OpenRaiser/Intern-Atlas huggingface dataset:
Learning to Interfere in Non-Orthogonal Multiple-Access Joint Source-Channel Coding
Venue: arXiv cs.NI 2025
Year: 2025

We consider multiple transmitters aiming to communicate their source signals (e.g., images) over a multiple access channel (MAC). Conventional communication systems minimize interference by orthogonally allocating resources (time and/or bandwidth) among users, which limits their capacity. We introduce a machine learning (ML)-aided wireless image transmission method that merges compression and channel coding using a multi-view autoencoder, which allows the transmitters to use all the available channel resources simultaneously, resulting in a non-orthogonal multiple access (NOMA) scheme. The receiver must recover all the images from the received superposed signal, while also associating each image with its transmitter. Traditional ML models deal with individual samples, whereas our model allows signals from different users to interfere in order to leverage gains from NOMA under limited bandwidth and power constraints. We introduce a progressive fine-tuning algorithm that doubles the number of users at each iteration, maintaining initial performance with orthogonalized user-specific projections, which is then improved through fine-tuning steps. Remarkably, our method scales up to 16 users and beyond, with only a 0.6% increase in the number of trainable parameters compared to a single-user model, significantly enhancing recovered image quality and outperforming existing NOMA-based methods over a wide range of datasets, metrics, and channel conditions. Our approach paves the way for more efficient and robust multi-user communication systems, leveraging innovative ML components and strategies.
Hierarchical Prediction-based Management for LMaaS Systems
Venue: arXiv cs.DC 2025
Year: 2025

Large Language Models (LLMs) have revolutionized numerous domains, driving the rise of Language-Model-as-a-Service (LMaaS) platforms that process millions of queries daily. These platforms must minimize latency and meet Service Level Objectives (SLOs) while optimizing resource usage. However, conventional cloud service management techniques, designed for traditional workloads, are suboptimal for LMaaS due to its dynamic service workloads and variable request loads. To address this, we propose PreServe, a tailored LMaaS management framework centered on hierarchical prediction. PreServe incorporates a service workload predictor to estimate periodic token density at a coarse granularity and a novel request load predictor to assess the resource demand of individual LLM requests, enabling the construction of a load anticipator for each LLM instance. By integrating both long-term and short-term predictions, PreServe adjusts resource allocation in advance, mitigating the risks of instance under- or over-provisioning. Besides, PreServe optimizes request routing by considering both current and anticipated future instance loads, ensuring balanced load distribution across instances. Evaluations on real-world production datasets show that PreServe outperforms state-of-the-art methods, reducing tail latency by 41.3%, cutting resource consumption by 49.38%, while incurring only 0.23% additional overhead.
Geometric Flow Models over Neural Network Weights
Venue: arXiv cs.LG 2025
Year: 2025

Deep generative models such as flow and diffusion models have proven to be effective in modeling high-dimensional and complex data types such as videos or proteins, and this has motivated their use in different data modalities, such as neural network weights. A generative model of neural network weights would be useful for a diverse set of applications, such as Bayesian deep learning, learned optimization, and transfer learning. However, the existing work on weight-space generative models often ignores the symmetries of neural network weights, or only takes into account a subset of them. Modeling those symmetries, such as permutation symmetries between subsequent layers in an MLP, the filters in a convolutional network, or scaling symmetries arising with the use of non-linear activations, holds the potential to make weight-space generative modeling more efficient by effectively reducing the dimensionality of the problem. In this light, we aim to design generative models in weight-space that more comprehensively respect the symmetries of neural network weights. We build on recent work on generative modeling with flow matching, and weight-space graph neural networks to design three different weight-space flows. Each of our flows takes a different approach to modeling the geometry of neural network weights, and thus allows us to explore the design space of weight-space flows in a principled way. Our results confirm that modeling the geometry of neural networks more faithfully leads to more effective flow models that can generalize to different tasks and architectures, and we show that while our flows obtain competitive performance with orders of magnitude fewer parameters than previous work, they can be further improved by scaling them up. We conclude by listing potential directions for future work on weight-space generative models.
Safeguarding Smart Inhaler Devices and Patient Privacy in Respiratory Health Monitoring
Venue: arXiv cs.CR 2025
Year: 2025

The rapid development of Internet of Things (IoT) technology has significantly impacted various market sectors. According to Li et al. (2024), an estimated 75 billion devices will be on the market in 2025. The healthcare industry is a target to improve patient care and ease healthcare provider burdens. Chronic respiratory disease is likely to benefit from their inclusion, with 545 million people worldwide recorded to suffer from patients using these devices to track their dosage. At the same time, healthcare providers can improve medication administration and monitor respiratory health (Soriano et al., 2020). While IoT medical devices offer numerous benefits, they also have security vulnerabilities that can expose patient data to cyberattacks. It's crucial to prioritize security measures in developing and deploying IoT medical devices, especially in personalized health monitoring systems for individuals with respiratory conditions. Efforts are underway to assess the security risks associated with intelligent inhalers and respiratory medical devices by understanding usability behavior and technological elements to identify and address vulnerabilities effectively. This work analyses usability behavior and technical vulnerabilities, emphasizing the confidentiality of information gained from Smart Inhalers. It then extrapolates to interrogate potential vulnerabilities with Implantable Medical Devices (IMDs). Our work explores the tensions in device development through the intersection of IoT technology and respiratory health, particularly in the context of intelligent inhalers and other breathing medical devices, calling for integrating robust security measures into the development and deployment of IoT devices to safeguard patient data and ensure the secure functioning of these critical healthcare technologies.
A Benchmark for Scalable Oversight Protocols
Venue: arXiv cs.AI 2025
Year: 2025

As AI agents surpass human capabilities, scalable oversight -- the problem of effectively supplying human feedback to potentially superhuman AI models -- becomes increasingly critical to ensure alignment. While numerous scalable oversight protocols have been proposed, they lack a systematic empirical framework to evaluate and compare them. While recent works have tried to empirically study scalable oversight protocols -- particularly Debate -- we argue that the experiments they conduct are not generalizable to other protocols. We introduce the scalable oversight benchmark, a principled framework for evaluating human feedback mechanisms based on our agent score difference (ASD) metric, a measure of how effectively a mechanism advantages truth-telling over deception. We supply a Python package to facilitate rapid and competitive evaluation of scalable oversight protocols on our benchmark, and conduct a demonstrative experiment benchmarking Debate.
TDBench: A Benchmark for Top-Down Image Understanding with Reliability Analysis of Vision-Language Models
Venue: arXiv cs.LG 2025
Year: 2025

Top-down images play an important role in safety-critical settings such as autonomous navigation and aerial surveillance, where they provide holistic spatial information that front-view images cannot capture. Despite this, Vision Language Models (VLMs) are mostly trained and evaluated on front-view benchmarks, leaving their performance in the top-down setting poorly understood. Existing evaluations also overlook a unique property of top-down images: their physical meaning is preserved under rotation. In addition, conventional accuracy metrics can be misleading, since they are often inflated by hallucinations or "lucky guesses", which obscures a model's true reliability and its grounding in visual evidence. To address these issues, we introduce TDBench, a benchmark for top-down image understanding that includes 2000 curated questions for each rotation. We further propose RotationalEval (RE), which measures whether models provide consistent answers across four rotated views of the same scene, and we develop a reliability framework that separates genuine knowledge from chance. Finally, we conduct four case studies targeting underexplored real-world challenges. By combining rigorous evaluation with reliability metrics, TDBench not only benchmarks VLMs in top-down perception but also provides a new perspective on trustworthiness, guiding the development of more robust and grounded AI systems. Project homepage: https://github.com/Columbia-ICSL/TDBench
Proof of Humanity: A Multi-Layer Network Framework for Certifying Human-Originated Content in an AI-Dominated Internet
Venue: arXiv cs.CR 2025
Year: 2025

The rapid proliferation of generative AI has led to an internet increasingly populated with synthetic content-text, images, audio, and video generated without human intervention. As the distinction between human and AI-generated data blurs, the ability to verify content origin becomes critical for applications ranging from social media and journalism to legal and financial systems. In this paper, we propose a conceptual, multi-layer architectural framework that enables telecommunications networks to act as infrastructure level certifiers of human-originated content. By leveraging identity anchoring at the physical layer, metadata propagation at the network and transport layers, and cryptographic attestations at the session and application layers, Telcos can provide an end-to-end Proof of Humanity for data traversing their networks. We outline how each OSI layer can contribute to this trust fabric using technical primitives such as SIM/eSIM identity, digital signatures, behavior-based ML heuristics, and edge-validated APIs. The framework is presented as a foundation for future implementation, highlighting monetization pathways for telcos such as trust-as-a-service APIs, origin-certified traffic tiers, and regulatory compliance tools. The paper does not present implementation or benchmarking results but offers a technically detailed roadmap and strategic rationale for transforming Telcos into validators of digital authenticity in an AI-dominated internet. Security, privacy, and adversarial considerations are discussed as directions for future work.
Flow State: Humans Enabling AI Systems to Program Themselves
Venue: arXiv cs.AI 2025
Year: 2025

Compound AI systems, orchestrating multiple AI components and external APIs, are increasingly vital but face challenges in managing complexity, handling ambiguity, and enabling effective development workflows. Existing frameworks often introduce significant overhead, implicit complexity, or restrictive abstractions, hindering maintainability and iterative refinement, especially in Human-AI collaborative settings. We argue that overcoming these hurdles requires a foundational architecture prioritizing structural clarity and explicit control. To this end, we introduce Pocketflow, a platform centered on Human-AI co-design, enabled by Pocketflow. Pocketflow is a Python framework built upon a deliberately minimal yet synergistic set of core abstractions: modular Nodes with a strict lifecycle, declarative Flow orchestration, native hierarchical nesting (Flow-as-Node), and explicit action-based conditional logic. This unique combination provides a robust, vendor-agnostic foundation with very little code that demonstrably reduces overhead while offering the expressiveness needed for complex patterns like agentic workflows and RAG. Complemented by Pocket AI, an assistant leveraging this structure for system design, Pocketflow provides an effective environment for iteratively prototyping, refining, and deploying the adaptable, scalable AI systems demanded by modern enterprises.
SHapley Estimated Explanation (SHEP): A Fast Post-Hoc Attribution Method for Interpreting Intelligent Fault Diagnosis
Venue: arXiv cs.LG 2025
Year: 2025

Despite significant progress in intelligent fault diagnosis (IFD), the lack of interpretability remains a critical barrier to practical industrial applications, driving the growth of interpretability research in IFD. Post-hoc interpretability has gained popularity due to its ability to preserve network flexibility and scalability without modifying model structures. However, these methods often yield suboptimal time-domain explanations. Recently, combining domain transform with SHAP has improved interpretability by extending explanations to more informative domains. Nonetheless, the computational expense of SHAP, exacerbated by increased dimensions from domain transforms, remains a major challenge. To address this, we propose patch-wise attribution and SHapley Estimated Explanation (SHEP). Patch-wise attribution reduces feature dimensions at the cost of explanation granularity, while SHEP simplifies subset enumeration to approximate SHAP, reducing complexity from exponential to linear. Together, these methods significantly enhance SHAP's computational efficiency, providing feasibility for real-time interpretation in monitoring tasks. Extensive experiments confirm SHEP's efficiency, interpretability, and reliability in approximating SHAP. Additionally, with open-source code, SHEP has the potential to serve as a benchmark for post-hoc interpretability in IFD. The code is available on https://github.com/ChenQian0618/SHEP.
Exploring energy consumption of AI frameworks on a 64-core RV64 Server CPU
Venue: arXiv cs.DC 2025
Year: 2025

In today's era of rapid technological advancement, artificial intelligence (AI) applications require large-scale, high-performance, and data-intensive computations, leading to significant energy demands. Addressing this challenge necessitates a combined approach involving both hardware and software innovations. Hardware manufacturers are developing new, efficient, and specialized solutions, with the RISC-V architecture emerging as a prominent player due to its open, extensible, and energy-efficient instruction set architecture (ISA). Simultaneously, software developers are creating new algorithms and frameworks, yet their energy efficiency often remains unclear. In this study, we conduct a comprehensive benchmark analysis of machine learning (ML) applications on the 64-core SOPHON SG2042 RISC-V architecture. We specifically analyze the energy consumption of deep learning inference models across three leading AI frameworks: PyTorch, ONNX Runtime, and TensorFlow. Our findings show that frameworks using the XNNPACK back-end, such as ONNX Runtime and TensorFlow, consume less energy compared to PyTorch, which is compiled with the native OpenBLAS back-end.
Augmenting Anonymized Data with AI: Exploring the Feasibility and Limitations of Large Language Models in Data Enrichment
Venue: arXiv cs.CR 2025
Year: 2025

Large Language Models (LLMs) have demonstrated advanced capabilities in both text generation and comprehension, and their application to data archives might facilitate the privatization of sensitive information about the data subjects. In fact, the information contained in data often includes sensitive and personally identifiable details. This data, if not safeguarded, may bring privacy risks in terms of both disclosure and identification. Furthermore, the application of anonymisation techniques, such as k-anonymity, can lead to a significant reduction in the amount of data within data sources, which may reduce the efficacy of predictive processes. In our study, we investigate the capabilities offered by LLMs to enrich anonymized data sources without affecting their anonymity. To this end, we designed new ad-hoc prompt template engineering strategies to perform anonymized Data Augmentation and assess the effectiveness of LLM-based approaches in providing anonymized data. To validate the anonymization guarantees provided by LLMs, we exploited the pyCanon library, designed to assess the values of the parameters associated with the most common privacy-preserving techniques via anonymization. Our experiments conducted on real-world datasets demonstrate that LLMs yield promising results for this goal.
Offline and Distributional Reinforcement Learning for Wireless Communications
Venue: arXiv cs.LG 2025
Year: 2025

The rapid growth of heterogeneous and massive wireless connectivity in 6G networks demands intelligent solutions to ensure scalability, reliability, privacy, ultra-low latency, and effective control. Although artificial intelligence (AI) and machine learning (ML) have demonstrated their potential in this domain, traditional online reinforcement learning (RL) and deep RL methods face limitations in real-time wireless networks. For instance, these methods rely on online interaction with the environment, which might be unfeasible, costly, or unsafe. In addition, they cannot handle the inherent uncertainties in real-time wireless applications. We focus on offline and distributional RL, two advanced RL techniques that can overcome these challenges by training on static datasets and accounting for network uncertainties. We introduce a novel framework that combines offline and distributional RL for wireless communication applications. Through case studies on unmanned aerial vehicle (UAV) trajectory optimization and radio resource management (RRM), we demonstrate that our proposed Conservative Quantile Regression (CQR) algorithm outperforms conventional RL approaches regarding convergence speed and risk management. Finally, we discuss open challenges and potential future directions for applying these techniques in 6G networks, paving the way for safer and more efficient real-time wireless systems.
Fast Thermal-Aware Chiplet Placement Assisted by Surrogate
Venue: arXiv cs.OH 2025
Year: 2025

With the advent of the post-Moore era, the 2.5-D advanced package is a promising solution to sustain the development of very large-scale integrated circuits. However, the thermal placement of chiplet, due to the high complexity of thermal simulation, is very challenging. In this paper, a surrogate-assisted simulated annealing algorithm is proposed to simultaneously minimize both the wirelength and the maximum temperature of integrated chips. To alleviate the computational cost of thermal simulation, a radial basis function network is introduced to approximate the thermal field, assisted by which the simulated annealing algorithm converges to the better placement in less time. Numerical results demonstrate that the surrogate-assisted simulated annealing algorithm is competitive to the state-of-the-art thermal placement algorithms of chiplet, suggesting its potential application in the agile design of 2.5D package chip.
Exploring Various Sequential Learning Methods for Deformation History Modeling
Venue: arXiv cs.LG 2025
Year: 2025

Current neural network (NN) models can learn patterns from data points with historical dependence. Specifically, in natural language processing (NLP), sequential learning has transitioned from recurrence-based architectures to transformer-based architectures. However, it is unknown which NN architectures will perform the best on datasets containing deformation history due to mechanical loading. Thus, this study ascertains the appropriateness of 1D-convolutional, recurrent, and transformer-based architectures for predicting deformation localization based on the earlier states in the form of deformation history. Following this investigation, the crucial incompatibility issues between the mathematical computation of the prediction process in the best-performing NN architectures and the actual values derived from the natural physical properties of the deformation paths are examined in detail.
RealProbe: An Automated and Lightweight Performance Profiler for In-FPGA Execution of High-Level Synthesis Designs
Venue: arXiv cs.AR 2025
Year: 2025

High-level synthesis (HLS) accelerates FPGA design by rapidly generating diverse implementations using optimization directives. However, even with cycle-accurate C/RTL co-simulation, the reported clock cycles often differ significantly from actual FPGA performance. This discrepancy hampers accurate bottleneck identification, leading to suboptimal design choices. Existing in-FPGA profiling tools, such as the Integrated Logic Analyzer (ILA), require tedious inspection of HLS-generated RTL and manual signal monitoring, reducing productivity. To address these challenges, we introduce RealProbe, the first fully automated, lightweight in-FPGA profiling tool for HLS designs. With a single directive--#pragma HLS RealProbe--the tool automatically generates all necessary code to profile cycle counts across the full function hierarchy, including submodules and loops. RealProbe extracts, records, and visualizes cycle counts with high precision, providing actionable insights into on-board performance. RealProbe is non-intrusive, implemented as independent logic to ensure minimal impact on kernel functionality or timing. It also supports automated design space exploration (DSE), optimizing resource allocation based on FPGA constraints and module complexity. By leveraging incremental synthesis and implementation, DSE runs independently of the original HLS kernel. Evaluated across 28 diverse test cases, including a large-scale design, RealProbe achieves 100% accuracy in capturing cycle counts with minimal logic overhead-just 16.98% LUTs, 43.15% FFs, and 0% BRAM usage. The tool, with full documentation and examples, is available on GitHub at https://github.com/sharc-lab/RealProbe .
Accurate GPU Memory Prediction for Deep Learning Jobs through Dynamic Analysis
Venue: arXiv cs.DC 2025
Year: 2025

The benefits of Deep Learning (DL) impose significant pressure on GPU resources, particularly within GPU cluster, where Out-Of-Memory (OOM) errors present a primary impediment to model training and efficient resource utilization. Conventional OOM estimation techniques, relying either on static graph analysis or direct GPU memory profiling, suffer from inherent limitations: static analysis often fails to capture model dynamics, whereas GPU-based profiling intensifies contention for scarce GPU resources. To overcome these constraints, VeritasEst emerges. It is an innovative, entirely CPU-based analysis tool capable of accurately predicting the peak GPU memory required for DL training tasks without accessing the target GPU. This "offline" prediction capability is core advantage of VeritasEst, allowing accurate memory footprint information to be obtained before task scheduling, thereby effectively preventing OOM and optimizing GPU allocation. Its performance was validated through thousands of experimental runs across convolutional neural network (CNN) models: Compared to baseline GPU memory estimators, VeritasEst significantly reduces the relative error by 84% and lowers the estimation failure probability by 73%. VeritasEst represents a key step towards efficient and predictable DL training in resource-constrained environments.