arXiv subjects

Ye Tian

Publications and source records attributed to Ye Tian.

At least 145 records · Page 8Linked to original sources

arXiv2025

Ethereum Fraud Detection via Joint Transaction Language Model and Graph Representation Learning

Ethereum faces growing fraud threats. Current fraud detection methods, whether employing graph neural networks or sequence models, fail to consider the semantic information and similarity patterns within transactions. Moreover, these approaches do not leverage the potential synergistic benefits of combining both types of models. To address these challenges, we propose TLMG4Eth that combines a transaction language model with graph-based methods to capture semantic, similarity, and structural features of transaction data in Ethereum. We first propose a transaction language model that converts numerical transaction data into meaningful transaction sentences, enabling the model to learn explicit transaction semantics. Then, we propose a transaction attribute similarity graph to learn transaction similarity information, enabling us to capture intuitive insights into transaction anomalies. Additionally, we construct an account interaction graph to capture the structural information of the account transaction network. We employ a deep multi-head attention network to fuse transaction semantic and similarity embeddings, and ultimately propose a joint training approach for the multi-head attention network and the account interaction graph to obtain the synergistic benefits of both.

cs.CR↗

arXiv2025

Diffusion-Sharpening: Fine-tuning Diffusion Models with Denoising Trajectory Sharpening

We propose Diffusion-Sharpening, a fine-tuning approach that enhances downstream alignment by optimizing sampling trajectories. Existing RL-based fine-tuning methods focus on single training timesteps and neglect trajectory-level alignment, while recent sampling trajectory optimization methods incur significant inference NFE costs. Diffusion-Sharpening overcomes this by using a path integral framework to select optimal trajectories during training, leveraging reward feedback, and amortizing inference costs. Our method demonstrates superior training efficiency with faster convergence, and best inference efficiency without requiring additional NFEs. Extensive experiments show that Diffusion-Sharpening outperforms RL-based fine-tuning methods (e.g., Diffusion-DPO) and sampling trajectory optimization methods (e.g., Inference Scaling) across diverse metrics including text alignment, compositional capabilities, and human preferences, offering a scalable and efficient solution for future diffusion model fine-tuning. Code: https://github.com/Gen-Verse/Diffusion-Sharpening

cs.CV↗

arXiv2025

A Hybrid Dynamic Subarray Architecture for Efficient DOA Estimation in THz Ultra-Massive Hybrid MIMO Systems

Terahertz (THz) communication combined with ultra-massive multiple-input multiple-output (UM-MIMO) technology is promising for 6G wireless systems, where fast and precise direction-of-arrival (DOA) estimation is crucial for effective beamforming. However, finding DOAs in THz UM-MIMO systems faces significant challenges: while reducing hardware complexity, the hybrid analog-digital (HAD) architecture introduces inherent difficulties in spatial information acquisition the large-scale antenna array causes significant deviations in eigenvalue decomposition results; and conventional two-dimensional DOA estimation methods incur prohibitively high computational overhead, hindering fast and accurate realization. To address these challenges, we propose a hybrid dynamic subarray (HDS) architecture that strategically divides antenna elements into subarrays, ensuring phase differences between subarrays correlate exclusively with single-dimensional DOAs. Leveraging this architectural innovation, we develop two efficient algorithms for DOA estimation: a reduced-dimension MUSIC (RD-MUSIC) algorithm that enables fast processing by correcting large-scale array estimation bias, and an improved version that further accelerates estimation by exploiting THz channel sparsity to obtain initial closed-form solutions through specialized two-RF-chain configuration. Furthermore, we develop a theoretical framework through Cramér-Rao lower bound analysis, providing fundamental insights for different HDS configurations. Extensive simulations demonstrate that our solution achieves both superior estimation accuracy and computational efficiency, making it particularly suitable for practical THz UM-MIMO systems.

eess.SP↗

arXiv2024

Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models

The rapidly developing field of large multimodal models (LMMs) has led to the emergence of diverse models with remarkable capabilities. However, existing benchmarks fail to comprehensively, objectively and accurately evaluate whether LMMs align with the diverse needs of humans in real-world scenarios. To bridge this gap, we propose the Multi-Dimensional Insights (MDI) benchmark, which includes over 500 images covering six common scenarios of human life. Notably, the MDI-Benchmark offers two significant advantages over existing evaluations: (1) Each image is accompanied by two types of questions: simple questions to assess the model's understanding of the image, and complex questions to evaluate the model's ability to analyze and reason beyond basic content. (2) Recognizing that people of different age groups have varying needs and perspectives when faced with the same scenario, our benchmark stratifies questions into three age categories: young people, middle-aged people, and older people. This design allows for a detailed assessment of LMMs' capabilities in meeting the preferences and needs of different age groups. With MDI-Benchmark, the strong model like GPT-4o achieve 79% accuracy on age-related tasks, indicating that existing LMMs still have considerable room for improvement in addressing real-world applications. Looking ahead, we anticipate that the MDI-Benchmark will open new pathways for aligning real-world personalization in LMMs. The MDI-Benchmark data and evaluation code are available at https://mdi-benchmark.github.io/

cs.AI↗

arXiv2024

A Visual-inertial Localization Algorithm using Opportunistic Visual Beacons and Dead-Reckoning for GNSS-Denied Large-scale Applications

With the development of smart cities, the demand for continuous pedestrian navigation in large-scale urban environments has significantly increased. While global navigation satellite systems (GNSS) provide low-cost and reliable positioning services, they are often hindered in complex urban canyon environments. Thus, exploring opportunistic signals for positioning in urban areas has become a key solution. Augmented reality (AR) allows pedestrians to acquire real-time visual information. Accordingly, we propose a low-cost visual-inertial positioning solution. This method comprises a lightweight multi-scale group convolution (MSGC)-based visual place recognition (VPR) neural network, a pedestrian dead reckoning (PDR) algorithm, and a visual/inertial fusion approach based on a Kalman filter with gross error suppression. The VPR serves as a conditional observation to the Kalman filter, effectively correcting the errors accumulated through the PDR method. This enables the entire algorithm to ensure the reliability of long-term positioning in GNSS-denied areas. Extensive experimental results demonstrate that our method maintains stable positioning during large-scale movements. Compared to the lightweight MobileNetV3-based VPR method, our proposed VPR solution improves Recall@1 by at least 3\% on two public datasets while reducing the number of parameters by 63.37\%. It also achieves performance that is comparable to the VGG16-based method. The VPR-PDR algorithm improves localization accuracy by more than 40\% compared to the original PDR.

cs.CV↗

arXiv2024

Ultrafast Spin Rotation of Relativistic Lepton Beams via Terahertz Wave in a Dielectric-Lined Waveguide

Spin rotation is central for the spin-manipulation of lepton beams which, in turn, plays an important role in investigation of the properties of spin-polarized lepton beams and the examination of spin-dependent interactions. However, realization of compact and ultrafast spin rotation of lepton beams, between longitudinal and transverse polarizations, still faces significant challenges. Here, we put forward a novel method for ultrafast (picosecond-timescale) spin rotation of a relativistic lepton beam via employing a moderate-intensity terahertz (THz) wave in a dielectric-lined waveguide (DLW). The lepton beam undergoes spin precession induced by the THz magnetic field. We find that optimizing the lepton velocity and THz phase velocity in the DLW can mitigate the impact of transverse Lorentz forces on the lepton beam and increase the precession frequency, thereby maintaining the beam quality and enhancing the efficiency of transverse-to-longitudinal spin rotation. The final polarization degree of the lepton beam exceeds $98\%$, and the energy spread can be improved significantly. Flexibility in adjusting the electromagnetic modes within the DLW adds further potential for spin-manipulation, and holds promise for advancing the development of spin-polarized particle beams, which have broad applications in materials science and atomic, nuclear, and high-energy physics.

physics.plasm-ph↗

arXiv2024

Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing

Despite the impressive capabilities of Large Language Models (LLMs) on various tasks, they still struggle with scenarios that involves complex reasoning and planning. Recent work proposed advanced prompting techniques and the necessity of fine-tuning with high-quality data to augment LLMs' reasoning abilities. However, these approaches are inherently constrained by data availability and quality. In light of this, self-correction and self-learning emerge as viable solutions, employing strategies that allow LLMs to refine their outputs and learn from self-assessed rewards. Yet, the efficacy of LLMs in self-refining its response, particularly in complex reasoning and planning task, remains dubious. In this paper, we introduce AlphaLLM for the self-improvements of LLMs, which integrates Monte Carlo Tree Search (MCTS) with LLMs to establish a self-improving loop, thereby enhancing the capabilities of LLMs without additional annotations. Drawing inspiration from the success of AlphaGo, AlphaLLM addresses the unique challenges of combining MCTS with LLM for self-improvement, including data scarcity, the vastness search spaces of language tasks, and the subjective nature of feedback in language tasks. AlphaLLM is comprised of prompt synthesis component, an efficient MCTS approach tailored for language tasks, and a trio of critic models for precise feedback. Our experimental results in mathematical reasoning tasks demonstrate that AlphaLLM significantly enhances the performance of LLMs without additional annotations, showing the potential for self-improvement in LLMs.

cs.CL↗

arXiv2024

The plan for a super $η$ factory at Huizhou accelerator complex

As an approximate Goldstone boson with zero quantum number and zero standard model charge, the decay processes of long-lived $η$ meson offer a unique opportunity to explore new physics beyond the standard model and new sources of CP violation, as well as test the low-energy QCD theory and measure the fundamental parameters of light quarks. To pursue these goals in the physics frontiers, we propose a plan to construct a super $η$ factory at HIAF high-energy terminal or at CiADS after its energy upgrade. The high-intensity proton beam at HIAF enables the production of a vast number of $η$ samples, exceeding $10^{13}$ events per year in the first stage, utilizing multiple layers of thin targets made of light nucleus. This paper presents the physics goals, the first-version conceptual design of the spectrometer, and some preliminary simulation results.

hep-ph↗

arXiv2024

Simulation of dark scalar particle sensitivity in $η$ rare decay channels at HIAF

Searching dark portal particle is a hot topic in particle physics frontier. We present a simulation study of an experiment targeted for searching the scalar portal particle at Huizhou $η$ factory. The HIAF high-intensity proton beam and a high event-rate spectrometer are suggested for the experiment aimed for the discovery of new physics. Under the conservative estimation, $5.9\times 10^{11}$ $η$ events could be produced in one month running of the experiment. The hadronic production of $η$ meson ($p + ^7\text{Li} \rightarrow ηX$) is simulated at beam energy of 1.8 GeV using GiBUU event generator. We tend to search for the light dark scalar particle in the rare decay channels $η\rightarrow S π^0 \rightarrow π^+ π^- π^0$ and $η\rightarrow S π^0 \rightarrow e^+ e^- π^0$. The detection efficiencies of the channels and the spectrometer resolutions are studied in the simulation. We also present the projected upper limits of the decay branching ratios of the dark scalar particle and the projected sensitivities to the model parameters.

hep-ph↗

arXiv2024

LAMBDA: Covering the Multimodal Critical Scenarios for Automated Driving Systems by Search Space Quantization

Scenario-based virtual testing is one of the most significant methods to test and evaluate the safety of automated driving systems (ADSs). However, it is impractical to enumerate all concrete scenarios in a logical scenario space and test them exhaustively. Recently, Black-Box Optimization (BBO) was introduced to accelerate the scenario-based test of ADSs by utilizing the historical test information to generate new test cases. However, a single optimum found by the BBO algorithm is insufficient for the purpose of a comprehensive safety evaluation of ADSs in a logical scenario. In fact, all the subspaces representing danger in the logical scenario space, rather than only the most critical concrete scenario, play a more significant role for the safety evaluation. Covering as many of the critical concrete scenarios in a logical scenario space through a limited number of tests is defined as the Black-Box Coverage (BBC) problem in this paper. We formalized this problem in a sample-based search paradigm and constructed a coverage criterion with Confusion Matrix Analysis. Furthermore, we propose LAMBDA (Latent-Action Monte-Carlo Beam Search with Density Adaption) to solve BBC problems. LAMBDA can quickly focus on critical subspaces by recursively partitioning the logical scenario space into accepted and rejected parts. Compared with its predecessor LaMCTS, LAMBDA introduces sampling density to overcome the sampling bias from optimization and Beam Search to obtain more parallelizability. Experimental results show that LAMBDA achieves state-of-the-art performance among all baselines and can reach at most 33 and 6000 times faster than Random Search to get 95% coverage of the critical areas in 2- and 5-dimensional synthetic functions, respectively. Experiments also demonstrate that LAMBDA has a promising future in the safety evaluation of ADSs in virtual tests.

cs.AI↗

arXiv2024

VidEgoThink: Assessing Egocentric Video Understanding Capabilities for Embodied AI

Recent advancements in Multi-modal Large Language Models (MLLMs) have opened new avenues for applications in Embodied AI. Building on previous work, EgoThink, we introduce VidEgoThink, a comprehensive benchmark for evaluating egocentric video understanding capabilities. To bridge the gap between MLLMs and low-level control in Embodied AI, we design four key interrelated tasks: video question-answering, hierarchy planning, visual grounding and reward modeling. To minimize manual annotation costs, we develop an automatic data generation pipeline based on the Ego4D dataset, leveraging the prior knowledge and multimodal capabilities of GPT-4o. Three human annotators then filter the generated data to ensure diversity and quality, resulting in the VidEgoThink benchmark. We conduct extensive experiments with three types of models: API-based MLLMs, open-source image-based MLLMs, and open-source video-based MLLMs. Experimental results indicate that all MLLMs, including GPT-4o, perform poorly across all tasks related to egocentric video understanding. These findings suggest that foundation models still require significant advancements to be effectively applied to first-person scenarios in Embodied AI. In conclusion, VidEgoThink reflects a research trend towards employing MLLMs for egocentric vision, akin to human capabilities, enabling active observation and interaction in the complex real-world environments.

cs.CV↗

arXiv2024

RealCompo: Balancing Realism and Compositionality Improves Text-to-Image Diffusion Models

Diffusion models have achieved remarkable advancements in text-to-image generation. However, existing models still have many difficulties when faced with multiple-object compositional generation. In this paper, we propose RealCompo, a new training-free and transferred-friendly text-to-image generation framework, which aims to leverage the respective advantages of text-to-image models and spatial-aware image diffusion models (e.g., layout, keypoints and segmentation maps) to enhance both realism and compositionality of the generated images. An intuitive and novel balancer is proposed to dynamically balance the strengths of the two models in denoising process, allowing plug-and-play use of any model without extra training. Extensive experiments show that our RealCompo consistently outperforms state-of-the-art text-to-image models and spatial-aware image diffusion models in multiple-object compositional generation while keeping satisfactory realism and compositionality of the generated images. Notably, our RealCompo can be seamlessly extended with a wide range of spatial-aware image diffusion models and stylized diffusion models. Our code is available at: https://github.com/YangLing0818/RealCompo

cs.CV↗

arXiv2024

VideoTetris: Towards Compositional Text-to-Video Generation

Diffusion models have demonstrated great success in text-to-video (T2V) generation. However, existing methods may face challenges when handling complex (long) video generation scenarios that involve multiple objects or dynamic changes in object numbers. To address these limitations, we propose VideoTetris, a novel framework that enables compositional T2V generation. Specifically, we propose spatio-temporal compositional diffusion to precisely follow complex textual semantics by manipulating and composing the attention maps of denoising networks spatially and temporally. Moreover, we propose an enhanced video data preprocessing to enhance the training data regarding motion dynamics and prompt understanding, equipped with a new reference frame attention mechanism to improve the consistency of auto-regressive video generation. Extensive experiments demonstrate that our VideoTetris achieves impressive qualitative and quantitative results in compositional T2V generation. Code is available at: https://github.com/YangLing0818/VideoTetris

cs.CV↗

arXiv2024

Towards Self-Improvement of LLMs via MCTS: Leveraging Stepwise Knowledge with Curriculum Preference Learning

Monte Carlo Tree Search (MCTS) has recently emerged as a powerful technique for enhancing the reasoning capabilities of LLMs. Techniques such as SFT or DPO have enabled LLMs to distill high-quality behaviors from MCTS, improving their reasoning performance. However, existing distillation methods underutilize the rich trajectory information generated by MCTS, limiting the potential for improvements in LLM reasoning. In this paper, we propose AlphaLLM-CPL, a novel pairwise training framework that enables LLMs to self-improve through MCTS behavior distillation. AlphaLLM-CPL efficiently leverages MCTS trajectories via two key innovations: (1) AlphaLLM-CPL constructs stepwise trajectory pairs from child nodes sharing the same parent in the search tree, providing step-level information for more effective MCTS behavior distillation. (2) AlphaLLM-CPL introduces curriculum preference learning, dynamically adjusting the training sequence of trajectory pairs in each offline training epoch to prioritize critical learning steps and mitigate overfitting. Experimental results on mathematical reasoning tasks demonstrate that AlphaLLM-CPL significantly outperforms previous MCTS behavior distillation methods, substantially boosting the reasoning capabilities of LLMs.

cs.LG↗

arXiv2024

Flow to Rare Events: An Application of Normalizing Flow in Temporal Importance Sampling for Automated Vehicle Validation

Automated Vehicle (AV) validation based on simulated testing requires unbiased evaluation and high efficiency. One effective solution is to increase the exposure to risky rare events while reweighting the probability measure. However, characterizing the distribution of risky events is particularly challenging due to the paucity of samples and the temporality of continuous scenario variables. To solve it, we devise a method to represent, generate, and reweight the distribution of risky rare events. We decompose the temporal evolution of continuous variables into distribution components based on conditional probability. By introducing the Risk Indicator Function, the distribution of risky rare events is theoretically precipitated out of naturalistic driving distribution. This targeted distribution is practically generated via Normalizing Flow, which achieves exact and tractable probability evaluation of intricate distribution. The rare event distribution is then demonstrated as the advantageous Importance Sampling distribution. We also promote the technique of temporal Importance Sampling. The combined method, named as TrimFlow, is executed to estimate the collision rate of Car-following scenarios as a tentative practice. The results showed that sampling background vehicle maneuvers from rare event distribution could evolve testing scenarios to hazardous states. TrimFlow reduced 86.1% of tests compared to generating testing scenarios according to their exposure in the naturalistic driving environment. In addition, the TrimFlow method is not limited to one specific type of functional scenario.

cs.LG↗

arXiv2024

Zeta elements for elliptic curves and applications

Let $E$ be an elliptic curve defined over $\mathbb{Q}$ with conductor $N$ and $p\nmid 2N$ a prime. Let $L$ be an imaginary quadratic field with $p$ split. We prove the existence of $p$-adic zeta element for $E$ over $L$, encoding two different $p$-adic $L$-functions associated to $E$ over $L$ via explicit reciprocity laws at the primes above $p$. We formulate a main conjecture for $E$ over $L$ in terms of the zeta element, mediating different main conjectures in which the $p$-adic $L$-functions appear, and prove some results toward them. The zeta element has various applications to the arithmetic of elliptic curves. This includes a proof of main conjecture for semistable elliptic curves $E$ over $\mathbb{Q}$ at supersingular primes $p$, as conjectured by Kobayashi in 2002. It leads to the $p$-part of the conjectural Birch and Swinnerton-Dyer (BSD) formula for such curves of analytic rank zero or one, and enables us to present the first infinite families of non-CM elliptic curves for which the BSD conjecture is true. We provide further evidence towards the BSD conjecture: new cases of $p$-converse to the Gross--Zagier and Kolyvagin theorem, and $p$-part of the BSD formula for ordinary primes $p$. Along the way, we give a proof of a conjecture of Perrin-Riou connecting Beilinson--Kato elements with rational points.

math.NT↗

arXiv2024

The Role of Transformer Models in Advancing Blockchain Technology: A Systematic Survey

As blockchain technology rapidly evolves, the demand for enhanced efficiency, security, and scalability grows.Transformer models, as powerful deep learning architectures,have shown unprecedented potential in addressing various blockchain challenges. However, a systematic review of Transformer applications in blockchain is lacking. This paper aims to fill this research gap by surveying over 200 relevant papers, comprehensively reviewing practical cases and research progress of Transformers in blockchain applications. Our survey covers key areas including anomaly detection, smart contract security analysis, cryptocurrency prediction and trend analysis, and code summary generation. To clearly articulate the advancements of Transformers across various blockchain domains, we adopt a domain-oriented classification system, organizing and introducing representative methods based on major challenges in current blockchain research. For each research domain,we first introduce its background and objectives, then review previous representative methods and analyze their limitations,and finally introduce the advancements brought by Transformer models. Furthermore, we explore the challenges of utilizing Transformer, such as data privacy, model complexity, and real-time processing requirements. Finally, this article proposes future research directions, emphasizing the importance of exploring the Transformer architecture in depth to adapt it to specific blockchain applications, and discusses its potential role in promoting the development of blockchain technology. This review aims to provide new perspectives and a research foundation for the integrated development of blockchain technology and machine learning, supporting further innovation and application expansion of blockchain technology.

cs.LG↗

arXiv2024

SIaM: Self-Improving Code-Assisted Mathematical Reasoning of Large Language Models

There is a growing trend of teaching large language models (LLMs) to solve mathematical problems through coding. Existing studies primarily focus on prompting powerful, closed-source models to generate seed training data followed by in-domain data augmentation, equipping LLMs with considerable capabilities for code-aided mathematical reasoning. However, continually training these models on augmented data derived from a few datasets such as GSM8K may impair their generalization abilities and restrict their effectiveness to a narrow range of question types. Conversely, the potential of improving such LLMs by leveraging large-scale, expert-written, diverse math question-answer pairs remains unexplored. To utilize these resources and tackle unique challenges such as code response assessment, we propose a novel paradigm that uses a code-based critic model to guide steps including question-code data construction, quality control, and complementary evaluation. We also explore different alignment algorithms with self-generated instruction/preference data to foster continuous improvement. Experiments across both in-domain (up to +5.7%) and out-of-domain (+4.4%) benchmarks in English and Chinese demonstrate the effectiveness of the proposed paradigm.

cs.CL↗