arXiv subjects

Ting Chen

Publications and source records attributed to Ting Chen.

At least 19 recordsLinked to original sources

arXiv2026

Experimental Realization of a Nonreciprocal Quantum Battery

Quantum batteries promise faster and more efficient energy storage and extraction by harnessing quantum mechanics, serving as essential building blocks for future quantum devices. A key practical requirement-unidirectional energy flow during charging-is straightforward in classical batteries via potential difference, yet remains a major challenge in the quantum regime due to the inherent reciprocity of coherent coupling. Here, we realize the first backflow-free, nonreciprocal quantum battery in the few-quantum regime, implemented in a highly controlled trapped-ion system via a qubit-reservoir engineering technique. By subtly tailoring quantum interference between coherent and dissipative couplings of two laser-cooled motional modes, we achieve strictly unidirectional energy flow from the charger to the battery. We observe maximum charging power at an exceptional point and demonstrate a complete charge-discharge cycle with unitary work extraction. This work establishes the foundation for directional energy transfer and storage in micro- and nanoscale quantum devices, with broad implications for nonreciprocal energy harvesting, precision metrology, information processing, and quantum thermodynamics.

quant-ph↗

arXiv2026

Multi-Material Topology Optimization of Phononic Crystals for Targeted Elastic Wave Bandgap

Phononic crystals are periodic elastic structures that exhibit frequency bandgaps within which elastic waves cannot propagate. The formation and width of these bandgaps depend on the spatial distribution of material properties within periodic phononic unit cells. In this work, we propose novel topology optimization formulations to achieve multi-material design of phononic crystals for bandgap optimization at targeted frequencies, where we discover the influence of spatially varying properties of base materials on the band structures that block and permit elastic wave propagation. Specifically, we model wave propagation by Floquet-Bloch theory and solve the resulting eigenvalue problem by the finite element method along the boundary of the irreducible Brillouin zone. We first propose a multivariate weight-based method via a novel interpolation scheme that maps the design variables of an arbitrary number of candidate materials into effective density and stiffness for elastic wave-propagation calculations, where bandgaps arise from the contrast in the stiffness and inertia among material constituents. We then develop two formulations for bandgap optimization. The first formulation maximizes the gap between two adjacent frequency bands using a bound-based approach, where we introduce two auxiliary scalar variables as additional design variables and incorporate them directly into the design objective, yielding a linear, differentiable design objective, with the eigenfrequencies entering the formulation through associated constraints. The second formulation places a bandgap over a user-defined target frequency range through a dynamic band-pairing strategy, where we adaptively identify the relevant pair of adjacent bands amid optimization iterations. Towards the end, we validate the bandgap performance of our designs against the transmission loss spectra computed from COMSOL Multiphysics.

cs.CE↗

arXiv2026

Web3 x AI Agents: Landscape, Integrations, and Foundational Challenges

The convergence of Web3 technologies and AI agents is a rapidly evolving frontier that is reshaping decentralized ecosystems. This paper presents the first comprehensive landscape analysis of the intersection between Web3 and AI agents, examining five critical dimensions: landscape, economics, governance, security, and trust mechanisms. Through an analysis of 979 capitalized Web3-AI agent tokens at a 1 September 2026 cutoff, compared with a cohort of 275 tokens collected at 6 April 2025, we develop a taxonomy and systematically map the current market landscape (RQ1), identifying distinct patterns in project distribution and capitalization. We complement the token frame with on-chain measurement of the agent standards layer: 530,266 ERC-8004 identity registrations across 16 chains and 432,913 x402 settlements on Base. We then investigate four integration domains: AI agents in decentralized finance (RQ2), governance (RQ3), security (RQ4), and Web3 trust mechanisms for agent reliability (RQ5). Infrastructure dominates the landscape in both project count and capitalization; value is highly concentrated (Gini 0.938), and the archived cohort lost 36.4% in capitalization over seventeen months despite token population growth. The agent standards layer shows adoption at scale, but economic throughput remains limited. Across integration domains, AI agents extend DeFi trading and make continuous security auditing affordable, while agents in governance remain largely a blueprint. These findings outline critical considerations for future research toward robust, intelligent, and trustworthy decentralized systems with AI agent interactions.

cs.CY↗

arXiv2026

GeoFWI3D: Large-scale 3D Velocity Model Dataset for Deep Learning-assisted Seismic Imaging

We introduce GeoFWI3D, a large-scale open-source benchmark dataset of geologically plausible 3D subsurface models designed to accelerate deep learning (DL) assisted seismic imaging and full waveform inversion (FWI). FWI is a physics-driven, wave-equation-based optimization technique used in seismic imaging to estimate the subsurface properties. However, traditional FWI is highly non-linear, non-unique, and ill-posed. The iterative process often converges to local minima when the starting model is insufficiently accurate. Most importantly, 3D FWI is prohibitively expensive. With the advent of DL, a direct mapping between the shot gathers and subsurface properties can be established by training a network on realistic models. The primary bottleneck for such approaches is the lack of large-scale, realistic training datasets. GeoFWI3D addresses this gap with 10,000 geologically diverse velocity models at 96x96x96 resolution, spanning four structural complexity classes: pure stratigraphy, faulted networks, salt diapirism, and complex coupled fault-salt systems. Each model is accompanied by co-registered multi-modal labels including compressional velocity (Vp), zero-offset seismic reflectivity, relative geologic time (RGT), and semantic fault/salt masks. To facilitate systematic evaluation, we introduce benchmark tasks covering fault detection, joint salt body segmentation and chronostratigraphy prediction, FWI, wavefield and traveltime surrogates using neural operators, and generative modeling with a 3D diffusion model. We present baseline results for each task to establish reference performance metrics for future users. The dataset is publicly available under Creative Commons Attribution 4.0 International.

physics.geo-ph↗

arXiv2026

Algorithmic Implementation of Multi-Window STFT Phase Retrieval

STFT phase retrieval aims to reconstruct a signal from spectrogram magnitudes. While two compactly supported windows theoretically guarantee uniqueness, a complete algorithmic framework remains lacking. This paper systematically constructs numerical schemes for multi-window STFT phase retrieval. We theoretically provide a nonuniqueness counterexample for the single-window case. We then propose a two-window reconstruction algorithm based on Shannon interpolation and analytic square-root extraction. To overcome the instability of the global sign choice, we introduce a third window and a robust $K$-sign method that determines the sign locally. We also adapt SCR algorithm to the STFT setting. For the $K$-sign method, we establish deterministic stability on a finite contiguous block of frequency samples, with the contribution of the complementary tail explicitly included in the error bound. Numerical experiments validate the effectiveness of all schemes.

math.CA↗

arXiv2026

Well-Posedness for KdV-Type Equations with Second-Order Derivative Nonlinearities

We study a class of complex-valued KdV-type equations with cubic second-order derivative nonlinearities and arbitrary complex coefficients. For sufficiently small initial data in $L^2$, we prove local well-posedness in $H^s(\mathbb R)$ for $s\ge3/4$. The key ingredient is a family of dyadic resolution spaces $Z_k=X_k+Y_k$, designed to overcome the logarithmic divergence arising from high$*$low$*$low interactions in which both derivatives fall on the highest-frequency factor. For the completely integrable third-order Kaup--Newell flow, we establish global-in-time $H^s(\mathbb R)$ bounds for $0\le s<1$ and global well-posedness in $H^s(\mathbb R)$ for every $s\ge3/4$, with no smallness assumption on the initial data.

math.AP↗

arXiv2026

Weighted Endpoint Variational and Jump Inequalites for Rough Singular Integrals

In this present paper, we study the weighted weak type $(1,\,1)$ bounds for rough maximal singular integral operators as well as the the corresponding variation and jump operators. Our first result is the weighted weak type $(1,\,1)$ bound for the maximal singular integral $$T_Ω^{*}f(x)=\sup\limits_{\varepsilon>0}|T_{\varepsilon,Ω}f(x)|=\sup\limits_{\varepsilon>0}\bigg|\int_{|x-y|>\varepsilon} \frac{Ω(x-y)}{|x-y|^{d}}f(y)dy\bigg|,$$ where $Ω\in L^\infty(\mathbb{S}^{d-1})$, homogeneous of degree zero, and satisfies the cancellation condition. We show that $$\|T_Ω^{*}\|_{L^1(w)\rightarrow L^{1,\infty}(w)}\lesssim[w]_{A_1}[w]_{A_\infty}\log([w]_{A_\infty}+1),$$ where $w$ belongs to the Muckenhoupt class $A_1(\mathbb{R}^d)$. This represents an essential improvement of a result (Honz\'ık, Inter.Math. Res. Not. 2020) and a result (Bhojak and Mohanty, J. Funct. Anal.2023). Our second one is the weighted weak type $(1,\,1)$ bounds for variation and jump operators corresponding to $\{T_{\varepsilon,Ω}\}_{\varepsilon\in2^{\mathbb{Z}}}$ and $\{T_{\varepsilon, Ω}^ϕ\}_{\varepsilon\in\mathbb{R}^{+}}$ under the condition $Ω\in L^\infty(\mathbb{S}^{d-1})$, where $T_{ε,Ω}^ϕ$ represents a smooth truncation of rough singular integral operator.These results of this part are the first weighted weak type $(1,\,1)$ variation inequalities and jump inequalities for rough singular integrals.

math.CA↗

arXiv2026

Towards World Models in Biomedical Research

A central goal of biomedicine is to understand, predict and ultimately control the dynamic mechanisms by which biological systems respond to perturbations, disease progression and therapeutic intervention. Although foundation models and large language models have accelerated biomedical data interpretation, most current systems remain focused on static pattern recognition rather than prospective simulation of biological futures. Here we propose biomedical world models as a paradigm for AI-driven discovery. These models learn latent representations of molecular, cellular, tissue and clinical states, together with intervention-conditioned dynamics that allow future trajectories to be simulated before actions are taken. We discuss how biomedical world models could function as data engines, environment simulators and scientific planning substrates across applications including virtual cells, organoids, virtual patients and surgical simulation. We outline the data infrastructure, evaluation benchmarks, safety constraints and governance frameworks required. Biomedical world models may provide a foundation for simulation-guided, closed-loop and experimentally actionable biomedical discovery.

cs.AI↗

arXiv2026

Grain Boundary Engineering Effect on Vortex Matter in Superconducting Films

Grain boundaries (GBs) in polycrystalline superconducting films act as a double-edged sword: they can pin vortices or degrade superconductivity through Josephson-like weak-link coupling. Here, we demonstrate that sputtering pressure tunes GB coupling in NbTiN films and visualize its consequences for vortex matter. The 5 mTorr film exhibits dispersed grain orientations and a two-step resistive transition under field, signaling intergranular weak-link behavior. In contrast, the 7 mTorr film develops a (111) texture, a single-step transition, higher critical current density, a second magnetization peak, and a δl-type pinning response consistent with improved GB coupling. Cryogenic magnetic force microscopy reveals a spatially heterogeneous, cluster-like vortex configuration in the 5 mTorr film, whereas the 7 mTorr film hosts a more uniform distribution with enhanced local order. These results establish a connection between deposition-controlled GB connectivity, macroscopic weak-link transport, and microscopic vortex organization, providing a practical route to tailor vortex pinning in polycrystalline superconducting films.

cond-mat.supr-con↗

arXiv2026

A Multi-Dimensional Evaluation of Explainability in Media Bias Detection

Detecting media bias automatically is difficult because biased framing is often subtle, yet in domains such as news analysis, accurate predictions alone are insufficient without explanations that reflect the model's underlying reasoning. We present a multi-dimensional evaluation of explainability in encoder-based media bias detection using the Bias Annotations By Experts (BABE) dataset. Specifically, we study BERT and RoBERTa as classifiers (base and large variants) along three complementary axes: predictive performance, explanation plausibility (token-level alignment with expert rationales), and mechanistic faithfulness (whether compact sets of attention heads recover predictive signal under counterfactual rationale masking). To induce variation in plausibility, we additionally investigate attention-supervised finetuning, which incorporates expert rationale annotations as an auxiliary training signal. Attention supervision serves as an intervention on attribution plausibility, while the effectiveness of attribution methods varies substantially across architectures. Circuit analysis further reveals substantial variation in mechanistic recoverability across architectures, suggesting that model scale alone does not determine circuit compressibility. Taken together, our findings suggest that predictive performance, attribution plausibility, and mechanistic faithfulness characterize different aspects of model behavior and should be evaluated separately when studying explainability in media bias detection.

cs.CL↗

arXiv2026

Enhancing Flow Matching with A Unified Guidance Framework for Efficient and Robust Speech Synthesis

Flow Matching (FM) has emerged as a powerful paradigm for speech generation but remains constrained by high inference latency and timbre leakage. To address these bottlenecks, we propose a unified guidance framework that enhances generation efficiency and robustness through two complementary strategies. On the data front, we introduce Data-guidance via heterogeneous augmentation, encouraging the model to disentangle linguistic content from acoustic residue. In parallel, we propose an enhanced Model-guidance mechanism that synergizes trajectory rectification with a novel intrinsic guidance objective. This approach distills conditional knowledge into network weights and straightens inference trajectory path, thereby eliminating Classifier-Free Guidance (CFG) overhead. Experiments demonstrate that our framework accelerates inference by nearly three times while effectively improving speaker similarity compared to state-of-the-art baselines.

cs.SD↗

arXiv2026

A Survey of LLM-Driven Penetration Testing: Taxonomy, Co-Evolution, and Open Challenges

Agents4Pentest, an emerging class of LLM-based autonomous penetration testing systems, has become a rapidly growing area in security research. Despite this growth, the field still lacks a unified taxonomy, a systematic understanding of how agent architectures and evaluation benchmarks have co-evolved, and a clear characterization of remaining capability and reliability gaps. This survey addresses these gaps through a systematic analysis of 81 papers between 2023 and 2026. We organize the literature into six categories: evaluation benchmarks, general-purpose systems, domain-specific frameworks, CTF-based systems, defense-oriented research, and surveys. We further trace a four-phase architectural evolution from text-only reasoning agents to agents trained with Reinforcement Learning with Verifiable Rewards (RLVR), showing that each transition is driven by a distinct capability bottleneck. Our analysis yields several key findings. First, RLVR marks a shift in capability acquisition from imitation of expert demonstrations to reward-driven self-improvement, enabling agents to discover previously undocumented attack strategies. Second, CTF platforms have evolved from evaluation testbeds into dual-purpose infrastructure for both agent evaluation and RL training. Third, domain-specific frameworks improve efficiency through recurring specialization mechanisms, but their gains remain largely confined to narrow task classes and are difficult to compare across domains because existing evaluations rely on different benchmarks. Fourth, the field is expanding beyond offensive automation toward adversarial defense and security compliance. Across these categories, we identify three structurally linked open challenges: evaluation reliability, limited performance on multi-stage attack scenarios, and scarcity of high-quality training data.

cs.SE↗

arXiv2026

ECGFlowCMR: Pretraining with ECG-Generated Cine CMR Helps Cardiac Disease Classification and Phenotype Prediction

Cardiac Magnetic Resonance (CMR) imaging provides a comprehensive assessment of cardiac structure and function but remains constrained by high acquisition costs and reliance on expert annotations, limiting the availability of large-scale labeled datasets. In contrast, electrocardiograms (ECGs) are inexpensive, widely accessible, and offer a promising modality for conditioning the generative synthesis of cine CMR. To this end, we propose ECGFlowCMR, a novel ECG-to-CMR generative framework that integrates a Phase-Aware Masked Autoencoder (PA-MAE) and an Anatomy-Motion Disentangled Flow (AMDF) to address two fundamental challenges: (1) the cross-modal temporal mismatch between multi-beat ECG recordings and single-cycle CMR sequences, and (2) the anatomical observability gap due to the limited structural information inherent in ECGs. Extensive experiments on the UK Biobank and a proprietary clinical dataset demonstrate that ECGFlowCMR can generate realistic cine CMR sequences from ECG inputs, enabling scalable pretraining and improving performance on downstream cardiac disease classification and phenotype prediction tasks.

eess.IV↗

arXiv2026

When Large Language Models Meet UAV Projects: An Empirical Study from Developers' Perspective

In recent years, unmanned aerial vehicles (UAVs) have become increasingly popular in our daily lives and have attracted significant research interest in software engineering. At the same time, large language models (LLMs) have made notable advancements in language understanding, reasoning, and generation, making LLM applications in UAVs a promising research direction. However, existing studies have largely remained in preliminary exploration with a limited understanding of real-world practice, which causes an academia-industry gap and hinders the application of LLMs in UAVs. To address this, we conducted the first empirical study to investigate how LLMs support UAVs. To characterize common tasks and application scenarios of real-world UAV-LLM practices, we conducted a large-scale empirical study involving 997 research papers and 1,509 GitHub projects. The results classified nine common tasks (e.g., Natural Language Command Parsing) in four UAV workflows (e.g., Information Input) undertaken by LLMs in real-world UAV projects and revealed a large difference in the task distribution of research efforts and industry practices. To gain deeper insight into these differences and understand developers' perspectives on the application of LLMs in UAVs, we conducted a survey of practitioners, receiving 52 valid responses from 15 countries. The results revealed that while 40.4% of developers have attempted to apply LLMs to UAV tasks, 59.6% still face challenges integrating their UAV projects with advanced LLM capabilities. Their feedback attributes these challenges to five factors, including technological maturity, performance, safety, cost, and others, and provides practical implications for researchers and developers in conducting UAV-LLM practices.

cs.SE↗

arXiv2026

YARD: Y-Architecture Register Decoding for Efficient Hallucination Mitigation in Large Vision-Language Models

Contrastive decoding (CD) seeks to mitigate hallucinations in Large Vision-Language Models (LVLMs) by contrasting the output distributions of a standard model and a visually degraded model. However, existing training-free CD methods suffer from sub-optimal degraded branches: completely dropping visual tokens is too extreme and induces language hallucinations, while corrupting input images offers coarse control over visual evidence and suffers from high inference latency due to requiring two full forward passes. To address these dilemmas, we propose YARD, a training-free Y-Architecture Register Decoding framework. Motivated by the observation that reliable text-to-vision grounding predominantly emerges in the middle decoder layers, YARD constructs the degraded branch internally by sharing shallow-layer computations and branching exactly at this critical stage. For the degraded branch, YARD replaces patch-level visual tokens with register tokens, which preserve global image semantics but lack fine-grained local evidence. This image-aware yet locally under-grounded design provides a faithful contrastive signal without extreme modality mismatch, while the Y-architecture strictly avoids a costly second forward pass. Extensive experiments on generative and discriminative hallucination benchmarks demonstrate that YARD consistently achieves state-of-the-art hallucination mitigation across multiple LVLMs, alongside a significant reduction in inference latency.

cs.CV↗

arXiv2026

OccamToken: Efficient VLM Inference with Training-Free and Budget-Adaptive Token Pruning

Vision-language models (VLMs) rely on long visual token sequences for visual understanding, making the prefill stage expensive in both computation and memory. Most existing pruning methods follow an absolute-ranking paradigm, assigning importance scores to visual tokens and retaining a fixed top-K subset. In this work, we argue that this paradigm is fundamentally brittle: attention sinks distort token importance rankings, while image redundancy and query-dependent visual evidence make fixed token budgets unreliable across inputs. We propose OccamToken, a training-free framework that replaces absolute token ranking with register-anchored relative evidence testing. Instead of asking which tokens are globally important, OccamToken evaluates whether a visual token provides information beyond a register-based reference. Our key insight is that register tokens naturally absorb low-information attention patterns, making them a stable reference for identifying genuinely informative visual evidence. Based on this principle, OccamToken performs both image-adaptive redundancy pruning and query-adaptive relevance pruning through dynamic thresholds derived from register attention. Across LLaVA-NeXT, LLaVA-v1.5, and Qwen3-VL, OccamToken consistently improves the accuracy-efficiency trade-off without additional training. Notably, on LLaVA-NeXT, it reduces 2,880 visual tokens to approximately 40 while preserving over 93% of the original accuracy, enabling stable visual token compression even in the extreme 1.4% retention regime.

cs.CV↗

arXiv2026

Towards Autonomous Business Intelligence via Data-to-Insight Discovery Agent

Transforming fragmented enterprise data into actionable insights remains a significant challenge for LLMs, constrained by complex database schemas, limitations in dynamic SQL generation, and the need for deep multi-dimensional analysis.In this paper, we propose AIDA(Autonomous Insight Discovery Agent), the first end-to-end framework designed for autonomous exploration in complex business environments. We establish a highly flexible instant retail environment encompassing 200+ metrics and 100+ dimensions, and integrates a proprietary Domain-Specific Language (DSL) that bridges semantic reasoning with precise SQL execution. Our reinforcement learning system subsequently formulates business analysis as a Pareto Principle-guided cumulative reasoning process. Experimental results demonstrate that AIDA significantly outperforms workflow-based agents, and extensive evaluations further reveal that AIDA achieves superior environmental perception and more in-depth analysis from diverse perspectives. Our work ultimately establishes the transformative potential of autonomous intelligence for industrial-scale business intelligence systems.

cs.AI↗

arXiv2026

Observation of quantum multi-Mpemba effect in a trapped-ion system

The quantum Mpemba effect (ME) in Markovian systems is conventionally explained by a smaller overlap between the initial state and the slowest decay mode (SDM). Such state, initially farther away from equilibrium or steady state, relaxes faster than closer ones, resulting to a crossing of their trajectories. This picture, by neglecting the transient dynamics, holds in the long-time limit. Here we experimentally observe multiple trajectory crossings (multi-ME) in the relaxation dynamics of a trapped ion. Such novel dynamics takes place in a unusual scenario where the initial state instead has a larger overlap with the SDM. We develop a theoretical framework based on relaxation speed to understand the multi-ME. We show that the initial relaxation speed is governed by the fastest decay mode, which together with the SDM overlap gives a phase diagram that reveals both the occurrence and the types of quantum ME observed in our experiment. Our study goes beyond the simple picture based on the long-time limit, tracks continuously the quantum ME dynamics, and establishes a comprehensive framework to describe the transient quantum relaxation.

quant-ph↗