arXiv subjects

Tian Qiu

Publications and source records attributed to Tian Qiu.

At least 37 records · Page 2Linked to original sources

arXiv2026

SoK: Blockchain Agent-to-Agent Payments

Agentic AI rivals human capabilities across a wide range of domains. Looking ahead, it is foreseeable that AI agents will autonomously handle complex workflows and interactions. Early prototypes of this paradigm are emerging, e.g., OpenClaw and Moltbook, signaling a shift toward Agent-to-Agent (A2A) ecosystems. However, despite these promising blueprints, critical trust and security challenges remain, particularly in scenarios involving financial transactions. Ensuring secure and reliable payment mechanisms between unknown and untrusted agents is crucial to complete a fully functional and trustworthy A2A ecosystem. Although blockchain-based infrastructures provide a natural foundation for this setting, via programmable settlement, transparent accounting, and open interoperability, trust and security challenges have not yet been fully addressed. Hence, for the first time, we systematize blockchain-based A2A payments, e.g., X402, with a four-stage lifecycle: discovery, authorization, execution, and accounting. We categorize representative designs at each stage and identify key challenges, including weak intent binding, misuse under valid authorization, payment-service decoupling, and limited accountability. We highlight future directions for strengthening cross-stage consistency, enabling behavior-aware control, and supporting compositional payment workflows across agents and systems.

q-fin.GN↗

arXiv2026

The velocity field of our Milky Way outer stellar halo based on DESI DR2

Using 64,000 halo K giants from Dark Energy Spectroscopic Instrument (DESI) second Data Release (DR2), we decompose the Milky Way (MW) stellar halo between 3 and 160 kpc into metal-rich (MR) and metal-poor (MP) components via a Gaussian mixture model (GMM). The two populations are nearly equal in number but chemically and kinematically distinct: MR stars occupy highly radial orbits with velocity anisotropy of beta ~0.94 and metallicity dispersion sigma([Fe/H]) ~0.17 dex, without obvious dependence on distance, and are mainly contributed by Gaia-Sausage/Enceladus (GSE) debris. MR component dominates the inner 30 kpc and re-emerges beyond 50 kpc, implying GSE debris can extend to ~70-80 kpc. MP stars exhibit a weaker radial bias of beta ~0.46, decreasing to -0.5 beyond 80 kpc, and with a larger metallicity dispersion of sigma([Fe/H]) ~0.46 dex, showing signatures of multiple minor mergers. Both components exhibit net prograde rotation at ~10-30 kpc with a stronger azimuthal signal in the MP population. The non-equilibrium motions of the outer halo (>50 kpc) are quantified with a dipole-plus-contraction velocity field. We find that the outer halo is simultaneously contracting (~-19 km/s, distance-independent) and subject to reflex motions (increases from -19 to -44 km/s with radius), reflecting the perturbation from the Large Magellanic Cloud (LMC). We also confirm a linear dependence of mean polar velocity for the outer stellar halo on the dipole velocity field, a direct consequence of the LMC and MW interaction. Our results provide a quantitative distance-resolved description of the MW's last major accretion event and its ongoing response to the first infall of the LMC.

astro-ph.GA↗

arXiv2026

The Milky Way stellar halo is twisted and doubly broken: insights from DESI DR2 Milky Way Survey observation

Using K giants from the second data release (DR2) of the Dark Energy Spectroscopic Instrument (DESI) Milky Way (MW) Survey, we measure the shape, orientation, radial profile, and density anisotropies of the MW stellar halo over 8 kpc$<r_\mathrm{GC}<200$ kpc. We identify a triaxial stellar halo (axes ratio $10:8:7$), 43 degrees tilted from the disk, showing two break radii at $\sim16$ kpc and $\sim76$ kpc, likely associated with Gaia-Sausage/Enceladus (GSE) and Large Magellanic Cloud (LMC), respectively. The inner stellar halo ($<30$ kpc) is oblate and aligned with the disk, whereas the outer stellar halo becomes prolate and perpendicular to the disk, consistent with the Vast Polar Structure of MW satellites. The twisted halo may arise from the disk-halo angular momentum shift triggered by the infall of a massive satellite. The anisotropic density distribution of the stellar halo is also measured, with successful re-identification of the Hercules-Aquila Cloud South/North (HAC-N/-S) and Virgo overdensities (VOD). Break radii are found at 15/30 kpc for VOD/HAC-N(-S). We identify the LMC transient density wake with a break radius at 60 kpc in the Pisces overdensity region. We also find new observational evidence of the LMC collective density wake, by showing a break radius at $\sim$100 kpc in the northern Galactic cap with a clear density peak at 90 kpc. In the end, we found that more metal-poor halo stars are more radially extended. Our results provide important clues to the assembly and evolution of the MW stellar halo under the standard cosmic structure formation framework.

astro-ph.GA↗

arXiv2026

Mutual Coupling Aware Channel Estimation for RIS-Aided Multi-User mmWave Systems

This paper proposes a three-stage uplink channel estimation protocol for reconfigurable intelligent surface (RIS)-aided multi-user (MU) millimeter-wave (mmWave) multiple-input single-output (MISO) systems, where both the base station (BS) and the RIS are equipped with uniform planar arrays (UPAs). The proposed approach explicitly accounts for the mutual coupling (MC) effect, modeled via scattering parameter multiport network theory. In Stage~I, a dimension-reduced subspace-based method is proposed to estimate the common angle of arrival (AoA) at the BS using the received signals across all users. In Stage~II, MC-aware cascaded channel estimation is performed for a typical user. The equivalent measurement vectors for each cascaded path are extracted and the reference column is reconstructed using a compressed sensing (CS)-based approach. By leveraging the structure of the cascaded channel, the reference column is rearranged to estimate the AoA at the RIS, thereby reducing the computational complexity associated with estimating other columns. Additionally, the common angle of departure (AoD) at the RIS is also obtained in this stage, which significantly reduces the pilot overhead for estimating the cascaded channels of other users in Stage~III. The RIS phase shift training matrix is designed to optimize performance in the presence of MC and outperforms random phase scheme. Simulation results validate that the proposed method yields better performance than the MC-unaware and existing approaches in terms of estimation accuracy and pilot efficiency.

eess.SP↗

arXiv2026

The Binary Fraction of Stars in the Dwarf Galaxy Ursa Minor via Dark Energy Spectroscopic Instrument

We utilize multi-epoch line-of-sight velocity measurements from the Milky Way Survey of the Dark Energy Spectroscopic Instrument to estimate the binary fraction for member stars in the dwarf spheroidal galaxy Ursa Minor. Our dataset comprises 670 distinct member stars, with a total of more than 2,000 observations collected over approximately one year. We constrain the binary fraction for UMi to be $0.61^{+0.16}_{-0.20}$ and $0.69^{+0.19}_{-0.17}$, with the binary orbital parameter distributions based on solar neighborhood observation from Duquennoy & Mayor (1991) and Moe & Di Stefano (2017), respectively. Furthermore, by dividing our data into two subsamples at the median metallicity, we identify that the binary fraction for the metal-rich ([Fe/H]>-2.14) population is slightly higher than that of the metal-poor ([Fe/H]<-2.14) population. Based on the Moe & Di Stefano model, the best-constrained binary fractions for metal-rich and metal-poor populations in UMi are $0.86^{+0.14}_{-0.24}$ and $0.48^{+0.26}_{-0.19}$, respectively. After a thorough examination, we find that this offset cannot be attributed to sample selection effects. We also divide our data into two subsamples according to their projected radius to the center of UMi, and find that the more centrally concentrated population in a denser environment has a lower binary fraction of $0.33^{+0.30}_{-0.20}$, compared with $1.00^{+0.00}_{-0.32}$ for the subsample in more outskirts.

astro-ph.SR↗

arXiv2026

Robust Answers, Fragile Logic: Probing the Decoupling Hypothesis in LLM Reasoning

While Chain-of-Thought (CoT) prompting has become a cornerstone for complex reasoning in Large Language Models (LLMs), the faithfulness of the generated reasoning remains an open question. We investigate the Decoupling Hypothesis: that correct answers often mask fragile, post-hoc rationalizations that are not causally tied to the model's prediction. To systematically verify this, we introduce MATCHA, a novel Answer-Conditioned Probing framework. Unlike standard evaluations that focus on final output accuracy, MATCHA isolates the reasoning phase by conditioning generation on the model's predicted answer, allowing us to stress-test the stability of the rationale itself. Our experiments reveal a critical vulnerability: under imperceptible input perturbations, LLMs frequently maintain the correct answer while generating inconsistent or nonsensical reasoning - effectively being ``Right for the Wrong Reasons''. Using LLM judges to quantify this robustness gap, we find that multi-step and commonsense tasks are significantly more susceptible to this decoupling than logical tasks. Furthermore, we demonstrate that adversarial examples generated by MATCHA transfer non-trivially to black-box models. Our findings expose the illusion of CoT robustness and underscore the need for future architectures that enforce genuine answer-reasoning consistency rather than mere surface-level accuracy.

cs.AI↗

arXiv2026

Phase Space Electronic Structure Theory: From Diatomic Lambda-Doubling to Macroscopic Einstein-de Haas

$Λ$-doubling of diatomic molecules is a subtle microscopic phenomenon that has long attracted the attention of experimental groups, insofar as rotation of molecular $\textit{nuclei}$ induces small energetic changes in the (degenerate) $\textit{electronic}$ state. A direct description of such a phenomenon clearly requires going beyond the Born-Oppenheimer approximation. Here we show that a phase space theory previously developed to capture electronic momentum and model vibrational circular dichroism -- and which we have postulated should also describe the Einstein-de Haas effect, a macroscopic manifestation of angular momentum conservation -- is also able to recover the $Λ$-doubling energy splitting (or $Λ$-splitting) of the NO molecule nearly quantitatively. The key observation is that, by parameterizing the electronic Hamiltonian in terms of both nuclear position ($\mathbf{X}$) and nuclear momentum ($\mathbf{P}$), a phase space method yields potential energy surfaces that explicitly include the electron-rotation coupling and correctly conserve angular momentum (which we show is essential to capture $Λ-$doubling). The data presented in this manuscript offers another small glimpse into the rich physics that one can learn from investigating phase space potential energy surfaces $E_{PS}(\mathbf{X},\mathbf{P})$ as a function of both nuclear position and momentum, all at a computational cost comparable to standard Born-Oppenheimer electronic structure calculations.

physics.chem-ph↗

arXiv2025

Three-Dimensional Anatomical Data Generation Based on Artificial Neural Networks

Surgical planning and training based on machine learning requires a large amount of 3D anatomical models reconstructed from medical imaging, which is currently one of the major bottlenecks. Obtaining these data from real patients and during surgery is very demanding, if even possible, due to legal, ethical, and technical challenges. It is especially difficult for soft tissue organs with poor imaging contrast, such as the prostate. To overcome these challenges, we present a novel workflow for automated 3D anatomical data generation using data obtained from physical organ models. We additionally use a 3D Generative Adversarial Network (GAN) to obtain a manifold of 3D models useful for other downstream machine learning tasks that rely on 3D data. We demonstrate our workflow using an artificial prostate model made of biomimetic hydrogels with imaging contrast in multiple zones. This is used to physically simulate endoscopic surgery. For evaluation and 3D data generation, we place it into a customized ultrasound scanner that records the prostate before and after the procedure. A neural network is trained to segment the recorded ultrasound images, which outperforms conventional, non-learning-based computer vision techniques in terms of intersection over union (IoU). Based on the segmentations, a 3D mesh model is reconstructed, and performance feedback is provided.

cs.CV↗

arXiv2025

Efficient Optimization of a Permanent Magnet Array for a Stable 2D Trap

Untethered magnetic manipulation of biomedical millirobots has a high potential for minimally invasive surgical applications. However, it is still challenging to exert high actuation forces on the small robots over a large distance. Permanent magnets offer stronger magnetic torques and forces than electromagnetic coils, however, feedback control is more difficult. As proven by Earnshaw's theorem, it is not possible to achieve a stable magnetic trap in 3D by static permanent magnets. Here, we report a stable 2D magnetic force trap by an array of permanent magnets to control a millirobot. The trap is located in an open space with a tunable distance to the magnet array in the range of 20 - 120mm, which is relevant to human anatomical scales. The design is achieved by a novel GPU-accelerated optimization algorithm that uses mean squared error (MSE) and Adam optimizer to efficiently compute the optimal angles for any number of magnets in the array. The algorithm is verified using numerical simulation and physical experiments with an array of two magnets. A millirobot is successfully trapped and controlled to follow a complex trajectory. The algorithm demonstrates high scalability by optimizing the angles for 100 magnets in under three seconds. Moreover, the optimization workflow can be adapted to optimize a permanent magnet array to achieve the desired force vector fields.

cs.RO↗

arXiv2025

Theoretical and Empirical Study of Spatial Power Focusing Effect for Sparse Arrays at Terahertz Band

This work investigates the spatial power focusing effect for large-scale sparse arrays at terahertz (THz) band, combining theoretical analysis with experimental validation. Specifically, based on a Green's function channel model, we analyze the power distribution along the $z$-axis, deriving a closed-form expression to characterize the focusing effect. Furthermore, the factors influencing the focusing effect, including phase noise and positional deviations, are theoretically analyzed and numerically simulated. Finally, a 300 GHz measurement platform based on a vector network analyzer (VNA) is constructed for experimental validation. The measurement results demonstrate close consistence with theoretical simulation results, confirming the spatial power focusing effect for sparse arrays.

eess.SP↗

arXiv2025

Deep Learning-based Intrusion Detection Systems: A Survey

Intrusion Detection Systems (IDS) have long been a hot topic in the cybersecurity community. In recent years, with the introduction of deep learning (DL) techniques, IDS have made great progress due to their increasing generalizability. The rationale behind this is that by learning the underlying patterns of known system behaviors, IDS detection can be generalized to intrusions that exploit zero-day vulnerabilities. In this survey, we refer to this type of IDS as DL-based IDS (DL-IDS). From the perspective of DL, this survey systematically reviews all the stages of DL-IDS, including data collection, log storage, log parsing, graph summarization, attack detection, and attack investigation. To accommodate current researchers, a section describing the publicly available benchmark datasets is included. This survey further discusses current challenges and potential future research directions, aiming to help researchers understand the basic ideas and visions of DL-IDS research, as well as to motivate their research interests.

cs.CR↗

arXiv2025

Marcus Theory and The Condon Approximation Revisited II: The Horror of Triplet Energy Transfer

We investigate the applicability of the Condon approximation (i.e. the notion that the diabatic coupling is invariant to geometry) in the context of both electron transfer (ET) and triplet energy transfer (TET) and compare the two cases. Although it is well appreciated that diabatic couplings usually arise from the interactions of electronic wavefunction tails, we show that ET tails are very different from TET tails. Using a simple model problem, our analysis explains in detail why the rates of TET decays with twice the rate of ET, while also leading to the hypothesis that the smaller diabatic couplings found for TET (versus ET) should imply more sensitivity to non-Condon fluctuations. As an example, for the classic sets of molecules investigated by Closs, we show that the Condon approximation is indeed less applicable for TET than for ET.

physics.chem-ph↗

arXiv2025

InsightQL: Advancing Human-Assisted Fuzzing with a Unified Code Database and Parameterized Query Interface

Fuzzing is a highly effective automated testing method for uncovering software vulnerabilities. Despite advances in fuzzing techniques, such as coverage-guided greybox fuzzing, many fuzzers struggle with coverage plateaus caused by fuzz blockers, limiting their ability to find deeper vulnerabilities. Human expertise can address these challenges, but analyzing fuzzing results to guide this support remains labor-intensive. To tackle this, we introduce InsightQL, the first human-assisting framework for fuzz blocker analysis. Powered by a unified database and an intuitive parameterized query interface, InsightQL aids developers in systematically extracting insights and efficiently unblocking fuzz blockers. Our experiments on 14 popular real-world libraries from the FuzzBench benchmark demonstrate the effectiveness of InsightQL, leading to the unblocking of many fuzz blockers and considerable improvements in code coverage (up to 13.90%).

cs.SE↗

arXiv2025

Locally analytic vectors in the completed cohomology of unitary Shimura curves

We use the methods introduced by Lue Pan to study the locally analytic vectors in the completed cohomology of unitary Shimura curves. As an application, we prove a classicality result on two-dimensional regular $σ$-de Rham representations of $\text{Gal}(\bar L/L)$ appearing in the locally $σ$-analytic vectors of the completed cohomology, where $L$ is a finite extension of $\mathbb{Q}_p$ and $σ:L\hookrightarrow E$ is an embedding of $L$ into a sufficiently large finite extension $E$ of $\mathbb{Q}_p$. We also prove that if a two-dimensional representation of $\text{Gal}(\bar L/L)$ appears in the locally $σ$-algebraic vectors of the completed cohomology then it is $σ$-de Rham. Finally, we give a geometric realization of some locally $σ$-analytic representations of $\mathrm{GL}_2(L)$. This realization has some applications to the $p$-adic local Langlands program, including a locality theorem for Galois representations arising from classical automorphic forms, an admissibility result for coherent cohomology of Drinfeld curves, and some special cases of the Breuil's locally analytic Ext$^1$-conjecture for $\mathrm{GL}_2(L)$.

math.NT↗

arXiv2025

A Graph-Neural-Network-Entropy model of vital node identification on network attack and propagation

Vital nodes usually play a key role in complex networks. Uncovering these nodes is an important task in protecting the network, especially when the network suffers intentional attack. Many existing methods have not fully integrated the node feature, interaction and state. In this article, we propose a novel method (GNNE) based on graph neural networks and information entropy. The method employs a Graph Convolutional Network (GCN) to learn the nodes' features, which are input into a Graph Attention Network (GAT) to obtain the influence factor of nodes, and the node influence factors are used to calculate the nodes' entropy to evaluate the node importance. The GNNE takes advantage of the GCN and GAT, with the GCN well extracting the nodes' features and the GAT aggregating the features of the nodes' neighbors by using the attention mechanism to assign different weights to the neighbors with different importance, and the nodes' entropy quantifies the nodes' state in the network. The proposed method is trained on a synthetic Barabasi-Albert network, and tested on six real datasets. Compared with eight traditional topology-based methods and four graph-machine-learning-based methods, the GNNE shows an advantage for the vital node identification in the perspectives of network attack and propagation.

cs.SI↗

arXiv2025

Towards scalable organ level 3D plant segmentation: Bridging the data algorithm computing gap

The precise characterization of plant morphology provides valuable insights into plant environment interactions and genetic evolution. A key technology for extracting this information is 3D segmentation, which delineates individual plant organs from complex point clouds. Despite significant progress in general 3D computer vision domains, the adoption of 3D segmentation for plant phenotyping remains limited by three major challenges: i) the scarcity of large-scale annotated datasets, ii) technical difficulties in adapting advanced deep neural networks to plant point clouds, and iii) the lack of standardized benchmarks and evaluation protocols tailored to plant science. This review systematically addresses these barriers by: i) providing an overview of existing 3D plant datasets in the context of general 3D segmentation domains, ii) systematically summarizing deep learning-based methods for point cloud semantic and instance segmentation, iii) introducing Plant Segmentation Studio (PSS), an open-source framework for reproducible benchmarking, and iv) conducting extensive quantitative experiments to evaluate representative networks and sim-to-real learning strategies. Our findings highlight the efficacy of sparse convolutional backbones and transformer-based instance segmentation, while also emphasizing the complementary role of modeling-based and augmentation-based synthetic data generation for sim-to-real learning in reducing annotation demands. In general, this study bridges the gap between algorithmic advances and practical deployment, providing immediate tools for researchers and a roadmap for developing data-efficient and generalizable deep learning solutions in 3D plant phenotyping. Data and code are available at https://github.com/perrydoremi/PlantSegStudio.

cs.CV↗

arXiv2025

Fast Methods For Multisite Charge Transfer Processes I: Constrained, State Averaged CASSCF(1,M) and CASSCF(2M-1,M) Simulations

We design a dynamically-weighted state-averaged constrained CASSCF to treat \ul{e}lectrons or \ul{h}oles moving between $n$ molecular fragments (where $n$ can be larger than 2). Within such a so-called eDSCn/hDSCn approach, we consider configurations that are mutually single excitations of each other, and we apply a generalized set of constraints to tailor the method for studying charge transfer problems. The constrained optimization problem is efficiently solved using a DIIS-SQP algorithm, thus maintaining computational efficiency. We demonstrate the method for a finite Su-Schrieffer-Heeger (SSH) chain, successfully reproducing the expected exponential decay of diabatic couplings with distance. When combined with a gradient, the current extension immediately enables efficient nonadiabatic dynamics simulations of complex multi-state charge transfer processes.

physics.comp-ph↗

arXiv2025

Fast Methods For Multisite Charge Transfer Processes II: Analytic Nuclear Gradients and Nonadiabatic Dynamics For cCASSCF(1,M) and cCASSCF(2M-1,M) Wavefunctions

We derive and implement analytic nuclear gradients and derivative couplings for a constrained Complete Active Space Self-Consistent Field with a small active space designed to model electron or hole transfer. Using a Lagrangian formalism, we are able to differentiate both the CASSCF energy and the constraint (which is required for globally smooth surfaces), and the resulting efficient algorithm can be immediately applied to nonadiabatic dynamics simulations of charge transfer processes. Here, we run initial surface-hopping simulations of a proton coupled electron transfer event for a phenoxyl-phenol system.

physics.comp-ph↗