OpenRaiser/Intern-Atlas huggingface dataset:
ELEC: Efficient Large Language Model-Empowered Click-Through Rate Prediction
Venue: arXiv cs.IR 2025
Year: 2025

Click-through rate (CTR) prediction plays an important role in online advertising systems. On the one hand, traditional CTR prediction models capture the collaborative signals in tabular data via feature interaction modeling, but they lose semantics in text. On the other hand, Large Language Models (LLMs) excel in understanding the context and meaning behind text, but they face challenges in capturing collaborative signals and they have long inference latency. In this paper, we aim to leverage the benefits of both types of models and pursue collaboration, semantics and efficiency. We present ELEC, which is an Efficient LLM-Empowered CTR prediction framework. We first adapt an LLM for the CTR prediction task. In order to leverage the ability of the LLM but simultaneously keep efficiency, we utilize the pseudo-siamese network which contains a gain network and a vanilla network. We inject the high-level representation vector generated by the LLM into a collaborative CTR model to form the gain network such that it can take advantage of both tabular modeling and textual modeling. However, its reliance on the LLM limits its efficiency. We then distill the knowledge from the gain network to the vanilla network on both the score level and the representation level, such that the vanilla network takes only tabular data as input, but can still generate comparable performance as the gain network. Our approach is model-agnostic. It allows for the integration with various existing LLMs and collaborative CTR models. Experiments on real-world datasets demonstrate the effectiveness and efficiency of ELEC for CTR prediction.
FlexEmu: Towards Flexible MCU Peripheral Emulation (Extended Version)
Venue: arXiv cs.CR 2025
Year: 2025

Microcontroller units (MCUs) are widely used in embedded devices due to their low power consumption and cost-effectiveness. MCU firmware controls these devices and is vital to the security of embedded systems. However, performing dynamic security analyses for MCU firmware has remained challenging due to the lack of usable execution environments -- existing dynamic analyses cannot run on physical devices (e.g., insufficient computational resources), while building emulators is costly due to the massive amount of heterogeneous hardware, especially peripherals. Our work is based on the insight that MCU peripherals can be modeled in a two-fold manner. At the structural level, peripherals have diverse implementations but we can use a limited set of primitives to abstract peripherals because their hardware implementations are based on common hardware concepts. At the semantic level, peripherals have diverse functionalities. However, we can use a single unified semantic model to describe the same kind of peripherals because they exhibit similar functionalities. Building on this, we propose FlexEmu, a flexible MCU peripheral emulation framework. Once semantic models are created, FlexEmu automatically extracts peripheral-specific details to instantiate models and generate emulators accordingly. We have successfully applied FlexEmu to model 12 kinds of MCU peripherals. Our evaluation on 90 firmware samples across 15 different MCU platforms shows that the automatically generated emulators can faithfully replicate hardware behaviors and achieve a 98.48% unit test passing rate, outperforming state-of-the-art approaches. To demonstrate the implications of FlexEmu on firmware security, we use the generated emulators to fuzz three popular RTOSes and uncover 10 previously unknown bugs.
Generalized eigenvalue stabilization for immersed explicit dynamics
Venue: arXiv cs.CE 2025
Year: 2025

Explicit time integration for immersed finite element discretizations severely suffers from the influence of poorly cut elements. In this contribution, we propose a generalized eigenvalue stabilization (GEVS) strategy for the element mass matrices of cut elements to cure their adverse impact on the critical time step size of the global system. We use spectral basis functions, specifically $C^0$ continuous Lagrangian interpolation polynomials defined on Gauss-Lobatto-Legendre (GLL) points, which, in combination with its associated GLL quadrature rule, yield high-order convergent diagonal mass matrices for uncut elements. Moreover, considering cut elements, we combine the proposed GEVS approach with the finite cell method (FCM) to guarantee definiteness of the system matrices. However, the proposed GEVS stabilization can directly be applied to other immersed boundary finite element methods. Numerical experiments demonstrate that the stabilization strategy achieves optimal convergence rates and recovers critical time step sizes of equivalent boundary-conforming discretizations. This also holds in the presence of weakly enforced Dirichlet boundary conditions using either Nitsche's method or penalty formulations.
Embedded Off-Switches for AI Compute
Venue: arXiv cs.CR 2025
Year: 2025

To address the risks of increasingly capable AI systems, we introduce a hardware-level off-switch that embeds thousands of independent "security blocks" in each AI accelerator. This massively redundant architecture is designed to prevent unauthorized chip use, even against sophisticated physical attacks. Our main security block design uses public key cryptography to check the authenticity of authorization licenses, and randomly generated nonces to prevent replay attacks. We evaluate attack vectors and present additional security block variants that could be added for greater robustness. Security blocks can be built with standard circuit components, ensuring compatibility with existing semiconductor manufacturing processes. With embedded security blocks, the next generation of AI accelerators could be more robustly defended against dangerous misuse.
FUnc-SNE: A flexible, Fast, and Unconstrained algorithm for neighbour embeddings
Venue: arXiv cs.LG 2025
Year: 2025

Neighbour embeddings (NE) allow the representation of high dimensional datasets into lower dimensional spaces and are often used in data visualisation. In practice, accelerated approximations are employed to handle very large datasets. Accelerating NE is challenging, and two main directions have been explored: very coarse approximations based on negative sampling (as in UMAP) achieve high effective speed but may lack quality in the extracted structures; less coarse approximations, as used in FIt-SNE or BH-t-SNE, offer better structure preservation at the cost of speed, while also restricting the target dimensionality to 2 or 3, limiting NE to visualisation. In some variants, the precision of these costlier accelerations also enables finer-grained control on the extracted structures through dedicated hyperparameters. This paper proposes to bridge the gab between both approaches by introducing a novel way to accelerate NE, requiring a small number of computations per iteration while maintaining good fine-grained structure preservation and flexibility through hyperparameter tuning, without limiting the dimensionality of the embedding space. The method was designed for interactive exploration of data; as such, it abandons the traditional two-phased approach of other NE methods, allowing instantaneous visual feedback when changing hyperparameters, even when these control processes happening on the high-dimensional side of the computations. Experiments using a publicly available, GPU accelerated GUI integration of the method show promising results in terms of speed, flexibility in the structures getting extracted, and show potential uses in broader machine learning contexts with minimal algorithmic modifications. Central to this algorithm is a novel approach to iterative approximate nearest neighbour search, which shows promising results compared to nearest neighbour descent.
IBN: An Interpretable Bidirectional-Modeling Network for Multivariate Time Series Forecasting with Variable Missing
Venue: arXiv cs.LG 2025
Year: 2025

Multivariate time series forecasting (MTSF) often faces challenges from missing variables, which hinder conventional spatial-temporal graph neural networks in modeling inter-variable correlations. While GinAR addresses variable missing using attention-based imputation and adaptive graph learning for the first time, it lacks interpretability and fails to capture more latent temporal patterns due to its simple recursive units (RUs). To overcome these limitations, we propose the Interpretable Bidirectional-modeling Network (IBN), integrating Uncertainty-Aware Interpolation (UAI) and Gaussian kernel-based Graph Convolution (GGCN). IBN estimates the uncertainty of reconstructed values using MC Dropout and applies an uncertainty-weighted strategy to mitigate high-risk reconstructions. GGCN explicitly models spatial correlations among variables, while a bidirectional RU enhances temporal dependency modeling. Extensive experiments show that IBN achieves state-of-the-art forecasting performance under various missing-rate scenarios, providing a more reliable and interpretable framework for MTSF with missing variables. Code is available at: https://github.com/zhangth1211/NICLab-IBN.
What's Coming Next? Short-Term Simulation of Business Processes from Current State
Venue: arXiv cs.SE 2025
Year: 2025

Business process simulation is an approach to evaluate business process changes prior to implementation. Existing methods in this field primarily support tactical decision-making, where simulations start from an empty state and aim to estimate the long-term effects of process changes. A complementary use-case is operational decision-making, where the goal is to forecast short-term performance based on ongoing cases and to analyze the impact of temporary disruptions, such as demand spikes and shortfalls in available resources. An approach to tackle this use-case is to run a long-term simulation up to a point where the workload is similar to the current one (warm-up), and measure performance thereon. However, this approach does not consider the current state of ongoing cases and resources in the process. This paper studies an alternative approach that initializes the simulation from a representation of the current state derived from an event log of ongoing cases. The paper addresses two challenges in operationalizing this approach: (1) Given a simulation model, what information is needed so that a simulation run can start from the current state of cases and resources? (2) How can the current state of a process be derived from an event log? The resulting short-term simulation approach is embodied in a simulation engine that takes as input a simulation model and a log of ongoing cases, and simulates cases for a given time horizon. An experimental evaluation shows that this approach yields more accurate short-term performance forecasts than long-term simulations with warm-up period, particularly in the presence of concept drift or bursty performance patterns.
Empirical Security Analysis of Software-based Fault Isolation through Controlled Fault Injection
Venue: arXiv cs.CR 2025
Year: 2025

We use browsers daily to access all sorts of information. Because browsers routinely process scripts, media, and executable code from unknown sources, they form a critical security boundary between users and adversaries. A common attack vector is JavaScript, which exposes a large attack surface due to the sheer complexity of modern JavaScript engines. To mitigate these threats, modern engines increasingly adopt software-based fault isolation (SFI). A prominent example is Google's V8 heap sandbox, which represents the most widely deployed SFI mechanism, protecting billions of users across all Chromium-based browsers and countless applications built on Node$.$js and Electron. The heap sandbox splits the address space into two parts: one part containing trusted, security-sensitive metadata, and a sandboxed heap containing memory accessible to untrusted code. On a technical level, the sandbox enforces isolation by removing raw pointers and using translation tables to resolve references to trusted objects. Consequently, an attacker cannot corrupt trusted data even with full control of the sandboxed data, unless there is a bug in how code handles data from the sandboxed heap. Despite their widespread use, such SFI mechanisms have seen little security testing. In this work, we propose a new testing technique that models the security boundary of modern SFI implementations. Following the SFI threat model, we assume a powerful attacker who fully controls the sandbox's memory. We implement this by instrumenting memory loads originating in the trusted domain and accessing untrusted, attacker-controlled sandbox memory. We then inject faults into the loaded data, aiming to trigger memory corruption in the trusted domain. In a comprehensive evaluation, we identify 19 security bugs in V8 that enable an attacker to bypass the sandbox.
A Survey of Long-Document Retrieval in the PLM and LLM Era
Venue: arXiv cs.IR 2025
Year: 2025

The proliferation of long-form documents presents a fundamental challenge to information retrieval (IR), as their length, dispersed evidence, and complex structures demand specialized methods beyond standard passage-level techniques. This survey provides the first comprehensive treatment of long-document retrieval (LDR), consolidating methods, challenges, and applications across three major eras. We systematize the evolution from classical lexical and early neural models to modern pre-trained (PLM) and large language models (LLMs), covering key paradigms like passage aggregation, hierarchical encoding, efficient attention, and the latest LLM-driven re-ranking and retrieval techniques. Beyond the models, we review domain-specific applications, specialized evaluation resources, and outline critical open challenges such as efficiency trade-offs, multimodal alignment, and faithfulness. This survey aims to provide both a consolidated reference and a forward-looking agenda for advancing long-document retrieval in the era of foundation models.
AgentSentinel: An End-to-End and Real-Time Security Defense Framework for Computer-Use Agents
Venue: arXiv cs.CR 2025
Year: 2025

Large Language Models (LLMs) have been increasingly integrated into computer-use agents, which can autonomously operate tools on a user's computer to accomplish complex tasks. However, due to the inherently unstable and unpredictable nature of LLM outputs, they may issue unintended tool commands or incorrect inputs, leading to potentially harmful operations. Unlike traditional security risks stemming from insecure user prompts, tool execution results from LLM-driven decisions introduce new and unique security challenges. These vulnerabilities span across all components of a computer-use agent. To mitigate these risks, we propose AgentSentinel, an end-to-end, real-time defense framework designed to mitigate potential security threats on a user's computer. AgentSentinel intercepts all sensitive operations within agent-related services and halts execution until a comprehensive security audit is completed. Our security auditing mechanism introduces a novel inspection process that correlates the current task context with system traces generated during task execution. To thoroughly evaluate AgentSentinel, we present BadComputerUse, a benchmark consisting of 60 diverse attack scenarios across six attack categories. The benchmark demonstrates a 87% average attack success rate on four state-of-the-art LLMs. Our evaluation shows that AgentSentinel achieves an average defense success rate of 79.6%, significantly outperforming all baseline defenses.
Quantum Computing for Large-scale Network Optimization: Opportunities and Challenges
Venue: arXiv cs.NI 2025
Year: 2025

The complexity of large-scale 6G-and-beyond networks demands innovative approaches for multi-objective optimization over vast search spaces, a task often intractable. Quantum computing (QC) emerges as a promising technology for efficient large-scale optimization. We present our vision of leveraging QC to tackle key classes of problems in future mobile networks. By analyzing and identifying common features, particularly their graph-centric representation, we propose a unified strategy involving QC algorithms. Specifically, we outline a methodology for optimization using quantum annealing as well as quantum reinforcement learning. Additionally, we discuss the main challenges that QC algorithms and hardware must overcome to effectively optimize future networks.
Filtered Approximate Nearest Neighbor Search: A Unified Benchmark and Systematic Experimental Study [Experiment, Analysis & Benchmark]
Venue: arXiv cs.DB 2025
Year: 2025

For a given dataset $\mathcal{D}$ and structured label $f$, the goal of Filtered Approximate Nearest Neighbor Search (FANNS) algorithms is to find top-$k$ points closest to a query that satisfy label constraints, while ensuring both recall and QPS (Queries Per Second). In recent years, many FANNS algorithms have been proposed. However, the lack of a systematic investigation makes it difficult to understand their relative strengths and weaknesses. Additionally, we found that: (1) FANNS algorithms have coupled, dataset-dependent parameters, leading to biased comparisons. (2) Key impact factors are rarely analyzed systematically, leaving unclear when each algorithm performs well. (3) Disparate datasets, workloads, and biased experiment designs make cross-algorithm comparisons unreliable. Thus, a comprehensive survey and benchmark for FANNS is crucial to achieve the following goals: designing a fair evaluation and clarifying the classification of algorithms, conducting in-depth analysis of their performance, and establishing a unified benchmark. First, we propose a taxonomy (dividing methods into \textit{filter-then-search}, \textit{search-then-filter}, \textit{hybrid-search}) and a systematic evaluation framework, integrating unified parameter tuning and standardized filtering across algorithms to reduce implementation-induced performance variations and reflect core trade-offs. Then, we conduct a comprehensive empirical study to analyze how query difficulty and dataset properties impact performance, evaluating robustness under pressures like filter selectivity, Recall@k, and scalability to clarify each method's strengths. Finally, we establish a standardized benchmark with real-world datasets and open-source related resources to ensure reproducible future research.
Query Expansion in the Age of Pre-trained and Large Language Models: A Comprehensive Survey
Venue: arXiv cs.IR 2025
Year: 2025

Modern information retrieval (IR) must reconcile short, ambiguous queries with increasingly diverse and dynamic corpora. Query expansion (QE) remains central to alleviating vocabulary mismatch, yet the design space has shifted with pre-trained and large language models (PLMs, LLMs). In this survey, we organize recent work along four complementary dimensions: the point of injection (implicit/embedding vs. selection-based explicit), grounding and interaction (from zero-grounding prompts to multi-round retrieve-expand loops), learning and alignment (SFT/PEFT/DPO), and knowledge-graph integration. A model-centric taxonomy is also outlined, spanning encoder-only, encoder-decoder, decoder-only, instruction-tuned, and domain or multilingual variants, with affordances for QE such as contextual disambiguation, controllable generation, and zero-shot or few-shot reasoning. Practice-oriented guidance specifies where neural QE helps most: first-stage retrieval, multi-query fusion, re-ranking, and retrieval-augmented generation (RAG). The survey compares traditional and neural QE across seven aspects and maps applications in web search, biomedicine, e-commerce, open-domain question answering/RAG, conversational and code search, and cross-lingual settings. The survey concludes with an agenda focused on reliable, safe, efficient, and adaptive QE, offering a principled blueprint for deploying and combining techniques under real-world constraints.
RAQ-MIMO: MIMO for Multi-Band Rydberg Atomic Quantum Receiver
Venue: arXiv cs.IT 2025
Year: 2025

Rydberg atomic quantum receivers (RAQRs) are capable of receiving multi-band radio-frequency (RF) signals simultaneously, which are expected to break Chu's limit for classical electronic antennas. However, signals from different users will interfere with each other in the optical intermediate frequency (IF) domain of the multi-band quantum receiver, which is termed the IF interference (IFI) problem. To address this problem, in this paper, we propose a multi-input multi-output (MIMO) architecture for Rydberg atomic quantum receiver (RAQ-MIMO) by exploiting the additional spatial diversity of MIMO receivers. Specifically, by applying the dynamic signal model of RAQRs, we clarify the physical relationship between the quantum local oscillator (LO) configurations and the multi-band gains with the concept of quantum transconductance. Then, with the quantum transconductance-based signal model, we formulate the spectral efficiency (SE) maximization problem and further propose the quantum weighted minimum mean square error (qWMMSE) algorithm, which jointly optimizes the quantum LO configurations and the classical precoder/combiner matrices. Furthermore, we test the qWMMSE algorithm within the standard space division multiple access (SDMA) scheme and the frequency division multiple access (FDMA) scheme. Simulation results demonstrate that the qWMMSE optimization framework can significantly improve the SE of RAQ-MIMO systems for both multiple access schemes, and that RAQ-MIMO systems can outperform classical electronic receiver-based multi-user MIMO systems by eliminating the mutual coupling effect between classical antennas.
"We provide our resources in a dedicated repository": Surveying the Transparency of HICSS publications
Venue: arXiv cs.SE 2025
Year: 2025

Every day, new discoveries are made by researchers from all across the globe and fields. HICSS is a flagship venue to present and discuss such scientific advances. Yet, the activities carried out for any given research can hardly be fully contained in a single document of a few pages-the "paper." Indeed, any given study entails data, artifacts, or other material that is crucial to truly appreciate the contributions claimed in the corresponding paper. External repositories (e.g., GitHub) are a convenient tool to store all such resources so that future work can freely observe and build upon them -- thereby improving transparency and promoting reproducibility of research as a whole. In this work, we scrutinize the extent to which papers recently accepted to HICSS leverage such repositories to provide supplementary material. To this end, we collect all the 5579 papers included in HICSS proceedings from 2017-2024. Then, we identify those entailing either human subject research (850) or technical implementations (737), or both (147). Finally, we review their text, examining how many include a link to an external repository-and, inspect its contents. Overall, out of 2028 papers, only 3\% have a functional and publicly available repository that is usable by downstream research. We release all our tools.