Designs, linear codes, plateaued functions, and their interconnections
In this paper, we mainly investigate profound interconnections between combinatorial designs, linear codes, and Boolean functions.
cs.IT↗
arXiv subjects
Jiaxin Wang
Publications and source records attributed to Jiaxin Wang.
SoK: Security of Autonomous LLM Agents in Agentic Commerce
Autonomous large language model (LLM) agents such as OpenClaw are pushing agentic commerce from human-supervised assistance toward machine actors that can negotiate, purchase services, manage digital assets, and execute transactions across on-chain and off-chain environments. Protocols such as the Trustless Agents standard (ERC-8004), Agent Payments Protocol (AP2), OKX Agent Payments Protocol (APP), the HTTP 402-based payment protocol (x402), Agent Commerce Protocol (ACP), the Agentic Commerce standard (ERC-8183), and Machine Payments Protocol (MPP) enable this transition, but they also create an attack surface that existing security frameworks do not capture well. This Systematization of Knowledge (SoK) develops a unified security framework for autonomous LLM agents in commerce and finance. We organize threats along five dimensions: agent integrity, transaction authorization, inter-agent trust, market manipulation, and regulatory compliance. From a systematically curated public corpus of academic papers, protocol documents, industry reports, and incident evidence, we derive 12 cross-layer attack vectors and show how failures propagate from reasoning and tooling layers into custody, settlement, market harm, and compliance exposure. We then propose a layered defense architecture addressing authorization gaps left by current agent-payment protocols. Overall, our analysis shows that securing agentic commerce is inherently a cross-layer problem that requires coordinated controls across LLM safety, protocol design, identity, market structure, and regulation. We conclude with a research roadmap and a benchmark agenda for secure autonomous commerce.
cs.CR↗
Memorization, Emergence, and Explaining Reversal Failures: A Controlled Study of Relational Semantics in LLMs
Autoregressive LLMs perform well on relational tasks that require linking entities via relational words (e.g., father/son, friend), but it is unclear whether they learn the logical semantics of such relations (e.g., symmetry and inversion logic) and, if so, whether reversal-type failures arise from missing relational semantics or left-to-right order bias. We propose a controlled Knowledge Graph-based synthetic framework that generates text from symmetric/inverse triples, train GPT-style autoregressive models from scratch, and evaluate memorization, logical inference, and in-context generalization to unseen entities to address these questions. We find a sharp phase transition in which relational semantics emerge with sufficient logic-bearing supervision, even in shallow (2-3 layer) models, and that successful generalization aligns with stable intermediate-layer signals. Finally, order-matched forward/reverse tests and a diffusion baseline indicate that reversal failures are primarily driven by autoregressive order bias rather than deficient inversion semantics.
cs.CL↗
Localization and Reshaping of Non-Minimum-Phase Zeros in Multi-Converter Systems
Non-minimum-phase (NMP) zeros in multi-converter power systems impose bandwidth ceilings on feedback control, yet quantifying them at the system level has been impractical because commercial converters withhold their internal controller models. This paper develops a Jacobian-based framework that decouples the NMP zeros from individual converter dynamics, proves them to be strictly real, and expresses their values as the singular values of a matrix constructed solely from the grid admittance matrix and steady-state power injections. Because these zeros govern the peak magnitude of the complementary sensitivity function, an exponential lower bound on this peak is derived as a function of the dominant zero, establishing that as the zero approaches the origin the stability margin degrades unavoidably. To counteract this degradation, a zero reshaping strategy is proposed that ranks converter nodes by their real participation factors and identifies the optimal site for voltage droop deployment without iterative search, steering the dominant zero away from the origin and thereby suppressing the sensitivity peak.
eess.SY↗
FATHOMER survey: III. Preliminary HI galaxy identification results
We present the HI galaxy observation results of the FATHOMER (FAst neuTral HydrOgen intensity Mapping ExpeRiment), a pilot drift scan survey by the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The survey comprises 28 hours of observations over 7 nights in 2021, covering a $60\, °^2$ sky area in the frequency range 1.05-1.45 GHz. The HI galaxies are identified using both a matched-filtering algorithm and the SoFiA source-finding pipeline, which yield consistent detections. We derive the velocity width ($W_{50}$), flux density, and HI mass for detected galaxies. A total of 702 galaxies are identified with HI mass above $10^{6.2}\,{M_\odot}$, signal-to-noise ratio greater than 5, and redshift $z < 0.09$. Among these, 331 are previously known from the ALFALFA survey. Of the newly detected sources, 9 have spectroscopic confirmation from SDSS, 285 are matched to SDSS or DESI photometric data, and 77 lack optical counterparts--possible candidates for dark or faint galaxies. Comparison with ALFALFA shows that FAST enables detection of galaxies at higher redshifts and with lower HI fluxes, despite the radio frequency interference (RFI) and partial data masking. A preliminary HI mass function analysis reveals a higher characteristic mass and steeper low-mass slope than ALFALFA, indicating FAST's enhanced sensitivity to massive and distant HI systems. These results demonstrate FAST's strong potential for future deep HI surveys and highlight the importance of improved RFI mitigation and completeness correction.
astro-ph.GA↗
WARM-CAT: Warm-Started Test-Time Comprehensive Knowledge Accumulation for Compositional Zero-Shot Learning
Compositional Zero-Shot Learning (CZSL) aims to recognize novel attribute-object compositions based on the knowledge learned from seen ones. Existing methods suffer from performance degradation caused by the distribution shift of label space at test time, which stems from the inclusion of unseen compositions recombined from attributes and objects. To overcome the challenge, we propose a novel approach that accumulates comprehensive knowledge in both textual and visual modalities from unsupervised data to update multimodal prototypes at test time. Building on this, we further design an adaptive update weight to control the degree of prototype adjustment, enabling the model to flexibly adapt to distribution shift during testing. Moreover, a dynamic priority queue is introduced that stores high-confidence images to acquire visual prototypes from historical images for inference. Since the model tends to favor compositions already stored in the queue during testing, we warm-start the queue by initializing it with training images for visual prototypes of seen compositions and generating unseen visual prototypes using the mapping learned between seen and unseen textual prototypes. Considering the semantic consistency of multimodal knowledge, we align textual and visual prototypes by multimodal collaborative representation learning. To provide a more reliable evaluation for CZSL, we introduce a new benchmark dataset, C-Fashion, and refine the widely used but noisy MIT-States dataset. Extensive experiments indicate that our approach achieves state-of-the-art performance on four benchmark datasets under both closed-world and open-world settings. The source code and datasets are available at https://github.com/xud-yan/WARM-CAT .
cs.CV↗
Ternary Spiking Neural Networks Enhanced by Complemented Neurons and Membrane Potential Aggregation
Spiking Neural Networks (SNNs) are promising energy-efficient models and powerful framworks of modeling neuron dynamics. However, existing binary spiking neurons exhibit limited biological plausibilities and low information capacity. Recently developed ternary spiking neuron possesses higher consistency with biological principles (i.e. excitation-inhibition balance mechanism). Despite of this, the ternary spiking neuron suffers from defects including iterative information loss, temporal gradient vanishing and irregular distributions of membrane potentials. To address these issues, we propose Complemented Ternary Spiking Neuron (CTSN), a novel ternary spiking neuron model that incorporates an learnable complemental term to store information from historical inputs. CTSN effectively improves the deficiencies of ternary spiking neuron, while the embedded learnable factors enable CTSN to adaptively adjust neuron dynamics, providing strong neural heterogeneity. Furthermore, based on the temporal evolution features of ternary spiking neurons' membrane potential distributions, we propose the Temporal Membrane Potential Regularization (TMPR) training method. TMPR introduces time-varying regularization strategy utilizing membrane potentials, furhter enhancing the training process by creating extra backpropagation paths. We validate our methods through extensive experiments on various datasets, demonstrating remarkable performance advances.
cs.NE↗
ErrEval: Error-Aware Evaluation for Question Generation through Explicit Diagnostics
Automatic Question Generation (QG) often produces outputs with critical defects, such as factual hallucinations and answer mismatches. However, existing evaluation methods, including LLM-based evaluators, mainly adopt a black-box and holistic paradigm without explicit error modeling, leading to the neglect of such defects and overestimation of question quality. To address this issue, we propose ErrEval, a flexible and Error-aware Evaluation framework that enhances QG evaluation through explicit error diagnostics. Specifically, ErrEval reformulates evaluation as a two-stage process of error diagnosis followed by informed scoring. At the first stage, a lightweight plug-and-play Error Identifier detects and categorizes common errors across structural, linguistic, and content-related aspects. These diagnostic signals are then incorporated as explicit evidence to guide LLM evaluators toward more fine-grained and grounded judgments. Extensive experiments on three benchmarks demonstrate the effectiveness of ErrEval, showing that incorporating explicit diagnostics improves alignment with human judgments. Further analyses confirm that ErrEval effectively mitigates the overestimation of low-quality questions.
cs.AI↗
SoK: Web3 RegTech for Cryptocurrency VASP AML/CFT Compliance
The decentralized architecture of Web3 technologies creates fundamental challenges for Anti-Money Laundering and Counter-Financing of Terrorism compliance. Traditional regulatory technology solutions designed for centralized financial systems prove inadequate for blockchain's transparent yet pseudonymous networks. This systematization examines how blockchain-native RegTech solutions leverage distributed ledger properties to enable novel compliance capabilities. We develop three taxonomies organizing the Web3 RegTech domain: a regulatory paradigm evolution framework across ten dimensions, a compliance protocol taxonomy encompassing five verification layers, and a RegTech lifecycle framework spanning preventive, real-time, and investigative phases. Through analysis of 41 operational commercial platforms and 28 academic prototypes selected from systematic literature review (2015-2025), we demonstrate that Web3 RegTech enables transaction graph analysis, real-time risk assessment, cross-chain analytics, and privacy-preserving verification approaches that are difficult to achieve or less commonly deployed in traditional centralized systems. Our analysis reveals critical gaps between academic innovation and industry deployment, alongside persistent challenges in cross-chain tracking, DeFi interaction analysis, privacy protocol monitoring, and scalability. We synthesize architectural best practices and identify research directions addressing these gaps while respecting Web3's core principles of decentralization, transparency, and user sovereignty.
cs.CR↗
Data-driven Communication and Control Design for Distributed Frequency Regulation with Black-box Inverters
The increasing penetration of inverter-based resources into the power grid, with often only black-box models available, challenges long-standing frequency control methods. Most recent works take a decentralized approach without online device coordination via communication. This paper considers both dynamic behavior and communication within secondary frequency control on an intermediate timescale. We develop a distributed data-driven approach that utilizes peer-to-peer communication between inverters to avoid the need for a central control center. To enable a trade off between communication network requirements and control performance, we present a framework to guide communication topology design for secondary frequency regulation. Following design of the inter-agent information exchange scheme, we design a controller that is structured according to the communication topology with a closed-loop stability guarantee. Case studies on the IEEE 39-bus system validate the framework and illustrate the trade-off between communication requirements and control performance that is enabled by our approach.
eess.SY↗
Further results on bent partitions
Bent partitions of $V_{n}^{(p)}$ play an important role in constructing (vectorial) bent functions, partial difference sets, and association schemes, where $V_{n}^{(p)}$ denotes an $n$-dimensional vector space over the finite field $\mathbb{F}_{p}$, $n$ is an even positive integer, and $p$ is a prime. For bent partitions, there remains a challenging open problem: Whether the depth of any bent partition of $V_{n}^{(p)}$ is always a power of $p$. Notably, the depths of all current known bent partitions of $V_{n}^{(p)}$ are powers of $p$. In this paper, we prove that for a bent partition $Γ$ of $V_{n}^{(p)}$ for which all the $p$-ary bent functions generated by $Γ$ are regular or all are weakly regular but not regular, the depth of $Γ$ must be a power of $p$. We present new constructions of bent partitions that (do not) correspond to vectorial dual-bent functions. In particular, a new construction of vectorial dual-bent functions is provided. Additionally, for general bent partitions of $V_{n}^{(2)}$, we establish a characterization in terms of Hadamard matrices.
cs.IT↗
Decoding TRON: A Comprehensive Framework for Large-Scale Blockchain Data Extraction and Exploration
Cryptocurrencies and Web3 applications based on blockchain technology have flourished in the blockchain research field. Unlike Bitcoin and Ethereum, due to its unique architectural designs in consensus mechanisms, resource management, and throughput, TRON has developed a more distinctive ecosystem and application scenarios centered around stablecoins. Although it is popular in areas like stablecoin payments and settlement, research on analyzing on-chain data from the TRON blockchain is remarkably scarce. To fill this gap, this paper proposes a comprehensive data extraction and exploration framework for the TRON blockchain. An innovative high-performance ETL system aims to efficiently extract raw on-chain data from TRON, including blocks, transactions, smart contracts, and receipts, establishing a research dataset. An in-depth analysis of the extracted dataset reveals insights into TRON's block generation, transaction trends, the dominance of exchanges, the resource delegation market, smart contract usage patterns, and the central role of the USDT stablecoin. The prominence of gambling applications and potential illicit activities related to USDT is emphasized. The paper discusses opportunities for future research leveraging this dataset, including analysis of delegate services, gambling scenarios, stablecoin activities, and illicit transaction detection. These contributions enhance blockchain data management capabilities and understanding of the rapidly evolving TRON ecosystem.
cs.CR↗
Self-orthogonal codes from plateaued functions and their applications in quantum codes and LCD codes
Self-orthogonal codes have received great attention due to their important applications in quantum codes, LCD codes and lattices. Recently, several families of self-orthogonal codes containing the all-$1$ vector were constructed by augmentation technique. In this paper, utilizing plateaued functions, we construct some classes of linear codes which do not contain the all-$1$ vector. We also investigate their punctured codes. The weight distributions of the constructed codes are explicitly determined. Under certain conditions, these codes are proved to be self-orthogonal. Furthermore, some classes of optimal linear codes are obtained from their duals. Using the self-orthogonal punctured codes, we also construct several new families of at least almost optimal quantum codes and optimal LCD codes.
cs.IT↗
Phase II of the LAMOST-Kepler/K2 Survey. II. Time Domain of Medium-resolution Spectroscopic Observations from 2018 to 2023
The LAMOST-Kepler/K2 Medium-Resolution Spectroscopic Survey (LK-MRS) conducted time-domain medium-resolution spectroscopic observations of 20 LAMOST plates in the Kepler and K2 fields from 2018 to 2023, a phase designated as LK-MRS-I. A catalog of stellar parameters for a total of 36,588 stars, derived from the spectra collected during these five years, including the effective temperature, the surface gravity, the metallicity, the α-element abundance, the radial velocity, and v sin i of the target stars, is released, together with the weighted averages and uncertainties. At S/N = 10, the measurement uncertainties are 120 K, 0.18 dex, 0.13 dex, 0.08 dex, 1.9 km/s, and 4.0 km/s for the above parameters, respectively. Comparisons with the parameters provided by the APOGEE and GALAH surveys validate the effective temperature and surface gravity measurements, showing minor discrepancies in metallicity and α-element abundance values. We identified some peculiar star candidates, including 764 metal-poor stars, 174 very metal-poor stars, and 30 high-velocity stars. Moreover, we found 2,333 stars whose radial velocity seems to be variable. Using Kepler/K2 or TESS photometric data, we confirmed 371 periodic variable stars among the radial velocity variable candidates and classified their variability types. LK-MRS-I provides spectroscopic data being useful for studies of the Kepler and K2 fields. The LK-MRS project will continue collecting time-domain medium-resolution spectra for target stars during the third phase of LAMOST surveys, providing data to support further scientific research.
astro-ph.SR↗
Inactive longitude and superflare in the active single-lined pre-main sequence binary V2279 Cyg
Young, solar-like stars in the pre-main sequence (PMS) stage exhibit vigorous magnetic activity that significantly influences their circumstellar environments and the processes of planetary formation and evolution. In binary systems, tidal forces and magnetic interactions can further shape the magnetic geometry. We report a longitudinal preference of star spots, chromospheric activities, and flares in the active single-lined spectroscopic PMS binary system V2279 Cyg, based on long-term photometric observations from \textit{Kepler} and \textit{TESS} alongside spectroscopic data from LAMOST. The system is classified as a weak-line T Tauri binary, with component masses estimated at 0.86 $M_\odot$ and 0.27 $M_\odot$. V2279 Cyg's nearly circular orbit is synchronized with its 4.126-day rotational period. Observations reveal large star spot regions clustered near the far-side hemisphere. Spectroscopic data show strong, double-peak H$α$ emission, the strength of which is highly correlated with star spot distribution, indicating the presence of an active longitude on the primary star. We also mapped the prominence structure co-rotating with the primary star, suggesting a dense structure close to the near-side hemisphere. Furthermore, we identify an inactive longitude of flares during the 4-year \textit{Kepler} observations, where the frequency of flare activity is significantly reduced after the superior conjunction, marking the first such identification in active binary systems. Additionally, a white light superflare, releasing energy of $2.5 \times 10^{37}$ erg, was detected in \textit{TESS} observations. These findings provide valuable insights into the magnetic field geometry and dynamo processes in PMS binaries, underscoring the critical role of tidal interactions in shaping magnetic activities.
astro-ph.SR↗
3D Scene-Camera Representation with Joint Camera Photometric Optimization
Representing scenes from multi-view images is a crucial task in computer vision with extensive applications. However, inherent photometric distortions in the camera imaging can significantly degrade image quality. Without accounting for these distortions, the 3D scene representation may inadvertently incorporate erroneous information unrelated to the scene, diminishing the quality of the representation. In this paper, we propose a novel 3D scene-camera representation with joint camera photometric optimization. By introducing internal and external photometric model, we propose a full photometric model and corresponding camera representation. Based on simultaneously optimizing the parameters of the camera representation, the proposed method effectively separates scene-unrelated information from the 3D scene representation. Additionally, during the optimization of the photometric parameters, we introduce a depth regularization to prevent the 3D scene representation from fitting scene-unrelated information. By incorporating the camera model as part of the mapping process, the proposed method constructs a complete map that includes both the scene radiance field and the camera photometric model. Experimental results demonstrate that the proposed method can achieve high-quality 3D scene representations, even under conditions of imaging degradation, such as vignetting and dirt.
cs.CV↗
FAST drift scan survey for HI intensity mapping: simulation on hunting HI filament with pairwise stacking
Filaments stand as pivotal structures within the cosmic web. However, direct detection of the cold gas content of the filaments remains challenging due to its inherent low brightness temperature. With the TNG hydrodynamical simulations, we demonstrate the effectiveness of isolating faint filament HI signal from the FAST HI intensity mapping (IM) survey through pairwise stacking of galaxies, which yields an average HI filament signal amplitude of $\sim 0.29\ {μ{\rm K}}$ at $z\simeq 0.1$. However, our simulations reveal a non-negligible contribution from HI-rich galaxies within or near the filaments. Particularly, the faint galaxies dominantly contribute to the extra filament HI signal. Our simulation also shows that the measurement uncertainty is produced by both thermal noise and background variation caused by brightness leakage from surrounding random galaxies. Given a fixed total observation time, a wide-field HI IM survey, which includes a large number of galaxy pairs, can simultaneously reduce thermal noise to below the filament signal level and minimize background variation to a negligible level. Through the end-to-end simulation, this work demonstrates the critical role of the galaxy pairwise stacking method in future filament HI detection, outlining a road map for filament HI detection in the next-generation HI IM surveys.
astro-ph.CO↗
Pulsation Properties of Blazhko and Non-Blazhko RRab Stars
In this study, we conduct a comparative analysis of the properties of Blazhko and non-Blazhko RRab stars. We identified 1054 non-Blazhko and 785 Blazhko RRab stars in the photometric data observed by K2 mission, which, combined with those 37 stars observed in the original Kepler field, constituted our study sample. Using the Fourier Decomposition method, we calculated the pulsation parameters, including phase differences and amplitude ratios, for these RRab stars, revealing significant discrepancies in the pulsation parameters between Blazhko and non-Blazhko RRab stars. However, distinguishing between Blazhko and Non-Blazhko RRab stars based on Fourier parameters remains challenging due to the significant overlap in their distributions. By cross-matching our sample with the LRS of LAMOST DR12, we identified 147 Blazhko and 111 non-Blazhko RRab stars, which exhibit similar metallicity distributions. Furthermore, cross-matching with Gaia DR3 data yielded 766 Blazhko and 950 non-Blazhko RRab stars, showing differences in color indices but not in absolute magnitudes. Our findings suggested the Blazhko effect is linked to pulsation parameters and colors, rather than metallicities or absolute magnitude.
astro-ph.SR↗