arXiv subjects

Tong Li

Publications and source records attributed to Tong Li.

At least 73 records · Page 4Linked to original sources

arXiv2025

Neural Tucker Convolutional Network for Water Quality Analysis

Water quality monitoring is a core component of ecological environmental protection. However, due to sensor failure or other inevitable factors, data missing often exists in long-term monitoring, posing great challenges in water quality analysis. This paper proposes a Neural Tucker Convolutional Network (NTCN) model for water quality data imputation, which features the following key components: a) Encode different mode entities into respective embedding vectors, and construct a Tucker interaction tensor by outer product operations to capture the complex mode-wise feature interactions; b) Use 3D convolution to extract fine-grained spatiotemporal features from the interaction tensor. Experiments on three real-world water quality datasets show that the proposed NTCN model outperforms several state-of-the-art imputation models in terms of accuracy.

cs.LG↗

arXiv2025

Reexamining the search for light ALPs at flavor and forward accelerator experiments

The axion-like particle (ALP) is a well-motivated extension of the Standard Model. In this work, we revisit the sensitivity of forward accelerator experiments to light long-lived ALPs, and analyze flavor constraints. Our analysis incorporates recent measurements of the rare decays $B\to K + X$ and $K\to π+X$, which place stringent bounds on quark flavor violation of a light ALP. We consider the complete list of hadronic modes in the calculation of the ALP decay rate and exclusive production channels based on recent improvements. The analysis includes the discussion of tree-level quark flavor-violating couplings in addition to a universal flavor-conserving ALP coupling to fermions and the electroweak ALP couplings. Our results demonstrate the complementarity of heavy meson decays and forward accelerator facilities in probing light ALPs. The interplay between two ALP couplings is also investigated.

hep-ph↗

arXiv2025

CSMapping: Scalable Crowdsourced Semantic Mapping and Topology Inference for Autonomous Driving

Crowdsourcing enables scalable autonomous driving map construction, but low-cost sensor noise hinders quality from improving with data volume. We propose CSMapping, a system that produces accurate semantic maps and topological road centerlines whose quality consistently increases with more crowdsourced data. For semantic mapping, we train a latent diffusion model on HD maps (optionally conditioned on SD maps) to learn a generative prior of real-world map structure, without requiring paired crowdsourced/HD-map supervision. This prior is incorporated via constrained MAP optimization in latent space, ensuring robustness to severe noise and plausible completion in unobserved areas. Initialization uses a robust vectorized mapping module followed by diffusion inversion; optimization employs efficient Gaussian-basis reparameterization, projected gradient descent zobracket multi-start, and latent-space factor-graph for global consistency. For topological mapping, we apply confidence-weighted k-medoids clustering and kinematic refinement to trajectories, yielding smooth, human-like centerlines robust to trajectory variation. Experiments on nuScenes, Argoverse 2, and a large proprietary dataset achieve state-of-the-art semantic and topological mapping performance, with thorough ablation and scalability studies.

cs.CV↗

arXiv2025

Laser-assisted Light-by-Light Scattering in Born-Infeld and Axion-like Particle Theories

The precision measurements of well-known light-by-light reactions lead to important insights of nonlinear quantum electrodynamics (QED) vacuum polarization. The laser of an intense electromagnetic field strength provides an essential tool for exploring nonlinear QED and new physics beyond Standard Model (SM) in the high-precision frontier. In this work, we propose to search for low-energy light-by-light scattering in the collision of a photon beam and a laser pulse of classical background field. We aim to investigate the impact of Born-Infeld (BI) and axion-like particle (ALP) theories on laser-assisted light-by-light scattering. We calculate the QED light-by-light scattering cross section using complete QED helicity amplitudes, and then combine them with the amplitudes in BI or ALP theory to evaluate the total cross section. The laser-assisted SM light-by-light scattering should be observable in future experiments with very moderate integrated luminosities. The sensitivity of laser-assisted light-by-light scattering to BI and ALP parameters is presented.

hep-ph↗

arXiv2025

Dynamic Population Distribution Aware Human Trajectory Generation with Diffusion Model

Human trajectory data is crucial in urban planning, traffic engineering, and public health. However, directly using real-world trajectory data often faces challenges such as privacy concerns, data acquisition costs, and data quality. A practical solution to these challenges is trajectory generation, a method developed to simulate human mobility behaviors. Existing trajectory generation methods mainly focus on capturing individual movement patterns but often overlook the influence of population distribution on trajectory generation. In reality, dynamic population distribution reflects changes in population density across different regions, significantly impacting individual mobility behavior. Thus, we propose a novel trajectory generation framework based on a diffusion model, which integrates the dynamic population distribution constraints to guide high-fidelity generation outcomes. Specifically, we construct a spatial graph to enhance the spatial correlation of trajectories. Then, we design a dynamic population distribution aware denoising network to capture the spatiotemporal dependencies of human mobility behavior as well as the impact of population distribution in the denoising process. Extensive experiments show that the trajectories generated by our model can resemble real-world trajectories in terms of some critical statistical metrics, outperforming state-of-the-art algorithms by over 54%.

cs.LG↗

arXiv2025

Pentest-R1: Towards Autonomous Penetration Testing Reasoning Optimized via Two-Stage Reinforcement Learning

Automating penetration testing is crucial for enhancing cybersecurity, yet current Large Language Models (LLMs) face significant limitations in this domain, including poor error handling, inefficient reasoning, and an inability to perform complex end-to-end tasks autonomously. To address these challenges, we introduce Pentest-R1, a novel framework designed to optimize LLM reasoning capabilities for this task through a two-stage reinforcement learning pipeline. We first construct a dataset of over 500 real-world, multi-step walkthroughs, which Pentest-R1 leverages for offline reinforcement learning (RL) to instill foundational attack logic. Subsequently, the LLM is fine-tuned via online RL in an interactive Capture The Flag (CTF) environment, where it learns directly from environmental feedback to develop robust error self-correction and adaptive strategies. Our extensive experiments on the Cybench and AutoPenBench benchmarks demonstrate the framework's effectiveness. On AutoPenBench, Pentest-R1 achieves a 24.2\% success rate, surpassing most state-of-the-art models and ranking second only to Gemini 2.5 Flash. On Cybench, it attains a 15.0\% success rate in unguided tasks, establishing a new state-of-the-art for open-source LLMs and matching the performance of top proprietary models. Ablation studies confirm that the synergy of both training stages is critical to its success.

cs.AI↗

arXiv2025

Learning to Hash for Recommendation: A Survey

With the explosive growth of users and items, Recommender Systems are facing unprecedented challenges in terms of retrieval efficiency and storage overhead. Learning to Hash techniques have emerged as a promising solution to these issues by encoding high-dimensional data into compact hash codes. As a result, hashing-based recommendation methods (HashRec) have garnered growing attention for enabling large-scale and efficient recommendation services. This survey provides a comprehensive overview of state-of-the-art HashRec algorithms. Specifically, we begin by introducing the common two-tower architecture used in the recall stage and by detailing two predominant hash search strategies. Then, we categorize existing works into a three-tier taxonomy based on: (i) learning objectives, (ii) optimization strategies, and (iii) recommendation scenarios. Additionally, we summarize widely adopted evaluation metrics for assessing both the effectiveness and efficiency of HashRec algorithms. Finally, we discuss current limitations in the field and outline promising directions for future research. We index these HashRec methods at the repository \href{https://github.com/Luo-Fangyuan/HashRec}{https://github.com/Luo-Fangyuan/HashRec}.

cs.IR↗

arXiv2025

Quantum spin-tensor Hall effect protected by pseudo time-reversal symmetry

The celebrated family of the Hall effect plays a fundamental role in modern physics. Starting from the anomalous Hall effect (AHE) and the quantum AHE (QAHE) with broken time-reversal symmetry (TRS) to their spinful generalizations, including spin Hall effect (SHE) and quantum SHE (QSHE) protected by TRS, they reveal rich transport and topological phenomena. However, in larger-spin $S$ ($S>1/2$) systems, besides charge current and spin current, there arise higher-rank spin-tensor currents. Recent work has uncovered an interesting spin-tensor Hall effect with spin-tensor currents in these larger-spin systems. Taking a step further, this work discovers a new class of topological states of matter dubbed \textit{quantum spin-tensor Hall} (QSTH) insulators with broken TRS, and their nontrivial topology is protected by a unique \textit{pseudo-TRS}. Most strikingly, QSTH insulators exhibit a quantized rank-2 spin-tensor Hall conductivity, whereas both charge (rank-0) and spin (rank-1) conductivities vanish. We also fully characterize their topological properties and highlight the physical interpretations via the underlying connections to QSHE. Our work enriches the family of the famous Hall effects and sheds light on the intriguing topological state of matter in larger-spin systems. It further offers new avenues toward spin-tensor-tronics and low-power atomtronics.

cond-mat.mes-hall↗

arXiv2025

Traceback of Poisoning Attacks to Retrieval-Augmented Generation

Large language models (LLMs) integrated with retrieval-augmented generation (RAG) systems improve accuracy by leveraging external knowledge sources. However, recent research has revealed RAG's susceptibility to poisoning attacks, where the attacker injects poisoned texts into the knowledge database, leading to attacker-desired responses. Existing defenses, which predominantly focus on inference-time mitigation, have proven insufficient against sophisticated attacks. In this paper, we introduce RAGForensics, the first traceback system for RAG, designed to identify poisoned texts within the knowledge database that are responsible for the attacks. RAGForensics operates iteratively, first retrieving a subset of texts from the database and then utilizing a specially crafted prompt to guide an LLM in detecting potential poisoning texts. Empirical evaluations across multiple datasets demonstrate the effectiveness of RAGForensics against state-of-the-art poisoning attacks. This work pioneers the traceback of poisoned texts in RAG systems, providing a practical and promising defense mechanism to enhance their security. Our code is available at: https://github.com/zhangbl6618/RAG-Responsibility-Attribution

cs.CR↗

arXiv2025

Who Taught the Lie? Responsibility Attribution for Poisoned Knowledge in Retrieval-Augmented Generation

Retrieval-Augmented Generation (RAG) integrates external knowledge into large language models to improve response quality. However, recent work has shown that RAG systems are highly vulnerable to poisoning attacks, where malicious texts are inserted into the knowledge database to influence model outputs. While several defenses have been proposed, they are often circumvented by more adaptive or sophisticated attacks. This paper presents RAGOrigin, a black-box responsibility attribution framework designed to identify which texts in the knowledge database are responsible for misleading or incorrect generations. Our method constructs a focused attribution scope tailored to each misgeneration event and assigns a responsibility score to each candidate text by evaluating its retrieval ranking, semantic relevance, and influence on the generated response. The system then isolates poisoned texts using an unsupervised clustering method. We evaluate RAGOrigin across seven datasets and fifteen poisoning attacks, including newly developed adaptive poisoning strategies and multi-attacker scenarios. Our approach outperforms existing baselines in identifying poisoned content and remains robust under dynamic and noisy conditions. These results suggest that RAGOrigin provides a practical and effective solution for tracing the origins of corrupted knowledge in RAG systems. Our code is available at: https://github.com/zhangbl6618/RAG-Responsibility-Attribution

cs.CR↗

arXiv2025

FENIX: Enabling In-Network DNN Inference with FPGA-Enhanced Programmable Switches

Machine learning (ML) is increasingly used in network data planes for advanced traffic analysis, but existing solutions (such as FlowLens, N3IC, BoS) still struggle to simultaneously achieve low latency, high throughput, and high accuracy. To address these challenges, we present FENIX, a hybrid in-network ML system that performs feature extraction on programmable switch ASICs and deep neural network inference on FPGAs. FENIX introduces a Data Engine that leverages a probabilistic token bucket algorithm to control the sending rate of feature streams, effectively addressing the throughput gap between programmable switch ASICs and FPGAs. In addition, FENIX designs a Model Engine to enable high-accuracy deep neural network inference in the network, overcoming the difficulty of deploying complex models on resource-constrained switch chips. We implement FENIX on a programmable switch platform that integrates a Tofino ASIC and a ZU19EG FPGA directly, and evaluate it on real-world network traffic datasets. Our results show that FENIX achieves microsecond-level inference latency and multi-terabit throughput with low hardware overhead, and delivers over 90% accuracy on mainstream network traffic classification tasks, outperforming the state of the art.

cs.NI↗

arXiv2025

Is Dark Matter the origin of the $B\to K ν\barν$ excess at Belle II?

We present two models of dark matter (DM) that can provide a natural explanation of the excess of $B^+\to K^+ +\,\text{invisible}$ events with respect to the Standard Model (SM) prediction for $B^+\to K^+ ν\barν$, which has been reported by the Belle II collaboration. Interactions between the dark and the visible sector are mediated by an axion-like particle (ALP) in one case, by the kinetic mixing between a dark photon and the SM photon in the second case. Both models encompass a light fermion singlet as the DM candidate and can account for the observed DM relic abundance through, respectively, the freeze-in and the freeze-out production mechanism, while simultaneously explaining the Belle II excess.

hep-ph↗

arXiv2025

New Physics Search at the CEPC: a General Perspective

The Circular Electron-Positron Collider (CEPC), a proposed next-generation Higgs factory, provides new opportunities to explore physics beyond the Standard Model (SM). With its clean electron-positron collision environment and the ability to collect large samples of Higgs, W, and Z bosons, the CEPC enables precision measurements and searches for new physics. This white paper outlines the CEPC's discovery potential, including studies of exotic decays of the Higgs, Z, and top quarks, dark matter and dark sector phenomena, long-lived particles, supersymmetry, and neutrino-related signatures. Advanced detector technologies and reconstruction techniques, such as one-to-one correspondence reconstruction and jet origin identification, significantly improve sensitivity to rare and weakly interacting processes. The CEPC is particularly well suited to probe the electroweak phase transition and test models of electroweak baryogenesis and dark sector interactions. In addition, global fit analyses highlight the CEPC's complementary role in constraining a wide range of new physics scenarios. These features position the CEPC as a powerful tool for exploring the next frontier in fundamental particle physics in the post-Higgs discovery era.

hep-ex↗

arXiv2025

Beyond Human Demonstrations: Diffusion-Based Reinforcement Learning to Generate Data for VLA Training

Vision-language-action (VLA) models have shown strong generalization across tasks and embodiments; however, their reliance on large-scale human demonstrations limits their scalability owing to the cost and effort of manual data collection. Reinforcement learning (RL) offers a potential alternative to generate demonstrations autonomously, yet conventional RL algorithms often struggle on long-horizon manipulation tasks with sparse rewards. In this paper, we propose a modified diffusion policy optimization algorithm to generate high-quality and low-variance trajectories, which contributes to a diffusion RL-powered VLA training pipeline. Our algorithm benefits from not only the high expressiveness of diffusion models to explore complex and diverse behaviors but also the implicit regularization of the iterative denoising process to yield smooth and consistent demonstrations. We evaluate our approach on the LIBERO benchmark, which includes 130 long-horizon manipulation tasks, and show that the generated trajectories are smoother and more consistent than both human demonstrations and those from standard Gaussian RL policies. Further, training a VLA model exclusively on the diffusion RL-generated data achieves an average success rate of 81.9%, which outperforms the model trained on human data by +5.3% and that on Gaussian RL-generated data by +12.6%. The results highlight our diffusion RL as an effective alternative for generating abundant, high-quality, and low-variance demonstrations for VLA models.

cs.RO↗

arXiv2025

Laser induced Compton Scattering to Dark Matter in Effective Field Theory

The detection of light dark matter (DM) is a longstanding challenge in terrestrial experiments. High-intensity facility of an intense electromagnetic field may provide a plausible strategy to study strong-field particle physics and search for light DM. In this work, we propose to search for light DM particle through the nonlinear Compton scattering in the presence of a high-intense laser field. An ultra-relativistic electron beam collides with an intense laser pulse of a number of optical photons and then decays to a pair of DM particles. We take into account the Dirac-type fermionic DM in leptophilic scenario and the DM-electron interactions in the framework of effective field theory. The decay rates of electron to a DM pair are calculated for effective DM operators of different bilinear products. We show the sensitivities of laser induced Compton scattering to the effective cutoff scale for DM lighter than 1 MeV and compare with direct detection experiments.

hep-ph↗

arXiv2025

Constraint on ultralight Nelson-Barr dark matter from time-dependent nuclear decay

Many experiments have notably reported anomalies in radioactive decay rates with periodic variations and challenged the traditional belief of time-independent decays. This periodicity could potentially be explained by the presence of a periodic dark matter (DM) candidate. In this work, we investigate the impact of time-dependent nuclear decay rates on the ultralight scalar DM in Nelson-Barr solution to the strong CP problem. The light scalar DM field in this framework induces periodic modulations of both CKM matrix elements and quark mass parameters. These modulations generate corresponding periodic perturbations in the neutron-proton mass difference and nuclear binding energies. Consequently, nuclear decay rates receive oscillating residuals. By analyzing the tritium decay rate, we derive new exclusion bounds on the Nelson-Barr DM parameters.

hep-ph↗

arXiv2025

Rethinking Model Redundancy for Low-light Image Enhancement

Low-light image enhancement (LLIE) is a fundamental task in computational photography, aiming to improve illumination, reduce noise, and enhance the image quality of low-light images. While recent advancements primarily focus on customizing complex neural network models, we have observed significant redundancy in these models, limiting further performance improvement. In this paper, we investigate and rethink the model redundancy for LLIE, identifying parameter harmfulness and parameter uselessness. Inspired by the rethinking, we propose two innovative techniques to mitigate model redundancy while improving the LLIE performance: Attention Dynamic Reallocation (ADR) and Parameter Orthogonal Generation (POG). ADR dynamically reallocates appropriate attention based on original attention, thereby mitigating parameter harmfulness. POG learns orthogonal basis embeddings of parameters and prevents degradation to static parameters, thereby mitigating parameter uselessness. Experiments validate the effectiveness of our techniques. We will release the code to the public.

cs.CV↗

arXiv2025

PRELUDE: A Benchmark Designed to Require Global Comprehension and Reasoning over Long Contexts

We introduce PRELUDE, a benchmark for evaluating long-context understanding through the task of determining whether a character's prequel story is consistent with the canonical narrative of the original book. Our task poses a stronger demand for global comprehension and deep reasoning than existing benchmarks -- as the prequels are not part of the original story, assessing their plausibility typically requires searching and integrating information that is only indirectly related. Empirically, 88% of instances require evidence from multiple parts of the narrative. Experimental results highlight the challenge of our task: in-context learning, RAG and in-domain training with state-of-the-art LLMs, and commercial DeepResearch services, lag behind humans by >15%. A further human study reveals that models often produce correct answers with flawed reasoning, leading to an over 30% gap in reasoning accuracy compared to humans. These findings underscore the substantial room for improvement in long-context understanding and reasoning.

cs.CL↗