Tootfinder

Towards AI Search Paradigm
Yuchen Li, Hengyi Cai, Rui Kong, Xinran Chen, Jiamin Chen, Jun Yang, Haojie Zhang, Jiayi Li, Jiayi Wu, Yiqun Chen, Changle Qu, Keyi Kong, Wenwen Ye, Lixin Su, Xinyu Ma, Long Xia, Daiting Shi, Jiashu Zhao, Haoyi Xiong, Shuaiqiang Wang, Dawei Yin
https://arxiv.org/abs/2506.17188

Towards AI Search Paradigm
In this paper, we introduce the AI Search Paradigm, a comprehensive blueprint for next-generation search systems capable of emulating human information processing and decision-making. The paradigm employs a modular architecture of four LLM-powered agents (Master, Planner, Executor and Writer) that dynamically adapt to the full spectrum of information needs, from simple factual queries to complex multi-stage reasoning tasks. These agents collaborate dynamically through coordinated workflows to e…

LLM-Enhanced Reranking for Complementary Product Recommendation
Zekun Xu, Yudi Zhang
https://arxiv.org/abs/2507.16237 https://arxiv.o…

LLM-Enhanced Reranking for Complementary Product Recommendation
Complementary product recommendation, which aims to suggest items that are used together to enhance customer value, is a crucial yet challenging task in e-commerce. While existing graph neural network (GNN) approaches have made significant progress in capturing complex product relationships, they often struggle with the accuracy-diversity tradeoff, particularly for long-tail items. This paper introduces a model-agnostic approach that leverages Large Language Models (LLMs) to enhance the reranki…

LVPNet: A Latent-variable-based Prediction-driven End-to-end Framework for Lossless Compression of Medical Images
Chenyue Song, Chen Hui, Qing Lin, Wei Zhang, Siqiao Li, Shengping Zhang, Haiqi Zhu, Zhixuan Li, Shaohui Liu, Feng Jiang, Xiang Li
https://arxiv.org/abs/2506.17983

LVPNet: A Latent-variable-based Prediction-driven End-to-end Framework for Lossless Compression of Medical Images
Autoregressive Initial Bits is a framework that integrates sub-image autoregression and latent variable modeling, demonstrating its advantages in lossless medical image compression. However, in existing methods, the image segmentation process leads to an even distribution of latent variable information across each sub-image, which in turn causes posterior collapse and inefficient utilization of latent variables. To deal with these issues, we propose a prediction-based end-to-end lossless medica…

Fragmented quantum phases in anti-blockade regime of Rydberg atom array
Han-Chao Chen, Zheng-Yuan Zhang, Meng Zhou, Xin Liu, Li-Hua Zhang, Bang Liu, Lu-Xia Wang, Dong-Sheng Ding, Bao-Sen Shi
https://arxiv.org/abs/2507.17488

Fragmented quantum phases in anti-blockade regime of Rydberg atom array
In this Letter, we report a parameter-dependent Hilbert space fragmentation in a one-dimensional Rydberg atom array under anti-blockade conditions. We identify distinct non-equilibrium dynamical phases and show that their quasi-periodic behavior arises from the interplay of multi-path interference and multi-photon cascaded excitations, reflecting fundamental differences in state connectivity and effective dimensionality. By constructing a complete phase diagram, we reveal the transition from th…

Predictive Hydrodynamic Simulations for Laser Direct-drive Implosion Experiments via Artificial Intelligence
Zixu Wang, Yuhan Wang, Junfei Ma, Fuyuan Wu, Junchi Yan, Xiaohui Yuan, Zhe Zhang, Jie Zhang
https://arxiv.org/abs/2507.16227

Predictive Hydrodynamic Simulations for Laser Direct-drive Implosion Experiments via Artificial Intelligence
This work presents predictive hydrodynamic simulations empowered by artificial intelligence (AI) for laser driven implosion experiments, taking the double-cone ignition (DCI) scheme as an example. A Transformer-based deep learning model MULTI-Net is established to predict implosion features according to laser waveforms and target radius. A Physics-Informed Decoder (PID) is proposed for high-dimensional sampling, significantly reducing the prediction errors compared to Latin hypercube sampling. …

VFArch\=e: A Dual-Mode Framework for Locating Vulnerable Functions in Open-Source Software
Lyuye Zhang, Jian Zhang, Kaixuan Li, Chong Wang, Chengwei Liu, Jiahui Wu, Sen Chen, Yaowen Zheng, Yang Liu
https://arxiv.org/abs/2506.18050

VFArchē: A Dual-Mode Framework for Locating Vulnerable Functions in Open-Source Software
Software Composition Analysis (SCA) has become pivotal in addressing vulnerabilities inherent in software project dependencies. In particular, reachability analysis is increasingly used in Open-Source Software (OSS) projects to identify reachable vulnerabilities (e.g., CVEs) through call graphs, enabling a focus on exploitable risks. Performing reachability analysis typically requires the vulnerable function (VF) to track the call chains from downstream applications. However, such crucial infor…

End-to-end reconstruction of ultra-high energy particle observables from radio detection of extensive air showers
Kewen Zhang, Duan Kaikai, Ramesh Koirala, Mat\'ias Tueros, Chao Zhang, Yi Zhang
https://arxiv.org/abs/2507.17266

End-to-end reconstruction of ultra-high energy particle observables from radio detection of extensive air showers
The radio detection of very inclined air showers offers a promising avenue for studying ultra-high-energy cosmic rays (UHECRs) and neutrinos. Accurate reconstruction methods are essential for investigating the properties of primary particles. Recently, we developed an analytical least-squares method to reconstruct the electric field using three polarization components. The reconstruction yields no bias, with a 68\% confidence interval of [-0.02, 0.02], and a standard deviation of 0.04. Using th…

Towards Efficient Privacy-Preserving Machine Learning: A Systematic Review from Protocol, Model, and System Perspectives
Wenxuan Zeng, Tianshi Xu, Yi Chen, Yifan Zhou, Mingzhe Zhang, Jin Tan, Cheng Hong, Meng Li
https://arxiv.org/abs/2507.14519

Towards Efficient Privacy-Preserving Machine Learning: A Systematic Review from Protocol, Model, and System Perspectives
Privacy-preserving machine learning (PPML) based on cryptographic protocols has emerged as a promising paradigm to protect user data privacy in cloud-based machine learning services. While it achieves formal privacy protection, PPML often incurs significant efficiency and scalability costs due to orders of magnitude overhead compared to the plaintext counterpart. Therefore, there has been a considerable focus on mitigating the efficiency gap for PPML. In this survey, we provide a comprehensive …

Quantifying Holistic Review: A Multi-Modal Approach to College Admissions Prediction
Jun-Wei Zeng, Jerry Shen
https://arxiv.org/abs/2507.15862 https://

Quantifying Holistic Review: A Multi-Modal Approach to College Admissions Prediction
This paper introduces the Comprehensive Applicant Profile Score (CAPS), a novel multi-modal framework designed to quantitatively model and interpret holistic college admissions evaluations. CAPS decomposes applicant profiles into three interpretable components: academic performance (Standardized Academic Score, SAS), essay quality (Essay Quality Index, EQI), and extracurricular engagement (Extracurricular Impact Score, EIS). Leveraging transformer-based semantic embeddings, LLM scoring, and XGB…

No-go theorems for logical gates on product quantum codes
Xiaozhen Fu, Han Zheng, Zimu Li, Zi-Wen Liu
https://arxiv.org/abs/2507.16797 https://

No-go theorems for logical gates on product quantum codes
Quantum error-correcting codes are essential to the implementation of fault-tolerant quantum computation. Homological products of classical codes offer a versatile framework for constructing quantum error-correcting codes with desirable properties, especially quantum low-density parity check (qLDPC) codes. Based on extensions of the Bravyi--König theorem that encompass codes without geometric locality, we establish a series of general no-go theorems for fault-tolerant logical gates supported b…