware machine translation (MT) leverages document-level information, yet it does not consistently outperform sentence-level MT, as contextual signals are unevenly beneficial across sentences. Existing training objectives do not explicitly model this variability, limiting a model's ability to adaptively exploit context. In this paper, we propose Cross-Preference Learning (CPL), a preference-based training framework that explicitly captures the complementary benefits of sentence-level and context-aware MT. CPL achieves this by integrating both intra- and cross-condition preferences into the preference optimization objective, providing explicit supervision to exploit informative context while remaining robust to uninformative context. We validate the proposed approach on several public context-aware MT tasks using multiple models, including Qwen3-4B, Qwen3-8B, and Llama-3-8B-Instruct. Experimental results demonstrate consistent improvements in translation quality and robustness across both input conditions, achieved without any architectural modifications.

cs.CL↗
arXiv2026

BRACE: Taming Sharp Irregularities via Barycentric Rational Forecasting for Fast Diffusion Transformers Inference

Diffusion Transformers (DiTs) have demonstrated exceptional performance in high-fidelity image and video generation. To alleviate their massive computational overhead, temporal feature caching has been proposed to bypass redundant computations. However, existing cache-then-forecast methods driven by derivative-based polynomials often cause severe quality degradation under high acceleration due to unstable long-step predictions. To address this bottleneck, we propose Barycentric Rational Forecasting with Chebyshev Enhancement (BRACE). Motivated by the observation that DiT feature trajectories are globally smooth yet frequently exhibit sharp irregularities and local non-smoothness, BRACE shifts the paradigm from derivative-driven polynomial extrapolation to feature-driven rational forecasting. Specifically, it maintains a local sliding window to cache sparse historical features and leverages adapted Chebyshev weights to formulate a barycentric rational function, directly aggregating these raw features to ensure numerical stability. Extensive experiments demonstrate that BRACE achieves state-of-the-art quality-efficiency trade-offs across various DiT architectures with negligible computational overhead.

cs.CV↗
arXiv2026

Scaling Neural Network Quantum States for Ab Initio Quantum Chemistry

Neural-network quantum states (NNQSs) can represent many-electron wave functions without explicitly enumerating the determinant space, but their accuracy depends jointly on model size and variational-optimization effort. Here we characterize this dependence for a physics-conditioned autoregressive NNQS trained separately on two six-molecule source benchmarks. Across eight model sizes and five optimization milestones, we find that model size and optimization steps jointly shape the energy error. The capacity advantage of larger models becomes more apparent with sufficient optimization, while the returns from additional optimization vary with model size. We capture this coupling using an interaction scaling law and quantify the cumulative compute of each evaluated configuration. The resulting error-compute Pareto frontiers provide a practical decision rule for jointly selecting model size and optimization steps under a given compute budget within the evaluated range. Furthermore, we find that this beneficial scaling trend persists during fine-tuning on held-out N$_2$. Pretrained models show decreasing error with increasing model size, with a steeper reduction following pretraining on the Hard benchmark. Together, these results place autoregressive neural quantum states within the broader landscape of empirical neural scaling and open a quantitative route toward the systematic scaling of neural quantum solvers for ab initio quantum chemistry.

physics.chem-ph↗
arXiv2026

Perturbative Variational Quantum Eigensolver via Reduced Density Matrices

Current noisy intermediate-scale quantum (NISQ) devices remain limited in their ability to perform accurate quantum chemistry simulations due to restricted numbers of high-fidelity qubits and short coherence times. To overcome these challenges, we introduce a reduced density matrix (RDM)-based perturbative variational quantum eigensolver (VQE) framework that augments active-space VQE with perturbation theory to recover electron correlation beyond the active space without increasing the qubit count or variational circuit depth. We formulate a fully coupled approach (VQE-PTs) and a diagonal approximation (VQE-PT). The former retains couplings among orthonormalized perturbers, whereas the latter neglects these couplings to simplify the classical post-processing. Numerical simulations of HF, N$_2$, and F$_2$ show that VQE-PTs provides a robust formulation across different molecular systems, while VQE-PT offers an efficient approximation. We further experimentally implement VQE-PT on the Quafu superconducting quantum processor for F$_2$, achieving a mean absolute error of 1.2 millihartree along the potential energy surface after error mitigation. These results demonstrate perturbative VQE as a practical framework for incorporating dynamic correlation in quantum chemistry simulations.

quant-ph↗
arXiv2026

Harmonic Hierarchy of Altermagnetic Spin Splitting from Symmetry-Adapted Wavefunctions

Altermagnets combine magnetic compensation with spin-momentum-locked splitting in the absence of spin-orbit coupling, yet existing descriptions, formulated primarily in terms of spin symmetry and lattice geometry, provide limited insight into the electric-structure perspective of its angular harmonic form. Here, we identify a wavefunction-level framework for altermagnetism in two-dimensional square lattices. Using symmetry-adapted polynomial wavefunctions, we show that the harmonic structure of momentum-space spin splitting is inherited from the geometry of the electronic wavefunctions which can be selected by crystal fields. Identical orbital sectors preserve conventional antiferromagnetic degeneracy, whereas intertwined linear and quadratic wavefunctions generate d-wave and g-wave altermagnetic anisotropies, respectively. Tight-binding analysis connects this hierarchy to inequivalent same-spin hopping channels. First-principles calculations on the g-wave mcm-type reticular material platforms confirm high-symmetry-linear degeneracy together with finite generic-k splitting. Our results establish a hierarchy linking wavefunction geometry, orbital realization, microscopic hopping anisotropy, and altermagnetic electronic structure.

cond-mat.mtrl-sci↗
arXiv2026

CADER: Confidence-Aware Dynamic Evidence Reasoning for Long-Video Understanding

Long-video understanding increasingly relies on large vision-language models and tool-augmented reasoning, but most systems apply the same inference procedure to every example regardless of difficulty. This uniform strategy invokes unnecessary tool-assisted processing for easy questions and provides limited control when difficult questions require fine-grained temporal evidence. We propose CADER (Confidence-Aware Dynamic Evidence Reasoning), a training-free framework for adaptive and reliable long-video reasoning. CADER first performs global reasoning over uniformly sampled frames and estimates answer confidence with a logit-margin signal, allowing high-confidence examples to exit early. For uncertain examples, CADER activates a second-stage tool-augmented loop that combines temporal cropping, lightweight semantic verification, and Relevance-Guided Resampling to progressively localize question-relevant evidence. This design treats tool use as a sample-level decision: a single global pass handles easy cases, while additional reasoning is reserved for examples where uncertainty suggests that more evidence is needed. Experiments on multiple VideoQA benchmarks show that CADER improves long-video reasoning while bypassing Stage~2 for high-confidence samples. Moreover, when applied to a backbone trained only with tool-free chain-of-thought supervision, CADER achieves competitive performance against specialized tool-augmented frameworks, suggesting a practical inference-time route for adaptive long-video reasoning.

cs.CV↗
arXiv2026

A High-Performance Pauli-Algebra Framework for Large-Scale Quantum Simulations

Efficient manipulation of Pauli-algebraic objects is a key bottleneck in the classical emulation and benchmarking of quantum algorithms for chemistry and many-body physics. This bottleneck appears in Hamiltonian construction, variational ansatz preparation, expectation-value and gradient evaluation, and real-time propagation, all of which require repeated Pauli-algebra operations. Here, we present a high-performance Pauli-algebra framework tailored to quantum many-body and quantum-chemical simulations. The framework combines compact binary symplectic encoding, canonical coefficient reduction, and grouped sparse operator representations that exploit shared bit-flip patterns among Pauli strings. The resulting Julia/C\texttt{++} implementation accelerates Pauli multiplication, Hamiltonian construction, and operator--state multiplication in sparse and symmetry-adapted many-electron spaces. Benchmarks demonstrate efficient Hamiltonian construction, large-active-space VQE and ADAPT-VQE calculations, and real-time variational dynamics on modern multicore CPU and GPU architectures. These results show that structure-aware Pauli-algebra engines provide a scalable classical backend for developing and benchmarking quantum algorithms in quantum chemistry and many-body simulation.

quant-ph↗
arXiv2026

Heteroskedastic Signals in Budgeted LLM Verification: Structural Heterogeneity Limits Optimization Gains

Selective-compute LLM systems decide which outputs merit verification, additional reasoning, tool execution, or human audit under a limited budget. It is natural to expect that stronger online optimization over a shared uncertainty or reward signal should improve these decisions. We take a critical look at this assumption and ask: when does optimizing harder fail because the signal is not decision-comparable across inputs? In budgeted LLM verification, we find that uncertainty quality is heteroskedastic across cost strata: some regions exhibit near-random discriminability while concentrating many errors. Under an explicit local model, we characterize the resulting distortion of global allocation and show that its upper bound scales with cross-stratum signal-quality dispersion. To separate weak signals from optimizer instability and structural mismatch, we introduce a controlled intervention hierarchy: Threshold, MP-Adapt, MP-Strat, and cost-stratified thresholding (CST). We then turn the diagnosis into Heterogeneity-Gated Allocation (HGA), which uses a warm-up comparability test to choose between global and cost-stratified allocation. Across MBPP and MATH using Qwen3-8B, LLaMA3-8B, and GPT-4o-mini, global online adaptation yields inconsistent gains over static thresholding; CST improves hit rate by up to 17 percentage points in strongly heterogeneous settings, while HGA preserves most gains and avoids blind stratification when the partition is not useful. These findings suggest a resource-allocation principle for LLM systems: before optimizing harder over a shared proxy, test whether the proxy is decision-comparable across observable operating regimes, and gate structural specialization on that test.

cs.AI↗
arXiv2026

Geometry-Driven Magnetoelectric Coupling in Two-Dimensional Compensated Ferrimagnets

The magnetoelectric coupling in compensated magnets enables stray-field-free manipulation of spin-splitting, holding great promise for spintronics, but inherently hindered by the symmetry mismatch between spatial-inversion-broken ferroelectricity and time-reversal-broken spin states. Here, based on a symmetry-decoupled analysis of magnetoelectric coupling in compensated magnets, we establish a geometry-driven spin-ferroelectric coupling mechanism in bilayer breathing kagome lattices. Within this geometric framework interlocking the out-of-plane electric polarization with cooperative intralayer structural distortions, we demonstrate that polarization switching drives a deterministic reversal of the global spin splitting. First-principles calculations on a prototype bilayer Nb3Cl8 successfully validate this mechanism, demonstrating the switching of spin-splitting states through an energetically feasible, asynchronous layer-by-layer transition pathway. Our proposed coupling originates from lattice geometry and structural symmetry, establishing a unique route toward switchable spin splitting in compensated ferrimagnets.

cond-mat.mtrl-sci↗
arXiv2026

VEPHand: View-Efficient Photometric Hand Performance Capture at Scale

Robust, high-fidelity 3D hand capture, while fundamental to digital human creation, remains challenging with practical multi-view systems that balance rich photometry with the geometric ambiguities of reconstruction arising from limited viewpoint density. This paper presents an end-to-end pipeline for dynamic hand performance capture and registration, specifically designed for view-efficient setups ($\sim$20 views). We address key challenges with two primary innovations. First, to overcome reconstruction difficulties like limited view overlap and background clutter, our mask-free neural method robustly extracts detailed hand geometry and appearance from unmasked images using scene parameterization and scenario-specific density regularization. Second, addressing registration challenges such as accurately capturing non-linear skin deformations and ensuring plausible results during severe self-contact, we propose a physics-inspired framework. It aligns reconstructions to a personalized hand model by optimizing intrinsic volumetric offsets within its canonical tetrahedral mesh, alongside pose parameters. This approach, supported by robust losses and optimization, captures fine surface deformations, ensures plausible results under severe articulation and self-contact, and demonstrates strong tolerance to input noise. We demonstrate the scalability and robustness of our automated pipeline on an extensive dataset of over 12,000 sequences, from which we also derive a large-scale, high-quality synthetic 2D/3D hand dataset for training downstream tasks. This showcases its effectiveness for single hands, intricate two-hand interactions, and natural hand-object manipulations. Our method achieves state-of-the-art reconstruction fidelity in view-efficient, unmasked scenarios and highly accurate registration. Our project page are available at https://vephand.github.io/.

cs.CV↗
arXiv2026

Transformer refined quantum sampling for strongly correlated electronic structure

Although quantum computing offers a promising solution for strongly correlated system simulation, existing algorithms face significant bottlenecks on current noisy intermediate-scale quantum (NISQ) devices. Here, we introduce QiankunNet-QSCI, a hybrid quantum-classical framework that addresses this challenge by combining efficient quantum-sampling with a transformer neural network. An efficient unitary selected configuration Interaction (USCI) ansatz especially designed for quantum sampling is proposed to identify the most chemically significant electronic configurations on the Zuchongzhi 3.1 quantum processor. Subsequently, the transformer model QiankunNet learns from these sparse yet critical quantum data to infer and reconstruct the complete electronic wavefunction with high fidelity. Simulation of the challenging 40-qubit [2Fe-2S] ferredoxin active center achieves chemical accuracy. Simulation of the nitrogenase P-cluster in a 114-electron 73-orbital active space also reaches 12 milli-Hartree-level agreement with the best density matrix renormalization group (DMRG) result. QiankunNet-QSCI thus offers a practical route to accurate quantum-assisted electronic structure calculations on current devices.

quant-ph↗
arXiv2026

FusionRCG: Orchestrating Recursive Computation Graphs across GPU Memory Hierarchies

Evaluating high-dimensional integrals via deep hierarchical recurrences is a dominant cost in quantum chemistry. While CPUs manage these efficiently, GPUs suffer a critical mismatch: limited per-thread memory is quickly overwhelmed by an explosion of simultaneously live intermediate variables. As recurrence scales, this forces massive data spilling to global memory, collapsing performance into a severe memory-bound regime. We present FusionRCG, a framework that jointly optimizes computation graph structure and GPU memory mapping. Exploiting the inherent topological flexibility of recurrence graphs, using electron repulsion integrals as an example, we contribute: (1) liveness-aware graph orchestration to minimize peak live intermediates; (2) algebraic dimensionality reduction via stepwise Cartesian-to-spherical fusion, shrinking intermediate footprints by up to $7.7\times$; and (3) an adaptive multi-tier kernel architecture routing graphs across the memory hierarchy. Evaluated on NVIDIA A100 GPUs, FusionRCG achieves up to $3.09\times$ end-to-end SCF speedup over GPU4PySCF and maintains $75\%$ parallel efficiency at 64~GPUs, successfully rescuing these workloads from memory-bound limits.

physics.comp-ph↗
arXiv2026

VulTriage: Triple-Path Context Augmentation for LLM-Based Vulnerability Detection

Automated vulnerability detection is a fundamental task in software security, yet existing learning-based methods still struggle to capture the structural dependencies, domain-specific vulnerability knowledge, and complex program semantics required for accurate detection. Recent Large Language Models (LLMs) have shown strong code understanding ability, but directly prompting them with raw source code often leads to missed vulnerabilities or false alarms, especially when vulnerable and benign functions differ only in subtle semantic details. To address this, we propose VulTriage, a triple-path context augmentation framework for LLM-based vulnerability detection. VulTriage enhances the LLM input through three complementary paths: a Control Path that extracts and verbalizes AST, CFG, and DFG information to expose control and data dependencies; a Knowledge Path that retrieves relevant CWE-derived vulnerability patterns and examples through hybrid dense--sparse retrieval; and a Semantic Path that summarizes the functional behavior of the code before the final judgment. These contexts are integrated into a unified instruction to guide the LLM toward more reliable vulnerability reasoning. Experiments on the PrimeVul pair test set show that VulTriage achieves state-of-the-art performance, outperforming existing deep learning and LLM-based baselines on key pair-wise and classification metrics. Further ablation studies verify the effectiveness of each path, and additional experiments on the Kotlin dataset demonstrate the generalization ability of VulTriage under low-resource and class-imbalanced settings. Our code is available at https://github.com/vinsontang1/VulTriage

cs.AI↗