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Ziqiang Wang

Publications and source records attributed to Ziqiang Wang.

At least 19 recordsLinked to original sources
arXiv2026

Nodeless $d$-wave superconductivity driven by charge fluctuations in monolayer FeSe on SrTiO$_3$

Monolayer FeSe and Fe(Te,Se) grown on SrTiO$_3$ exhibit some of the highest superconducting transition temperatures among iron-based superconductors, yet their pairing mechanism and symmetry remain elusive. Motivated by the recent surprising discovery of nodeless $d$-wave superconductivity using scanning Josephson tunneling microscopy (SJTM) [T. Wei et al., arXiv:2610.38421 (2026)], we investigate superconductivity in monolayer FeSe/SrTiO$3$ (1L-FeSe/STO) within an extended Hubbard model incorporating both onsite and intersite Coulomb interactions among Fe $3d$ and Se $4p$ orbitals. We show that Fe-Se intersite Coulomb repulsion strongly enhances small-momentum $p$-$d$ charge-transfer fluctuations, which mediate attractive intraorbital spin-singlet pairing interactions. These electronic fluctuations may be further enhanced by forward-scattering optical phonons of the SrTiO$3$ substrate. The resulting intrapocket attraction, together with residual repulsive interpocket interactions, stabilizes a mirror-odd, nodeless $B_{1g}$ ($d$-wave) superconducting state with opposite gap signs on the two electron pockets. Both Fe $t_{2g}$ and Se $4p$ orbitals contribute substantially to this multiorbital superconductivity. The calculated superconducting gap and density of states exhibit a two-peak structure, while the Josephson tunneling conductance displays a half-lattice-period modulation, in excellent agreement with experimental observations. Our results establish electronic charge-transfer fluctuations as a viable pairing mechanism for unconventional nodeless $d$-wave superconductivity in monolayer FeSe/STO.

cond-mat.supr-con↗
arXiv2026

Direct real-space imaging of sign-reversal superconducting state by scanning Josephson tunneling

Phase-sensitive experiments based on Josephson tunneling have been considered as a pivotal method to determine the pairing symmetry in high-temperature (high-Tc) cuprate superconductors. However, in iron-based superconductors, traditional phase-sensitive experiments face setbacks because of the complexity of pairing symmetry in multiband systems. Here, using scanning Josephson tunneling microscopy (SJTM), we report the half-unit-cell periodic Josephson tunneling modulations in monolayer high-Tc Fe(Te,Se) film epitaxially grown on SrTiO3(001). The Josephson tunneling almost vanishes along the lines connecting next-nearest-neighbor Fe atoms under an s-wave superconducting tip. Combined with single-particle tunneling spectra showing the full superconducting gap, our experimental results are consistent with the sign-reversal nodeless d-wave pairing symmetry. Our findings provide sub-unit-cell scale superconducting phase information for understanding the mechanism of high-Tc superconductivity. We anticipate our work as a starting point for atomically resolved Josephson interference experiments using SJTM in various unconventional superconductors.

cond-mat.supr-con↗
arXiv2026

Engineering tunable $p$-wave magnetism in antiferromagnetic bilayers

We propose a symmetry-guided route to engineer tunable $p$-wave magnetism in a bilayer system composed of two AB-stacked antiferromagnetic square lattices with their collinear moments nonparallel to each other. We show that an in-plane relative shift between the two layers selectively breaks the symmetries protecting spin degeneracy while preserving time-reversal-related constraints, thereby generating odd-parity spin splitting in a fully compensated magnetic state without spin-orbit coupling. The direction and the magnitude of the resulting $p$-wave spin-splitting, together with the associated spin responses, can be continuously tuned by the in-plane displacement, providing a potential knob for control. We further map the bilayer system onto an effective bond-modulated square lattice and investigate the corresponding Hubbard model within a mean-field framework. The calculated phase diagram reveals extended regions where $p$-wave magnetism with coplanar or chiral spin textures emerges spontaneously. Our results establish a minimal equilibrium platform for realizing and manipulating $p$-wave magnetism in two-dimensional systems.

cond-mat.str-el↗
arXiv2026

Field Response of the Orbital Moment Probed by Integrated Magnetic Circular Dichroism

Magnetic circular dichroism (MCD) is an optical probe of orbital time reversal symmetry breaking. Although frequency integrated MCD is related to the self-rotation contribution to orbital magnetization at zero field, its magnetic response generally differs from the thermodynamic orbital susceptibility. Here, we define the MCD susceptibility and derive general expressions for multiband systems using an electromagnetic $U(1)$ gauge invariant approach. For insulators, the response contains contributions from the non-Abelian quantum metric, Berry curvature, and field induced interband mixing. For metals, a finite temperature Green function formulation separates the smooth response into Fermi sea and Fermi surface contributions. An independent Landau level calculation confirms the expressions for the zero-field MCD moment and its linear magnetic response. In a continuum model for twisted bilayer graphene, the MCD susceptibility vanishes within the charge neutrality gap, while the thermodynamic orbital susceptibility remains markedly diamagnetic. Near the gap edges, the orbital moment term dominates the response. Features near the van Hove energies are associated with density of states enhancement. These results clarify how the two susceptibilities reflect different orbital processes even for the same electronic structure and thus establish the MCD susceptibility as a distinct probe of orbital magnetic response.

cond-mat.str-el↗
arXiv2026

ANT: A Multi-Granularity Network Traffic Dataset and Benchmark for Agents Behavior Auditing

The growing adoption of large language model (LLM) agents creates a need for network administrators and security teams to audit agent behavior within organizational networks without inspecting private user content. Network traffic offers an observable source of evidence, but how much it reveals about agent tasks and operations remains unclear. Existing traffic datasets lack the joint task and stage annotations needed to evaluate this question. We introduce ANT (Agent Network Traffic), a dataset providing agent behavior information at risk, scenario, and behavior primitive granularities alongside network traffic. ANT contains 3,114 execution episodes across 20 tasks and five scenarios, comprising 276,417 bidirectional flows and 40,049 behavior primitive segments organized into 47 macro groups. We establish a benchmark for agent risk identification, scenario recognition, and behavior primitive classification using 13 representative traffic analysis baselines. The results show that existing methods recover useful but uneven behavioral signals. They struggle to identify risk when malicious workflows resemble benign tasks and to distinguish scenarios with similar traffic patterns. Primitive classification is more reliable for frequent macro groups and those with distinctive traffic patterns than for rare or semantically similar groups. ANT provides a common basis for developing more precise auditing and forensic analysis of agent behavior from network traffic. Our data and code are available at https://anonymous.4open.science/r/ant-main-suite-7BC0/.

cs.AI↗
arXiv2026

NetLexicon: Learning Discrete Behavioral Representations for Encrypted Web Traffic Analysis

Encrypted Web traffic analysis requires effective representations of observable communication behavior. Existing pretraining methods often adapt NLP/CV objectives and sequence architectures, motivating learning objectives that capture traffic-specific interaction patterns. We present NetLexicon, a discrete pretraining framework that learns reusable behavioral states from unlabeled traffic. It converts contextual traffic windows into discrete states through vector quantization, constructing a compact traffic lexicon. We design two complementary pretraining objectives. State Transition Prediction (STP) forecasts subsequent sequence structure and packet features from observed history, while Statistical Feature Alignment (SFA) grounds learned states in window-level traffic statistics. Together, they guide the lexicon to capture recurring communication behaviors and their evolution. We evaluate NetLexicon on four benchmarks covering Web application identification, service type identification, and malware detection. NetLexicon improves Macro-F1 by up to 25.5 percentage points over the strongest baseline on each benchmark and reduces fine-tuning time per epoch by up to 23.6 times relative to the evaluated baselines. Further analysis shows that the learned discrete states capture recognizable patterns in packet size, timing, and data transfer. These results demonstrate that incorporating observable behavioral structure into pretraining supports effective, efficient, and interpretable representations for encrypted traffic analysis.

cs.NI↗
arXiv2026

One Rollout Is All You Get: Fully Test-Time Adaptation for GUI Agents

GUI agents are deployed with frozen weights and discard everything they experience on the job. Existing ways to update an agent's weights assume something deployment withholds: ground truth, rollouts beyond the single attempt (retries, samples, practice runs), or a learning phase other than deployment. Because GUI actions can be irreversible, a deployed agent gets one attempt per task occurrence, in arrival order, and every attempt counts. No ground truth is available at any point. We define fully test-time adaptation for GUI agents by these constraints and pair it with a minimal weight-space method, SOLO. Auxiliary models read each episode: a judge selects the episodes it deems successful, and a proposer-verifier pair relabels a failed episode's prefix with the subtask that prefix completed. Admitted episodes enter a short sliding window, and each admission updates a small adapter by top-K self-distillation on the agent's own predictions, provided the window holds a judged success. On recurring task streams built from WebArena, VisualWebArena and MobileWorld, SOLO improves on the frozen agent with both UI-TARS-7B and Qwen3-VL-8B, by three to six points of success rate, and exceeds two in-setting memory methods on the web streams.

cs.LG↗
arXiv2026

Contribution of remote bands to orbital magnetization in twisted bilayer graphene

Motivated by recent theoretical and experimental works on orbital magnetization $M_{\mathrm{orb}}$ for the interacting system, we develop a gauge-invariant framework to compute $M_{\mathrm{orb}}$ for correlated phases of magic-angle twisted bilayer graphene within self-consistent Hartree-Fock approximation. Based on the projector formulation of the theory of orbital magnetization, we evaluate both $M_{\mathrm{orb}}$ and the self-rotation contribution $m_{\mathrm{SR}}$ directly from the Hartree-Fock Hamiltonian. We demonstrate that, in contrast to topological invariants such as the Chern number, both $M_{\mathrm{orb}}$ and $m_{\mathrm{SR}}$ obtain substantial contributions from remote bands and thus require careful convergence with respect to the number of included remote bands. Applying this approach to correlated phases at integer fillings, we obtain converged $M_{\mathrm{orb}}$ and $m_{\mathrm{SR}}$ for time reversal symmetry broken Chern insulating states at $ν=\pm3$ and for competing correlated phases at other integer fillings. Our results establish a systematic and controlled approach for evaluating orbital magnetization in correlated moiré systems and clarify the crucial role of remote bands in determining their magnetic response.

cond-mat.str-el↗
arXiv2026

Intertwined Charge Stripes and Majorana Zero Modes in An Iron-Based Superconductor

In type-II superconductors, magnetic fields modulate the amplitude and phase of the superconducting order parameter, forming quantized vortices where superconductivity is locally suppressed and exotic bound states or competing electronic orders emerge. Using spectroscopic-imaging scanning tunneling microscopy on epitaxial Ba(Fe$_{0.94}Co$_{0.06})$_2$As$_2$ films, we discover an incommensurate charge-stripe order aligned with the Fe-Fe bond direction. These charge modulations become strongly enhanced at the vortex core and extend far into the vortex halo. Strikingly, the charge order modulates Andreev bound states of vortices at non-zero energies, producing abelian vortices with half-odd-integer level quantization and non-abelian vortices with integer-quantized core states that host a Majorana zero mode. The distinct vortex types are distinguished by the registry of their centers relative to the charge-stripe pattern and remain robust in ultrathin (2.5-unit-cell) films. Our findings reveal a density-wave-textured vortex matter and provide fresh insights into intertwined phenomena in iron-based superconductors.

cond-mat.supr-con↗
arXiv2026

When Words Are Safe But Actions Kill: Probing Physical Jailbreak Beyond Textual Jailbreak in Hidden-State Risk Space

Large language models (LLMs) increasingly serve as high-level planners for embodied agents, where linguistically benign instructions can become unsafe once grounded in the physical world. We study whether this physically grounded jailbreak is the same safety problem as ordinary textual jailbreak. Through hidden-state direction analysis and random-split null tests, we show that textual jailbreak (TJ) and physical jailbreak (PJ) form separable signals in LLM representations across Qwen2.5-3B/7B/14B/32B, Phi-3.5 and SmolLM2. Building on this separability, we propose PRISM, a single-layer L2-regularized logistic probe over full hidden states. PRISM achieves 86.2--87.7\% accuracy on SafeAgentBench with 11.7--13.7\% false-positive rates (FPRs), while same-scale LLM judges over-block safe tasks at 24.7--39.0\% FPR. To test whether the result survives lexical-shortcut controls, we introduce an interaction-balanced revision of PhysicalJailbreakBench-2K (PJB-2K): a fixed 2{,}000-row comparison set sampled by label and physical mechanism from a larger object--site construction. On the underlying 10{,}000-row pool, word-TFIDF and the embedding layer remain at chance (AUC 0.497 and 0.500). At layer 25, selected by an i.i.d. sweep, cell-grouped cross-validation gives PRISM 0.718 AUC, compared with 0.398 for a physics-free label control under the same protocol. On the identical 2{,}000 comparison rows, these PRISM predictions obtain 0.671 balanced accuracy, while Qwen2.5 judges from 3B to 72B obtain 0.538--0.577 and exhibit high FPR. These results support hidden-state probing as a representation-level method for physical safety beyond text moderation, without relying on the near-perfect scores of shortcut-prone paired templates.

cs.AI↗
arXiv2026

CoAdapt-GUI: Joint Workflow Context and Policy Adaptation for Unseen GUI Applications

Mobile GUI agents remain brittle when deployed to applications absent from source training. We study novel-app generalization under a limited target interaction budget and without target demonstrations. We introduce CoAdapt-GUI, a test-time adaptation (TTA) framework that jointly adapts structured workflow context and policy from the agent's own target-app rollouts and rewards. The workflow context retains transferable procedures, failure modes, and verification rules while excluding app-bound source details. This separation allows reusable workflow knowledge to guide adaptation without transferring source-interface state. For policy adaptation, task-context-matched group-relative optimization updates a LoRA adapter on a frozen vision-language model. Across two unseen-app evaluations, CoAdapt-GUI reaches 45.0% on AndroidWorld-Generalization, compared with 37.5% for the reported Policy-Only TTA baseline, and raises AndroidWorld Plus performance from 38.6% to 52.9%. These results show that transfer-constrained workflow context provides substantial gains and that joint policy adaptation further improves held-out performance.

cs.AI↗
arXiv2026

Benchmarking LLM Judges for Mobile Agent Evaluation

Mobile agent benchmarks increasingly rely on LLM-based judges to evaluate task completion, yet the reliability of these judges on mobile agent trajectories remains largely unexamined. We introduce MobileJudgeBench, a benchmark for systematically evaluating LLM-as-judge methods on mobile agent trajectories. Our benchmark comprises 931 human-annotated trajectories spanning 6 mobile agent benchmarks, 4 agent models, and 68 apps. We evaluate 6 judge methods (five adapted from SPA-Bench, A3 with two modes, AndroidArena, and AgentRewardBench, plus a simple baseline we design) across multiple LLM backends. Our experiments reveal three key findings. First, a simple baseline judge with sampled screenshots is competitive with, and often exceeds, purpose-built methods, indicating that more elaborate judge pipelines do not consistently improve judge quality; among competitive methods, the LLM backbone is the primary driver. Second, benchmark quality metrics reliably predict real-world judge utility: they correlate with both agent ranking fidelity for evaluation and downstream performance when judges serve as reward signals for on-policy reinforcement learning. Third, failure analysis across two LLM backends uncovers qualitatively opposite failure profiles, one conservative and the other permissive, linked to the backbone's precision-recall characteristics.

cs.AI↗
arXiv2026

DA-MergeLoRA: Hypernetwork-Based LoRA Merging for Few-Shot Test-Time Domain Adaptation

Few-shot Test-Time Domain Adaptation (FSTT-DA) seeks to adapt models to novel domains using only a handful of unlabeled target samples. This setting is more realistic than typical domain adaptation setups, which assume access to target data during source training. However, prior FSTT-DA approaches fail to effectively leverage source domain-specific knowledge, relying on shallow batch normalization updates, prompt-based methods that treat the model as a black box, or ensembling strategies that do not capture cross-domain relationships. To address these limitations, we introduce a new FSTT-DA framework that integrates LoRA fine-tuning with model merging. In our approach, separate LoRA modules are fine-tuned on CLIP's vision encoder for each source domain. Since LoRA modifies only a small fraction of the model's parameters, it retains the base model's generalized knowledge while internally learning domain-specific features. To adapt the learned knowledge to a specific target domain, we propose a hypernetwork trained via meta-learning that generates per-column merging factors to combine LoRA modules. Given a small batch of target images, the hypernetwork produces merging weights that fuse source LoRA modules into a single adapted representation. Our results demonstrate state-of-the-art performance across various domain adaptation datasets. Our code is publicly available at https://github.com/nahbois4321/DA-MergeLoRA.

cs.CV↗
arXiv2026

Phase-shifted multicomponent spin-charge nematicity in an altermagnet

Altermagnets host spin-split Fermi surfaces without net magnetization. This intrinsically multicomponent electronic setting raises the possibility that familiar correlated electron phases acquire unconventional spin-charge structure. Here we report the discovery of altermagnetic nematicity in Co0.25NbSe2. Using spectroscopic-imaging scanning tunneling microscopy and spin-polarized scanning tunneling microscopy, we find that the three nominally C3-related directions lose rotational equivalence in the zero-field state, in both charge and spin-sensitive tunneling channels. Strikingly, the dominant spin-sensitive component is shifted by one C3 sector relative to the dominant charge component, revealing a phase-shifted spin-charge nematic response. A phenomenological theory shows that altermagnetic order favors a finite relative phase between the charge and spin-sensitive nematic components -- C3 lattice pinning frustrates this preferred offset and selects the observed phase locking. These results establish altermagnetic nematicity as a new form of multicomponent electronic liquid-crystal order and point to a potentially generic route by which altermagnets can transform conventional correlated phases into symmetry-engineered spin-charge orders.

cond-mat.str-el↗
arXiv2026

Spontaneous emergence of altermagnetism in the single-orbital extended Hubbard model

Altermagnetism (AM), the recently discovered third class of collinear magnetic order, is characterized by non-relativistic momentum-dependent spin-split electronic structure with compensated zero net magnetization. It can arise from the conventional antiferromagnetism by introducing local anisotropy on the two opposite-spin sublattices, either through structural changes in local crystallographic symmetry or spontaneous emergence of local staggered orbital order from electron correlations in multi-orbital systems. Here, we demonstrate on the two-dimensional square lattice that a $d$-wave AM can emerge spontaneously in the single-orbital extended Hubbard model, without invoking crystallographic anisotropy and multi-orbital physics. We carry out mean-field studies on the concrete single-orbital $t$-$U$-$V$ model with $U$ and $V$ the onsite and nearest-neighbor Coulomb interactions, obtaining the mean-field ground states, analyzing their properties, and determining the phase diagram in the $U$-$V$ plane. The $d$-wave AM with novel spin-transport behavior is found to be stabilized in a wide region of the phase diagram when the system is doped away from half-filling, actualized by the coexistence of onsite antiferromagnetic order and complex $d$-wave nearest-neighbor spin bond orders. Our findings provide an alternative route to achieve AM and substantially expand the range of candidate AM materials.

cond-mat.str-el↗
arXiv2026

NEURON-Fabric: Architecture-Runtime Co-Design for Controlled Low-Bit Gradient Communication

Large-scale neural-network training repeatedly aggregates gradients across devices, making communication a central cost in distributed learning. Low-bit gradient aggregation can reduce this cost, but applying it as a static replacement for full-precision communication can destabilize training because safe precision depends on training phase, model structure, runtime bucketization, and the communication substrate. This paper presents NEURON-Fabric, a profile-guided runtime system for controlled low-bit gradient communication. NEURON-Fabric uses calibrated operating profiles, model-aware runtime bindings, online training-health monitoring, and reducer-capacity checks to decide when low-bit aggregation should be admitted, when execution should fall back to FP32, and which model regions are eligible for each route. The runtime preserves model semantics inside mixed DDP buckets and treats reducer admission as an architecture-runtime co-design problem rather than as a standalone compression operator. Across vision, Transformer, and autoregressive language-model workloads, NEURON-Fabric validates the path from calibration to distributed communication-hook execution. Static low-bit communication can collapse training accuracy, while profile-guided control preserves accuracy near full-precision references or calibrated targets and reduces modeled gradient-communication traffic in the evaluated settings. Transformer and billion-parameter language-model checks show that the same routing and fallback mechanisms execute across model families and multi-node deployments. Reducer-side replay and reducer-path measurements identify when compact sign-count aggregation is expected to reduce communication cost and when endpoint capacity should trigger fallback.

cs.DC↗
arXiv2026

Beyond the GUI Paradigm: Do Mobile Agents Need the Phone Screen?

Recent advances in mobile agents are dominated by the GUI paradigm, in which agents perceive UI information and emit screen interactions. However, mobile platforms also expose a command-line interface (CLI) that provides direct access to device services and data. We argue CLI deserves first-class consideration alongside GUI. We evaluate three coding agents (Claude Code, Terminus-2, mini-swe-agent) across four model APIs on AndroidWorld and MobileWorld without any mobile-specific post-training, comparing against three reproducible GUI baselines (GUI-Owl-1.5-32B, MAI-UI, Qwen3-VL-32B). Claude Code (Opus 4.7) reaches 71.8\% and 51.9\%, outperforming every reproducible GUI baseline (69.3/68.1/57.8\% on AndroidWorld; 43.2/26.3/13.3\% on MobileWorld), while every other CLI configuration remains competitive. To establish the paradigm's ceiling, we provide oracle CLI solutions that reach 88.8\% on AndroidWorld (103/116 tasks CLI-solvable) and 86.3\% on MobileWorld (101/117 tasks CLI-solvable), indicating substantial room for future improvement. To cover everyday user intents beyond the GUI scope, we introduce the \textbf{CLI-Advantage Task Suite}, comprising 45 templates across five categories: bulk operations, multi-condition filtering, aggregation, cross-app workflows, and hidden device state. Every CLI agent outperforms every GUI baseline in all five categories, with substantially fewer steps per task (10.7 vs.\ 18.6). To support future research on mobile CLI agents, we will open-source agent implementations, oracle solutions, the CLI-Advantage suite, and evaluation infrastructure.

cs.SE↗
arXiv2026

NEURON-Fabric: CXL-Side Low-Bit Gradient Aggregation for Distributed Training

In large-model distributed training, especially large language model workloads, gradient All-Reduce increasingly stresses the memory and communication path. This paper asks whether a Compute Express Link (CXL) memory controller can aggregate low-bit gradient signals as gradient cache lines pass through it, while preserving a 32-bit floating-point (FP32) path for workloads, layers, or phases that should not use low-bit approximation. We present NEURON-Fabric, a CXL-side controller architecture that performs packed gradient-binary (G-Binary) sign-count aggregation and gradient-ternary (G-Ternary) gated aggregation near CXL memory, with a control interface for selecting low-bit or FP32 paths. Cycle-level timing experiments show that the measured five-cycle low-bit aggregation datapath adds at most 1.67 percent exposed runtime overhead in the full last-level-cache miss regime; under bandwidth pressure, the same compute stage is hidden by CXL service time. Functional tests confirm byte-exact identity read-back, G-Binary sign-count aggregation, and G-Ternary gating. Training checks quantify the communication and accuracy tradeoff: low-bit aggregation remains close to FP32 on CIFAR-10/ResNet-18 and SST-2/DistilBERT, while full-path low-bit aggregation fails on CIFAR-100/ResNet-18. Layer-aware admission identifies the classifier head as sensitive; keeping the head on FP32 while applying low-bit aggregation to the backbone recovers most accuracy and reduces gradient traffic to 3.6-5.4 percent of the FP32 baseline. Hardware synthesis and FPGA place-and-route estimates suggest that the 512-bit aggregation datapath is small enough to be treated as a near-memory datapath extension rather than a separate accelerator-scale block.

cs.DC↗