arXiv subjects

Ziting Wang

Publications and source records attributed to Ziting Wang.

At least 19 recordsLinked to original sources

arXiv2026

FDABench: A Benchmark for Data Agents on Analytical Queries over Heterogeneous Data

The growing demand for data-driven decision-making has created an urgent need for data agents that can reason over heterogeneous data (databases, documents, web content, images, videos, and audio) to answer complex analytical queries. However, evaluating such agents remains challenging: existing benchmarks often focus on isolated agent capabilities or limited data modalities, lacking comprehensive coverage of heterogeneous data and rigorous evaluation across diverse data agent architectures. To address these challenges, we present FDABench, a benchmark for evaluating data agents' reasoning ability over heterogeneous data in analytical scenarios. Our contributions are threefold: (1) A comprehensive benchmark of 2,007 tasks spanning six data modalities with a unified, multi-granularity evaluation framework. (2) We design PUDDING, an agentic dataset construction framework that leverages LLM generation with iterative expert validation for reliable and scalable benchmark construction. (3) Extensive experiments across diverse data agent architectures, including general analytical agents, semantic operator frameworks, and RAG-based methods, revealing key insights and guidelines for future data agent development. Our data and source code are released at https://github.com/fdabench/FDAbench.

cs.DB↗

arXiv2026

Demonstrating TOFFEE: A Learned System for Synthesizing Data Agent Trajectories at Scale

LLM-powered data agents are playing an increasingly important role in data-driven decision making. However, existing data agents struggle to generalize to unseen data environments and analytical workflows, especially in heterogeneous enterprise settings. This creates a growing need for synthesizing high-quality data agent trajectories that capture complex analytical workflows for given data environments. Such trajectories support two key downstream uses: they can serve as supervised finetuning (SFT) data that adapts data agent models to the target domain, and as in-context learning (ICL) demonstrations to guide general-purpose LLMs in unfamiliar data environments. Thus, we introduce TOFFEE, a system for synthesizing high-quality data agent trajectories from given data environments via Monte Carlo Tree Search (MCTS) with adaptive model selection and cross-task prefix reuse. We show that TOFFEE can effectively generate scalable trajectory data for complex analytical tasks across heterogeneous environments. In this demonstration, we present the system framework of TOFFEE, including its task pool construction, trajectory explorer, and learned cost model. We also introduce the web interface of TOFFEE and its workflow, and demonstrate two end-to-end scenarios: trajectory synthesis for data agent finetuning, and demonstration-augmented data agent reasoning.

cs.AI↗

arXiv2026

Understanding Implicit Trust Errors in Core Carrier Networks through Multi-Agent Flaw Discovery and Analysis

Cellular core networks (CNs) are critical infrastructure, yet their internal security model has historically relied on physical isolation: interfaces between core components often operate within an assumed trust zone. As CNs transition to cloud-native deployments, this assumption weakens, expanding the attack surface and enabling external adversaries to reach previously internal interfaces. From a root-cause analysis of security flaws reported in GitHub issues for opensource CN implementations, we found a recurring pattern of blind trust among CN components. Components may omit syntactic validation, fail to enforce semantic invariants, or allocate resources without checking availability. Once internal interfaces become reachable, these weaknesses can lead to severe impacts such as denial of service and session hijacking. We call these vulnerabilities implicit trust errors (iTrue). To detect iTrues and understand their security impacts, we designed iFinder, an LLM-driven multi-agent system that summarizes known flaws, distills them into detection patterns, and applies them to discover new iTrues in CN implementations. To suppress hallucinations produced by large language models (LLMs), we built an innovative strategy that crosschecks both 3GPP specifications and CN code to capture existing protection missed by the agents. Further, we developed a technique that uses LLMs to generate proof-of-concept (PoC) exploits for potential iTrues and iteratively refine the PoCs by automatically executing them against CN implementations and analyzing results. Running iFinder on seven prominent open-source CN implementations, we discovered 84 previously unknown vulnerabilities. Among them, 83 have already been confirmed and 81 have been assigned CVEs. Importantly, a session-hijacking flaw has been confirmed on real-world commercial 5G core networks.

cs.CR↗

arXiv2026

Visual Generation in the New Era: An Evolution from Atomic Mapping to Agentic World Modeling

Recent visual generation models have made major progress in photorealism, typography, instruction following, and interactive editing, yet they still struggle with spatial reasoning, persistent state, long-horizon consistency, and causal understanding. We argue that the field should move beyond appearance synthesis toward intelligent visual generation: plausible visuals grounded in structure, dynamics, domain knowledge, and causal relations. To frame this shift, we introduce a five-level taxonomy: Atomic Generation, Conditional Generation, In-Context Generation, Agentic Generation, and World-Modeling Generation, progressing from passive renderers to interactive, agentic, world-aware generators. We analyze key technical drivers, including flow matching, unified understanding-and-generation models, improved visual representations, post-training, reward modeling, data curation, synthetic data distillation, and sampling acceleration. We further show that current evaluations often overestimate progress by emphasizing perceptual quality while missing structural, temporal, and causal failures. By combining benchmark review, in-the-wild stress tests, and expert-constrained case studies, this roadmap offers a capability-centered lens for understanding, evaluating, and advancing the next generation of intelligent visual generation systems.

cs.CV↗

arXiv2026

Data Agents Under Attack: Vulnerabilities in LLM-Driven Analytical Systems

Data agents integrate LLM-driven reasoning with relational data access, executable analytical tools, and multi-step workflow orchestration, making them increasingly central to enterprise analytics. This integration introduces new security vulnerabilities across data resources, database execution, and agent reasoning, recombining concerns from database security and general-purpose LLM-agent security into failure modes that neither line of work captures on its own. To address this gap, we present a systematic security study of data agents. Our contributions are threefold. First, we develop a layered vulnerability framework that identifies eight data agent-specific risks across interpretation, execution, and policy layers. Second, we introduce an attack taxonomy organized by adversary goal, tactic, and technique, covering three goals, seven tactics, and fourteen techniques, and pair it with an LLM-driven payload generation pipeline grounded in real database schemas. Third, we evaluate these attacks on six systems, including four open-source data agents and two production cloud analytics services. Our experiments reveal substantial security vulnerabilities across current systems and yield four key takeaways.

cs.CR↗

arXiv2026

Observation and Modulation of the Quantum Mpemba Effect on a Superconducting Quantum Processor

In non-equilibrium quantum systems, the quantum Mpemba effect (QME) emerges as a counterintuitive phenomenon: systems exhibiting greater initial symmetry breaking restore symmetry faster. It has been attracting broad interest in studying QME dynamics and potential applications in quantum information science. While theoretical exploration of QME has surged, experimental studies, specifically on its flexible modulation, remain limited. Here, we report the observation and modulation of QME using a superconducting processor featuring an all-to-all connected, tunable-coupling architecture that enables precise control from short- to long-range interactions. This platform allows independent manipulation of coupling regimes, on-site potentials, and initial states, enabling us to elucidate their roles in QME. To quantify symmetry restoration, we employ entanglement asymmetry (EA), derived from the reconstructed density matrix via quantum state tomography, as a sensitive probe. In strong short-range coupling regimes, EA crossovers during quenches from tilted Néel states confirm the presence of QME. In contrast, in intermediate coupling regimes, synchronized EA and entanglement entropy dynamics reveal the suppression of QME. Remarkably, QME reemerges with the introduction of on-site linear potentials or quenches from tilted ferromagnetic states, the latter proving robust against on-site disorder. Our study demonstrates flexible QME modulation on a superconducting platform with multiple controllable parameters, shedding light on quantum many-body non-equilibrium dynamics and opening avenues for quantum information applications.

quant-ph↗

arXiv2026

Exploring Hilbert-Space Fragmentation on a Superconducting Processor

Isolated interacting quantum systems generally thermalize, yet there are several examples for the breakdown of ergodicity, such as many-body localization and quantum scars. Recently, ergodicity breaking has been observed in systems subjected to linear potentials, termed Stark many-body localization. This phenomenon is closely associated with Hilbert-space fragmentation, characterized by a strong dependence of dynamics on initial conditions. Here, we explore initial-state dependent dynamics using a ladder-type superconducting processor with up to 24 qubits, which enables precise control of the qubit frequency and initial state preparation. In systems with linear potentials, we experimentally observe distinct non-equilibrium dynamics for initial states with the same quantum numbers and energy, but with varying domain wall numbers. Accompanied by the numerical simulation for systems with larger sizes, we reveal that this distinction becomes increasingly pronounced as the system size grows, in contrast with weakly disordered interacting systems. Our results provide convincing experimental evidence of the fragmentation in Stark systems, enriching our understanding of the weak breakdown of ergodicity.

quant-ph↗

arXiv2026

CARROT: A Learned Cost-Constrained Retrieval Optimization System for RAG

Large Language Models (LLMs) have demonstrated impressive ability in generation and reasoning tasks but struggle with handling up-to-date knowledge, leading to inaccuracies or hallucinations. Retrieval-Augmented Generation (RAG) mitigates this by retrieving and incorporating external knowledge into input prompts. In particular, due to LLMs' context window limitations and long-context hallucinations, only the most relevant "chunks" are retrieved. However, current RAG systems face three key challenges: (1) chunks are often retrieved independently without considering their relationships, such as redundancy and ordering; (2) the utility of chunks is non-monotonic, as adding more chunks can degrade quality; and (3) retrieval strategies fail to adapt to the unique characteristics of different queries. To overcome these challenges, we design a cost-constrained retrieval optimization framework for RAG. We adopt a Monte Carlo Tree Search (MCTS) based strategy to find the optimal chunk combination order, which considers the chunks' correlations. In addition, to address the non-monotonicity of chunk utility, instead of treating budget exhaustion as the termination condition, we design a utility computation strategy to identify the optimal chunk combination without necessarily exhausting the budget. Furthermore, we propose a configuration agent that predicts optimal configurations for each query domain, improving our framework's adaptability and efficiency. Experimental results demonstrate up to a 30% improvement over baseline models, highlighting the framework's effectiveness, scalability, and suitability. Our source code has been released at https://github.com/wang0702/CARROT.

cs.DB↗

arXiv2025

Microwave-activated high-fidelity three-qubit gate scheme for fixed-frequency superconducting qubits

Scalable superconducting quantum processors require balancing critical constraints in coherence, control complexity, and spectral crowding. Fixed-frequency architectures suppress flux noise and simplify control via all-microwave operations but remain limited by residual ZZ crosstalk. Here we propose a microwave-activated three-qubit gate protocol for fixed-frequency transmon qubits in the large-detuning regime ($|Δ| \gg g$), leveraging the third-order nonlinear interaction to coherently exchange $\ket{001} \leftrightarrow \ket{110}$ states. By incorporating a phase-compensated optimization protocol, numerical simulations demonstrate a high average gate fidelity exceeding $99.9\%$. Systematic error analysis identifies static long-range ZZ coupling as the dominant error source in multi-qubit systems, which can be suppressed via operations in the large-detuning regime ($\sim 1$ GHz). The protocol maintains process fidelities exceeding $98\%$ under decoherence, while demonstrating intrinsic robustness to fabrication-induced parameter variations and compatibility with existing all-microwave two-qubit gate architectures. This hardware-efficient strategy advances scalable quantum computing systems by improving coherence properties, reducing spectral congestion, and expanding the experimental toolkit for error-resilient quantum operations in the noisy intermediate-scale quantum era.

quant-ph↗

arXiv2025

DisorientLiDAR: Physical Attacks on LiDAR-based Localization

Deep learning models have been shown to be susceptible to adversarial attacks with visually imperceptible perturbations. Even this poses a serious security challenge for the localization of self-driving cars, there has been very little exploration of attack on it, as most of adversarial attacks have been applied to 3D perception. In this work, we propose a novel adversarial attack framework called DisorientLiDAR targeting LiDAR-based localization. By reverse-engineering localization models (e.g., feature extraction networks), adversaries can identify critical keypoints and strategically remove them, thereby disrupting LiDAR-based localization. Our proposal is first evaluated on three state-of-the-art point-cloud registration models (HRegNet, D3Feat, and GeoTransformer) using the KITTI dataset. Experimental results demonstrate that removing regions containing Top-K keypoints significantly degrades their registration accuracy. We further validate the attack's impact on the Autoware autonomous driving platform, where hiding merely a few critical regions induces noticeable localization drift. Finally, we extended our attacks to the physical world by hiding critical regions with near-infrared absorptive materials, thereby successfully replicate the attack effects observed in KITTI data. This step has been closer toward the realistic physical-world attack that demonstrate the veracity and generality of our proposal.

cs.CV↗

arXiv2025

Observation of Inelastic Meson Scattering in a Floquet System using a Digital Quantum Simulator

Lattice gauge theories provide a non-perturbative framework for understanding confinement and hadronic physics, but their real-time dynamics remain challenging for classical computations. However, quantum simulators offer a promising alternative for exploring such dynamics beyond classical capabilities. Here, we experimentally investigate meson scattering using a superconducting quantum processor. Employing a digital protocol, we realize a Floquet spin chain equivalent to a one-dimensional Floquet $\mathbb{Z}_2$ lattice gauge theory. We observe Bloch oscillations of single kinks and strong binding between adjacent kinks, signaling confinement and the formation of stable mesons in this Floquet system. Using full-system joint readout, we resolve meson populations by string length, enabling identification of meson scattering channels. Our results reveal the fragmentation of a long-string meson into multiple short-string mesons, which is also an experimental signature of string breaking. Moreover, we directly observe inelastic meson scattering, where two short-string mesons can merge into a longer one. Our results pave the way for studying interacting gauge particles and composite excitations on digital quantum simulators.

quant-ph↗

arXiv2025

Non-Equilibrium Criticality-Enhanced Quantum Sensing with Superconducting Qubits

Exploiting quantum features allows for estimating external parameters with precisions well beyond the capacity of classical sensors, a phenomenon known as quantum-enhanced precision. Quantum criticality has been identified as a resource for achieving such enhancements with respect to the probe size. However, they demand complex probe preparation and measurement and the achievable enhancement is ultimately restricted to narrow parameter regimes. On the other hand, non-equilibrium probes harness dynamics, enabling quantum-enhanced precision with respect to time over a wide range of parameters through simple probe initialization. Here, we unify these approaches through a Stark-Wannier localization platform, where competition between a linear gradient field and particle tunneling enables quantum-enhanced sensitivity across an extended parameter regime. The probe is implemented on a 9-qubit superconducting quantum device, in both single- and double-excitation subspaces, where we explore its performance in the extended phase, the critical point and the localized phase. Despite employing only computational-basis measurements we have been able to achieve near-Heisenberg-limited precision by combining outcomes at distinct evolution times. In addition, we demonstrate that the performance of the probe in the entire extended phase is significantly outperforming the performance in the localized regime. Our results highlight Stark-Wannier systems as versatile platforms for quantum sensing, where the combination of criticality and non-equilibrium dynamics enhances precision over a wide range of parameters without stringent measurement requirements.

quant-ph↗

arXiv2025

Microwave Engineering of Tunable Spin Interactions with Superconducting Qubits

Quantum simulation has emerged as a powerful framework for investigating complex many - body phenomena. A key requirement for emulating these dynamics is the realization of fully controllable quantum systems enabling various spin interactions. Yet, quantum simulators remain constrained in the types of attainable interactions. Here we demonstrate experimental realization of multiple microwave - engineered spin interactions in superconducting quantum circuits. By precisely controlling the native XY interaction and microwave drives, we achieve tunable spin Hamiltonians including: (i) XYZ spin models with continuously adjustable parameters, (ii) transverse - field Ising systems, and (iii) Dzyaloshinskii - Moriya interacting systems. Our work expands the toolbox for analogue - digital quantum simulation, enabling exploration of a wide range of exotic quantum spin models.

quant-ph↗

arXiv2025

Direct Implementation of High-Fidelity Three-Qubit Gates for Superconducting Processor with Tunable Couplers

Three-qubit gates can be constructed using combinations of single-qubit and two-qubit gates, making their independent realization unnecessary. However, direct implementation of three-qubit gates reduces the depth of quantum circuits, streamlines quantum programming, and facilitates efficient circuit optimization, thereby enhancing overall performance in quantum computation. In this work, we propose and experimentally demonstrate a high-fidelity scheme for implementing a three-qubit controlled-controlled-Z (CCZ) gate in a flip-chip superconducting quantum processor with tunable couplers. This direct CCZ gate is implemented by simultaneously leveraging two tunable couplers interspersed between three qubits to enable three-qubit interactions, achieving an average final state fidelity of $97.94\%$ and a process fidelity of $93.54\%$. This high fidelity cannot be achieved through a simple combination of single- and two-qubit gate sequences from processors with similar performance levels. Our experiments also verify that multilayer direct implementation of the CCZ gate exhibits lower leakage compared to decomposed gate approaches. As a showcase, we utilize the CCZ gate as an oracle to implement the Grover search algorithm on three qubits, demonstrating high performance with the target probability amplitude significantly enhanced after two iterations. These results highlight the advantage of our approach, and facilitate the implementation of complex quantum circuits.

quant-ph↗

arXiv2025

Many-body delocalization with a two-dimensional 70-qubit superconducting quantum simulator

Quantum many-body systems with sufficiently strong disorder can exhibit a non-equilibrium phenomenon, known as the many-body localization (MBL), which is distinct from conventional thermalization. While the MBL regime has been extensively studied in one dimension, its existence in higher dimensions remains elusive, challenged by the avalanche instability. Here, using a 70-qubit two-dimensional (2D) superconducting quantum simulator, we experimentally explore the robustness of the MBL regime in controlled finite-size 2D systems. We observe that the decay of imbalance becomes more pronounced with increasing system sizes, scaling up from 21, 42 to 70 qubits, with a relatively large disorder strength, and for the first time, provide an evidence for the many-body delocalization in 2D disordered systems. Our experimental results are consistent with the avalanche theory that predicts the instability of MBL regime beyond one spatial dimension. This work establishes a scalable platform for probing high-dimensional non-equilibrium phases of matter and their finite-size effects using superconducting quantum circuits.

quant-ph↗

arXiv2025

Prethermalization by Random Multipolar Driving on a 78-Qubit Superconducting Processor

Time-dependent drives hold the promise of realizing non-equilibrium many-body phenomena that are absent in undriven systems. Yet, drive-induced heating normally destabilizes the systems, which can be parametrically suppressed in the high-frequency regime by using periodic (Floquet) drives. It remains largely unknown to what extent highly controllable quantum simulators can suppress heating in non-periodically driven systems. Using the 78-qubit superconducting quantum processor, Chuang-tzu 2.0, we report the experimental observation of long-lived prethermal phases in many-body systems with tunable heating rates, driven by structured random protocols, characterized by $n$-multipolar temporal correlations. By measuring both the particle imbalance and subsystem entanglement entropy, we monitor the entire heating process over 1,000 driving cycles and observe the existence of the prethermal plateau. The prethermal lifetime is `doubly tunable': one way by driving frequency, the other by multipolar order; it grows algebraically with the frequency with the universal scaling exponent $2n{+}1$. Using quantum state tomography on different subsystems, we demonstrate a non-uniform spatial entanglement distribution and observe a crossover from area-law to volume-law entanglement scaling. With 78 qubits and 137 couplers in a 2D configuration, the entire far-from-equilibrium heating dynamics are beyond the reach of simulation using tensor-network numerical techniques. Our work highlights superconducting quantum processors as a powerful platform for exploring universal scaling laws and non-equilibrium phases of matter in driven systems in regimes where classical simulation faces formidable challenges.

quant-ph↗

arXiv2023

Two Binary trees of Rational numbers -- the S-tree and the SC-tree

In this study, we explore a novel approach to demonstrate the countability of rational numbers and illustrate the relationship between the Calkin-Wilf tree and the Stern-Brocot tree in a more intuitive manner. By employing a growth pattern akin to that of the Calkin-Wilf tree, we construct the S-tree and establish a one-to-one correspondence between the vertices of the S-tree and the rational numbers in the interval $(0,1]$ using 0-1 sequences. To broaden the scope of this concept, we further develop the SC-tree, which is proven to encompass all positive rational numbers, with each rational number appearing only once. We also delve into the interplay among these four trees and offer some applications for the newly introduced tree structures.

math.HO↗

arXiv2023

Inertial randomized Kaczmarz algorithms for solving coherent linear systems

In this paper, by regarding the two-subspace Kaczmarz method [20] as an alternated inertial randomized Kaczmarz algorithm we present a new convergence rate estimate which is shown to be better than that in [20] under a mild condition. Furthermore, we accelerate the alternated inertial randomized Kaczmarz algorithm and introduce a multi-step inertial randomized Kaczmarz algorithm which is proved to have a faster convergence rate. Numerical experiments support the theory results and illustrate that the multi-inertial randomized Kaczmarz algorithm significantly outperform the two-subspace Kaczmarz method in solving coherent linear systems.

math.NA↗