OpenRaiser/Intern-Atlas huggingface dataset:
Real-world Edge Neural Network Implementations Leak Private Interactions Through Physical Side Channel
Venue: arXiv cs.CR 2025
Year: 2025

Neural networks have become a fundamental component of numerous practical applications, and their implementations, which are often accelerated by hardware, are integrated into all types of real-world physical devices. User interactions with neural networks on hardware accelerators are commonly considered privacy-sensitive. Substantial efforts have been made to uncover vulnerabilities and enhance privacy protection at the level of machine learning algorithms, including membership inference attacks, differential privacy, and federated learning. However, neural networks are ultimately implemented and deployed on physical devices, and current research pays comparatively less attention to privacy protection at the implementation level. In this paper, we introduce a generic physical side-channel attack, ScaAR, that extracts user interactions with neural networks by leveraging electromagnetic (EM) emissions of physical devices. Our proposed attack is implementation-agnostic, meaning it does not require the adversary to possess detailed knowledge of the hardware or software implementations, thanks to the capabilities of deep learning-based side-channel analysis (DLSCA). Experimental results demonstrate that, through the EM side channel, ScaAR can effectively extract the class label of user interactions with neural classifiers, including inputs and outputs, on the AMD-Xilinx MPSoC ZCU104 FPGA and Raspberry Pi 3 B. In addition, for the first time, we provide side-channel analysis on edge Large Language Model (LLM) implementations on the Raspberry Pi 5, showing that EM side channel leaks interaction data, and different LLM tokens can be distinguishable from the EM traces.
Idiom Detection in Sorani Kurdish Texts
Venue: arXiv cs.CL 2025
Year: 2025

Idiom detection using Natural Language Processing (NLP) is the computerized process of recognizing figurative expressions within a text that convey meanings beyond the literal interpretation of the words. While idiom detection has seen significant progress across various languages, the Kurdish language faces a considerable research gap in this area despite the importance of idioms in tasks like machine translation and sentiment analysis. This study addresses idiom detection in Sorani Kurdish by approaching it as a text classification task using deep learning techniques. To tackle this, we developed a dataset containing 10,580 sentences embedding 101 Sorani Kurdish idioms across diverse contexts. Using this dataset, we developed and evaluated three deep learning models: KuBERT-based transformer sequence classification, a Recurrent Convolutional Neural Network (RCNN), and a BiLSTM model with an attention mechanism. The evaluations revealed that the transformer model, the fine-tuned BERT, consistently outperformed the others, achieving nearly 99% accuracy while the RCNN achieved 96.5% and the BiLSTM 80%. These results highlight the effectiveness of Transformer-based architectures in low-resource languages like Kurdish. This research provides a dataset, three optimized models, and insights into idiom detection, laying a foundation for advancing Kurdish NLP.
Reducing Action Space for Deep Reinforcement Learning via Causal Effect Estimation
Venue: arXiv cs.LG 2025
Year: 2025

Intelligent decision-making within large and redundant action spaces remains challenging in deep reinforcement learning. Considering similar but ineffective actions at each step can lead to repetitive and unproductive trials. Existing methods attempt to improve agent exploration by reducing or penalizing redundant actions, yet they fail to provide quantitative and reliable evidence to determine redundancy. In this paper, we propose a method to improve exploration efficiency by estimating the causal effects of actions. Unlike prior methods, our approach offers quantitative results regarding the causality of actions for one-step transitions. We first pre-train an inverse dynamics model to serve as prior knowledge of the environment. Subsequently, we classify actions across the entire action space at each time step and estimate the causal effect of each action to suppress redundant actions during exploration. We provide a theoretical analysis to demonstrate the effectiveness of our method and present empirical results from simulations in environments with redundant actions to evaluate its performance. Our implementation is available at https://github.com/agi-brain/cee.git.
Exploring Answer Set Programming for Provenance Graph-Based Cyber Threat Detection: A Novel Approach
Venue: arXiv cs.CR 2025
Year: 2025

Provenance graphs are useful and powerful tools for representing system-level activities in cybersecurity; however, existing approaches often struggle with complex queries and flexible reasoning. This paper presents a novel approach using Answer Set Programming (ASP) to model and analyze provenance graphs. We introduce an ASP-based representation that captures intricate relationships between system entities, including temporal and causal dependencies. Our model enables sophisticated analysis capabilities such as attack path tracing, data exfiltration detection, and anomaly identification. The declarative nature of ASP allows for concise expression of complex security patterns and policies, facilitating both real-time threat detection and forensic analysis. We demonstrate our approach's effectiveness through case studies showcasing its threat detection capabilities. Experimental results illustrate the model's ability to handle large-scale provenance graphs while providing expressive querying. The model's extensibility allows for incorporation of new system behaviors and security rules, adapting to evolving cyber threats. This work contributes a powerful, flexible, and explainable framework for reasoning about system behaviors and security incidents, advancing the development of effective threat detection and forensic investigation tools.
Accelerating Protocol Synthesis and Detecting Unrealizability with Interpretation Reduction
Venue: arXiv cs.LO 2025
Year: 2025

We present a novel counterexample-guided, sketch-based method for the synthesis of symbolic distributed protocols in TLA+. Our method's chief novelty lies in a new search space reduction technique called interpretation reduction, which allows to not only eliminate incorrect candidate protocols before they are sent to the verifier, but also to avoid enumerating redundant candidates in the first place. Further performance improvements are achieved by an advanced technique for exact generalization of counterexamples. Experiments on a set of established benchmarks show that our tool is almost always faster than the state of the art, often by orders of magnitude, and was also able to synthesize an entire TLA+ protocol "from scratch" in less than 3 minutes where the state of the art timed out after an hour. Our method is sound, complete, and guaranteed to terminate on unrealizable synthesis instances under common assumptions which hold in all our benchmarks.
Data Assetization via Resources-decoupled Federated Learning
Venue: arXiv cs.LG 2025
Year: 2025

With the development of the digital economy, data is increasingly recognized as an essential resource for both work and life. However, due to privacy concerns, data owners tend to maximize the value of data through the circulation of information rather than direct data transfer. Federated learning (FL) provides an effective approach to collaborative training models while preserving privacy. However, as model parameters and training data grow, there are not only real differences in data resources between different data owners, but also mismatches between data and computing resources. These challenges lead to inadequate collaboration among data owners, compute centers, and model owners, reducing the global utility of the three parties and the effectiveness of data assetization. In this work, we first propose a framework for resource-decoupled FL involving three parties. Then, we design a Tripartite Stackelberg Model and theoretically analyze the Stackelberg-Nash equilibrium (SNE) for participants to optimize global utility. Next, we propose the Quality-aware Dynamic Resources-decoupled FL algorithm (QD-RDFL), in which we derive and solve the optimal strategies of all parties to achieve SNE using backward induction. We also design a dynamic optimization mechanism to improve the optimal strategy profile by evaluating the contribution of data quality from data owners to the global model during real training. Finally, our extensive experiments demonstrate that our method effectively encourages the linkage of the three parties involved, maximizing the global utility and value of data assets.
Data-NoMAD: A Tool for Boosting Confidence in the Integrity of Social Science Survey Data
Venue: arXiv cs.CR 2025
Year: 2025

To safeguard against data fabrication and enhance trust in quantitative social science, we present Data Non-Manipulation Authentication Digest (Data-NoMAD). Data-NoMAD is a tool that allows researchers to certify, and others to verify, that a dataset has not been inappropriately manipulated between the point of data collection and the point at which a replication archive is made publicly available. Data-NoMAD creates and stores a column hash digest of a raw dataset upon initial download from a survey platform (the current version works with Qualtrics and SurveyCTO), but before it is subject to appropriate manipulations such as anonymity-preserving redactions. Data-NoMAD can later be used to verify the integrity of a publicly archived dataset by identifying columns that have been deleted, added, or altered. Data-NoMAD complements existing efforts at ensuring research integrity and integrates seamlessly with extant replication practices.
MedAgentBench: A Realistic Virtual EHR Environment to Benchmark Medical LLM Agents
Venue: arXiv cs.LG 2025
Year: 2025

Recent large language models (LLMs) have demonstrated significant advancements, particularly in their ability to serve as agents thereby surpassing their traditional role as chatbots. These agents can leverage their planning and tool utilization capabilities to address tasks specified at a high level. However, a standardized dataset to benchmark the agent capabilities of LLMs in medical applications is currently lacking, making the evaluation of LLMs on complex tasks in interactive healthcare environments challenging. To address this gap, we introduce MedAgentBench, a broad evaluation suite designed to assess the agent capabilities of large language models within medical records contexts. MedAgentBench encompasses 300 patient-specific clinically-derived tasks from 10 categories written by human physicians, realistic profiles of 100 patients with over 700,000 data elements, a FHIR-compliant interactive environment, and an accompanying codebase. The environment uses the standard APIs and communication infrastructure used in modern EMR systems, so it can be easily migrated into live EMR systems. MedAgentBench presents an unsaturated agent-oriented benchmark that current state-of-the-art LLMs exhibit some ability to succeed at. The best model (Claude 3.5 Sonnet v2) achieves a success rate of 69.67%. However, there is still substantial space for improvement which gives the community a next direction to optimize. Furthermore, there is significant variation in performance across task categories. MedAgentBench establishes this and is publicly available at https://github.com/stanfordmlgroup/MedAgentBench , offering a valuable framework for model developers to track progress and drive continuous improvements in the agent capabilities of large language models within the medical domain.
A Predictive Approach for Enhancing Accuracy in Remote Robotic Surgery Using Informer Model
Venue: arXiv cs.RO 2025
Year: 2025

Precise and real-time estimation of the robotic arm's position on the patient's side is essential for the success of remote robotic surgery in Tactile Internet (TI) environments. This paper presents a prediction model based on the Transformer-based Informer framework for accurate and efficient position estimation. Additionally, it combines a Four-State Hidden Markov Model (4-State HMM) to simulate realistic packet loss scenarios. The proposed approach addresses challenges such as network delays, jitter, and packet loss to ensure reliable and precise operation in remote surgical applications. The method integrates the optimization problem into the Informer model by embedding constraints such as energy efficiency, smoothness, and robustness into its training process using a differentiable optimization layer. The Informer framework uses features such as ProbSparse attention, attention distilling, and a generative-style decoder to focus on position-critical features while maintaining a low computational complexity of O(L log L). The method is evaluated using the JIGSAWS dataset, achieving a prediction accuracy of over 90 percent under various network scenarios. A comparison with models such as TCN, RNN, and LSTM demonstrates the Informer framework's superior performance in handling position prediction and meeting real-time requirements, making it suitable for Tactile Internet-enabled robotic surgery.
Comparable Corpora: Opportunities for New Research Directions
Venue: arXiv cs.CL 2025
Year: 2025

Most conference papers present new results, but this paper will focus more on opportunities for the audience to make their own contributions. This paper is intended to challenge the community to think more broadly about what we can do with comparable corpora. We will start with a review of the history, and then suggest new directions for future research. This was a keynote at BUCC-2025, a workshop associated with Coling-2025.
CodeMonkeys: Scaling Test-Time Compute for Software Engineering
Venue: arXiv cs.LG 2025
Year: 2025

Scaling test-time compute is a promising axis for improving LLM capabilities. However, test-time compute can be scaled in a variety of ways, and effectively combining different approaches remains an active area of research. Here, we explore this problem in the context of solving real-world GitHub issues from the SWE-bench dataset. Our system, named CodeMonkeys, allows models to iteratively edit a codebase by jointly generating and running a testing script alongside their draft edit. We sample many of these multi-turn trajectories for every issue to generate a collection of candidate edits. This approach lets us scale "serial" test-time compute by increasing the number of iterations per trajectory and "parallel" test-time compute by increasing the number of trajectories per problem. With parallel scaling, we can amortize up-front costs across multiple downstream samples, allowing us to identify relevant codebase context using the simple method of letting an LLM read every file. In order to select between candidate edits, we combine voting using model-generated tests with a final multi-turn trajectory dedicated to selection. Overall, CodeMonkeys resolves 57.4% of issues from SWE-bench Verified using a budget of approximately 2300 USD. Our selection method can also be used to combine candidates from different sources. Selecting over an ensemble of edits from existing top SWE-bench Verified submissions obtains a score of 66.2% and outperforms the best member of the ensemble on its own. We fully release our code and data at https://scalingintelligence.stanford.edu/pubs/codemonkeys.
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection
Venue: arXiv cs.MM 2025
Year: 2025

While large generative artificial intelligence (GenAI) models have achieved significant success, they also raise growing concerns about online information security due to their potential misuse for generating deceptive content. Out-of-context (OOC) multimodal misinformation detection, which often retrieves Web evidence to identify the repurposing of images in false contexts, faces the issue of reasoning over GenAI-polluted evidence to derive accurate predictions. Existing works simulate GenAI-powered pollution at the claim level with stylistic rewriting to conceal linguistic cues, and ignore evidence-level pollution for such information-seeking applications. In this work, we investigate how polluted evidence affects the performance of existing OOC detectors, revealing a performance degradation of more than 9 percentage points. We propose two strategies, cross-modal evidence reranking and cross-modal claim-evidence reasoning, to address the challenges posed by polluted evidence. Extensive experiments on two benchmark datasets show that these strategies can effectively enhance the robustness of existing out-of-context detectors amidst polluted evidence.
Reproduction Research of FSA-Benchmark
Venue: arXiv cs.DC 2025
Year: 2025

In the current landscape of big data, the reliability and performance of storage systems are essential to the success of various applications and services. as data volumes continue to grow exponentially, the complexity and scale of the storage infrastructures needed to manage this data also increase. a significant challenge faced by data centers and storage systems is the detection and management of fail-slow disks that experience a gradual decline in performance before ultimately failing. Unlike outright disk failures, fail-slow conditions can go undetected for prolonged periods, leading to considerable impacts on system performance and user experience.
DeServe: Towards Affordable Offline LLM Inference via Decentralization
Venue: arXiv cs.DC 2025
Year: 2025

The rapid growth of generative AI and its integration into everyday workflows have significantly increased the demand for large language model (LLM) inference services. While proprietary models remain popular, recent advancements in open-source LLMs have positioned them as strong contenders. However, deploying these models is often constrained by the high costs and limited availability of GPU resources. In response, this paper presents the design of a decentralized offline serving system for LLM inference. Utilizing idle GPU resources, our proposed system, DeServe, decentralizes access to LLMs at a lower cost. DeServe specifically addresses key challenges in optimizing serving throughput in high-latency network environments. Experiments demonstrate that DeServe achieves a 6.7x-12.6x improvement in throughput over existing serving system baselines in such conditions.
Punch Out Model Synthesis: A Stochastic Algorithm for Constraint Based Tiling Generation
Venue: arXiv cs.DC 2025
Year: 2025

As an artistic aid in tiled level design, Constraint Based Tiling Generation (CBTG) algorithms can help to automatically create level realizations from a set of tiles and placement constraints. Merrell's Modify in Blocks Model Synthesis (MMS) and Gumin's Wave Function Collapse (WFC) have been proposed as Constraint Based Tiling Generation (CBTG) algorithms that work well for many scenarios but have limitations in problem size, problem setup and solution biasing. We present Punch Out Model Synthesis (POMS), a Constraint Based Tiling Generation algorithm, that can handle large problem sizes, requires minimal assumptions for setup and can help mitigate solution biasing. POMS attempts to resolve indeterminate grid regions by trying to progressively realize sub-blocks, performing a stochastic boundary erosion on previously resolved regions should sub-block resolution fail. We highlight the results of running a reference implementation on different tile sets and discuss a tile correlation length, implied by the tile constraints, and its role in choosing an appropriate block size to aid POMS in successfully finding grid realizations.
Accelerating Particle-Mesh Algorithms with FPGAs and OmpSs@OpenCL
Venue: arXiv cs.DC 2025
Year: 2025

Due to its flexible architecture, FPGAs support unique, deep hardware pipeline implementations for accelerating HPC applications. However, these devices are quite new in the HPC space, and thus, have been scarcely explored outside some specific scientific domain, such as machine learning or biological sequence alignment. The objective of this thesis is to characterize the FPGA-based solution for accelerating particle-mesh algorithms, in which the force applied to each particle is computed based on the fields deposited in a finite mesh (or grid). Our starting point is a 2D kinetic PIC plasma simulator called ZPIC that has the same core algorithm and functionalities as OSIRIS. To create an efficient hardware design, the program keeps the particles strictly sorted by tiles (a group of cells) and uses the local memory as an explicitly managed cache. We also create multiple copies of the local current buffer to solve dependencies during the deposition phase. The resulting pipeline was replicated multiple times to explore data parallelism and increase its throughput. We then compare our hardware solution against similar implementations on GPU and multicore CPUs, showing promising results in term of power efficiency and performance. Keywords: FPGA, OpenCL, Kinetic Plasma Simulation.
Jump Point Search Pathfinding in 4-connected Grids
Venue: arXiv cs.RO 2025
Year: 2025

This work introduces JPS4, a novel pathfinding algorithm for 4-connected grid maps. JPS4 builds upon the Jump Point Search (JPS8) algorithm, originally designed for 8-connected environments. To achieve efficient pathfinding on 4-connected grids, JPS4 employs a canonical ordering and a successor function that enable online graph pruning. This reduces the search space by minimizing unnecessary node expansions. The core concept of JPS4 as well as JPS8 lies in the utilization of jump points. Strategically placed at obstacle corners, jump points prevent the search from overlooking crucial sections of the state space. They essentially reinitialize the canonical ordering, allowing exploration beyond obstacles. This mechanism ensures JPS4 finds optimal paths even in complex environments. The paper further explores the optimality of JPS4 and compares its performance against the established A* algorithm on various grid maps. Benchmarking results demonstrate that JPS4 significantly outperforms A* in scenarios with high obstacle density. However, A* remains more efficient on open maps. Overall, JPS4 presents itself as a promising alternative to A* for pathfinding on 4-connected grids, particularly applicable in video game development.
Resource Allocation Driven by Large Models in Future Semantic-Aware Networks
Venue: arXiv cs.DC 2025
Year: 2025

Large model has emerged as a key enabler for the popularity of future networked intelligent applications. However, the surge of data traffic brought by intelligent applications puts pressure on the resource utilization and energy consumption of the future networks. With efficient content understanding capabilities, semantic communication holds significant potential for reducing data transmission in intelligent applications. In this article, resource allocation driven by large models in semantic-aware networks is investigated. Specifically, a semantic-aware communication network architecture based on scene graph models and multimodal pre-trained models is designed to achieve efficient data transmission. On the basis of the proposed network architecture, an intelligent resource allocation scheme in semantic-aware network is proposed to further enhance resource utilization efficiency. In the resource allocation scheme, the semantic transmission quality is adopted as an evaluation metric and the impact of wireless channel fading on semantic transmission is analyzed. To maximize the semantic transmission quality for multiple users, a diffusion model-based decision-making scheme is designed to address the power allocation problem in semantic-aware networks. Simulation results demonstrate that the proposed large-model-driven network architecture and resource allocation scheme achieve high-quality semantic transmission.