OpenRaiser/Intern-Atlas huggingface dataset:
How Deep is your Guess? A Fresh Perspective on Deep Learning for Medical Time-Series Imputation
Venue: arXiv cs.LG 2025
Year: 2025

We present a comprehensive analysis of deep learning approaches for Electronic Health Record (EHR) time-series imputation, examining how architectural and framework biases combine to influence model performance. Our investigation reveals varying capabilities of deep imputers in capturing complex spatiotemporal dependencies within EHRs, and that model effectiveness depends on how its combined biases align with medical time-series characteristics. Our experimental evaluation challenges common assumptions about model complexity, demonstrating that larger models do not necessarily improve performance. Rather, carefully designed architectures can better capture the complex patterns inherent in clinical data. The study highlights the need for imputation approaches that prioritise clinically meaningful data reconstruction over statistical accuracy. Our experiments show imputation performance variations of up to 20\% based on preprocessing and implementation choices, emphasising the need for standardised benchmarking methodologies. Finally, we identify critical gaps between current deep imputation methods and medical requirements, highlighting the importance of integrating clinical insights to achieve more reliable imputation approaches for healthcare applications.
Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computing
Venue: arXiv cs.LG 2025
Year: 2025

Federated Learning (FL) can protect the privacy of the vehicles in vehicle edge computing (VEC) to a certain extent through sharing the gradients of vehicles' local models instead of local data. The gradients of vehicles' local models are usually large for the vehicular artificial intelligence (AI) applications, thus transmitting such large gradients would cause large per-round latency. Gradient quantization has been proposed as one effective approach to reduce the per-round latency in FL enabled VEC through compressing gradients and reducing the number of bits, i.e., the quantization level, to transmit gradients. The selection of quantization level and thresholds determines the quantization error, which further affects the model accuracy and training time. To do so, the total training time and quantization error (QE) become two key metrics for the FL enabled VEC. It is critical to jointly optimize the total training time and QE for the FL enabled VEC. However, the time-varying channel condition causes more challenges to solve this problem. In this paper, we propose a distributed deep reinforcement learning (DRL)-based quantization level allocation scheme to optimize the long-term reward in terms of the total training time and QE. Extensive simulations identify the optimal weighted factors between the total training time and QE, and demonstrate the feasibility and effectiveness of the proposed scheme.
From AoI to QVAoI: Query-Based Semantics-Aware Scheduling for Energy-Harvesting IoT Status Update Systems
Venue: arXiv cs.IT 2025
Year: 2025

In this work, we study the freshness and significance of information in an IoT status update system where an Energy Harvesting (EH) device samples an information source and forwards the update packets to a destination node through a direct channel. We introduce and optimize a semantics-aware metric, Query Version Age of Information (QVAoI), in the system along with other metrics: Query Age of Information (QAoI), Version Age of Information (VAoI), and Age of Information (AoI). By employing the MDP framework, we formulate the optimization problem and determine the optimal transmission policies at the device, which involve deciding the time slots for updating, subject to the energy limitations imposed by the device's battery and energy arrivals. Through analytical and numerical results, we compare the performance of QVAoI-Optimal, QAoI-Optimal, VoI-Optimal, and AoI-Optimal policies with a baseline greedy policy. All semantics-aware policies show significantly improved performance compared to the greedy policy. The QVAoI-Optimal policy, in particular, demonstrates a significant performance improvement by either providing fresher, more relevant, and valuable updates with the same amount of energy arrivals or reducing the number of transmissions in the system to maintain the same level of freshness and significance of information compared to the QAoI-Optimal and other policies.
Model Surgery: Modulating LLM's Behavior Via Simple Parameter Editing
Venue: arXiv cs.AI 2025
Year: 2025

Large Language Models (LLMs) have demonstrated great potential as generalist assistants, showcasing powerful task understanding and problem-solving capabilities. To deploy LLMs as AI assistants, it is crucial that these models exhibit desirable behavioral traits, such as non-toxicity and resilience against jailbreak attempts. Current approaches for detoxification or preventing jailbreaking usually involve Supervised Fine-Tuning (SFT) or Reinforcement Learning from Human Feedback (RLHF), which requires finetuning billions of parameters through gradient descent with substantial computational cost. Furthermore, models modified through SFT and RLHF may deviate from the pretrained models, potentially leading to a degradation in foundational LLM capabilities. In this paper, we observe that surprisingly, directly editing a small subset of parameters can effectively modulate specific behaviors of LLMs, such as detoxification and resistance to jailbreaking, with only inference-level computational resources. Experiments demonstrate that in the detoxification task, our approach achieves reductions of up to 90.0% in toxicity on the RealToxicityPrompts dataset and 49.2% on ToxiGen, while maintaining the LLM's general capabilities in areas such as common sense, question answering, and mathematics
Thunderbolt: Concurrent Smart Contract Execution with Non-blocking Reconfiguration for Sharded DAGs
Venue: arXiv cs.DB 2025
Year: 2025

Sharding has emerged as a critical technique for enhancing blockchain system scalability. However, existing sharding approaches face unique challenges when applied to Directed Acyclic Graph (DAG)-based protocols that integrate expressive smart contract processing. Current solutions predominantly rely on coordination mechanisms like 2PC and require transaction read/write sets to optimize parallel execution. These requirements introduce two fundamental limitations: 1) additional coordination phases incur latency overhead, and 2) pre-declaration of read/write sets proves impractical for Turing-complete smart contracts with dynamic access patterns. This paper presents Thunderbolt, a novel sharding architecture for both single-shard transactions (Single-shard TXs) and cross-shard transactions (Cross-shard TXs) and enables nonblocking reconfiguration to ensure system liveness. Our design introduces 4 key innovations: 1) each replica serves dual roles as a full-shard representative and transaction proposer, employing the Execution-Order-Validation (EOV) model for Single-shard TXs and Order-Execution (OE) model for Cross-shard TXs. 2) we develop a DAG-based coordination protocol that establishes deterministic ordering between two transaction types while preserving concurrent execution capabilities. 3) we implement a dynamic concurrency controller that schedules Single-shard TXs without requiring prior knowledge of read/write sets, enabling runtime dependency resolution. 4) Thunderbolt introduces a nonblocking shard reconfiguration mechanism to address censorship attacks by featuring frequent shard re-assignment without impeding the construction of DAG nor blocking consensus. Thunderbolt achieves a 50x throughput improvement with 64 replicas compared to serial execution in the Tusk framework.
FlashNorm: fast normalization for LLMs
Venue: arXiv cs.LG 2025
Year: 2025

This paper presents FlashNorm, which is an exact but faster implementation of RMSNorm followed by linear layers. RMSNorm is used by many LLMs such as Llama, Mistral, and OpenELM. FlashNorm also speeds up Layer Normalization and its recently proposed replacement Dynamic Tanh (DyT) arXiv:2503.10622. FlashNorm also reduces the number of parameter tensors by simply merging the normalization weights with the weights of the next linear layer. See https://github.com/OpenMachine-ai/transformer-tricks for code and more transformer tricks.
Managing O-RAN Networks: xApp Development from Zero to Hero
Venue: arXiv cs.NI 2025
Year: 2025

The Open Radio Access Network (O-RAN) Alliance proposes an open architecture that disaggregates the RAN and supports executing custom control logic in near-real time from third-party applications, the xApps. Despite O-RAN's efforts, the creation of xApps remains a complex and time-consuming endeavor, aggravated by the sometimes fragmented, outdated, or deprecated documentation from the O-RAN Software Community (OSC). These challenges hinder academia and industry from developing and validating solutions and algorithms on O-RAN networks. This tutorial addresses this gap by providing the first comprehensive guide for developing xApps to manage the O-RAN ecosystem from theory to practice. We provide a thorough theoretical foundation of the O-RAN architecture and detail the functionality offered by Near Real-Time RAN Intelligent Controller (Near-RT RIC) components. We examine the xApp design and configuration. We explore the xApp lifecycle and demonstrate how to deploy and manage xApps on a Near-RT RIC. We address the xApps' interfaces and capabilities, accompanied by practical examples. We provide comprehensive details on how xApps can control the RAN. We discuss debugging strategies and good practices to aid the xApp developers in testing their xApps. Finally, we review the current landscape and open challenges for creating xApps.
Circuits and Backdoors: Five Shades of the SETH
Venue: arXiv cs.CC 2025
Year: 2025

The Strong Exponential Time Hypothesis (SETH) is a standard assumption in (fine-grained) parameterized complexity and many tight lower bounds are based on it. We consider a number of reasonable weakenings of the SETH, with sources from (i) circuit complexity (ii) backdoors for SAT-solving (iii) graph width parameters and (iv) weighted satisfiability problems. Our goal is to arrive at formulations which are simultaneously more plausible as hypotheses, but also capture interesting and robust notions of complexity. Using several tools from classical complexity theory we are able to consolidate these numerous hypotheses into a hierarchy of five main equivalence classes of increasing solidity. This framework serves as a step towards structurally classifying a variety of SETH-based lower bounds into intermediate equivalence classes. To illustrate the applicability of our framework, for each of our classes we give at least one (non-SAT) problem which is equivalent to the class as a characteristic example application. As our main showcase, we consider a natural parameterization of Independent Set by vertex deletion distance from several standard graph classes. We provide precise characterizations of the difficulty of breaking such bounds, in particular proving that obtaining $(2-\varepsilon)^kn^{O(1)}$ time algorithms for Cograph$+kv$ or Block$+kv$ graphs is equivalent to obtaining a fast satisfiability algorithm for circuits of depth $\varepsilon n$; while solving the weighted version for Interval$+kv$ graphs is equivalent to the (seemingly) harder problem of obtaining a fast satisfiability algorithm for SAT parameterized by a 2-SAT backdoor.
Unsupervised 3D Point Cloud Completion via Multi-view Adversarial Learning
Venue: arXiv cs.CV 2025
Year: 2025

In real-world scenarios, scanned point clouds are often incomplete due to occlusion issues. The tasks of self-supervised and weakly-supervised point cloud completion involve reconstructing missing regions of these incomplete objects without the supervision of complete ground truth. Current methods either rely on multiple views of partial observations for supervision or overlook the intrinsic geometric similarity that can be identified and utilized from the given partial point clouds. In this paper, we propose MAL-UPC, a framework that effectively leverages both region-level and category-specific geometric similarities to complete missing structures. Our MAL-UPC does not require any 3D complete supervision and only necessitates single-view partial observations in the training set. Specifically, we first introduce a Pattern Retrieval Network to retrieve similar position and curvature patterns between the partial input and the predicted shape, then leverage these similarities to densify and refine the reconstructed results. Additionally, we render the reconstructed complete shape into multi-view depth maps and design an adversarial learning module to learn the geometry of the target shape from category-specific single-view depth images of the partial point clouds in the training set. To achieve anisotropic rendering, we design a density-aware radius estimation algorithm to improve the quality of the rendered images. Our MAL-UPC outperforms current state-of-the-art self-supervised methods and even some unpaired approaches. We will make the source code publicly available at https://github.com/ltwu6/malspc
Convex space learning for tabular synthetic data generation
Venue: arXiv cs.LG 2025
Year: 2025

Generating synthetic samples from the convex space of the minority class is a popular oversampling approach for imbalanced classification problems. Recently, deep-learning approaches have been successfully applied to modeling the convex space of minority samples. Beyond oversampling, learning the convex space of neighborhoods in training data has not been used to generate entire tabular datasets. In this paper, we introduce a deep learning architecture (NextConvGeN) with a generator and discriminator component that can generate synthetic samples by learning to model the convex space of tabular data. The generator takes data neighborhoods as input and creates synthetic samples within the convex space of that neighborhood. Thereafter, the discriminator tries to classify these synthetic samples against a randomly sampled batch of data from the rest of the data space. We compared our proposed model with five state-of-the-art tabular generative models across ten publicly available datasets from the biomedical domain. Our analysis reveals that synthetic samples generated by NextConvGeN can better preserve classification and clustering performance across real and synthetic data than other synthetic data generation models. Synthetic data generation by deep learning of the convex space produces high scores for popular utility measures. We further compared how diverse synthetic data generation strategies perform in the privacy-utility spectrum and produced critical arguments on the necessity of high utility models. Our research on deep learning of the convex space of tabular data opens up opportunities in clinical research, machine learning model development, decision support systems, and clinical data sharing.
The Average-Value Allocation Problem
Venue: arXiv cs.DS 2025
Year: 2025

We initiate the study of centralized algorithms for welfare-maximizing allocation of goods to buyers subject to average-value constraints. We show that this problem is NP-hard to approximate beyond a factor of $\frac{e}{e-1}$, and provide a $\frac{4e}{e-1}$-approximate offline algorithm. For the online setting, we show that no non-trivial approximations are achievable under adversarial arrivals. Under i.i.d. arrivals, we present a polytime online algorithm that provides a constant approximation of the optimal (computationally-unbounded) online algorithm. In contrast, we show that no constant approximation of the ex-post optimum is achievable by an online algorithm.
Spectral Representation for Causal Estimation with Hidden Confounders
Venue: arXiv cs.LG 2025
Year: 2025

We address the problem of causal effect estimation where hidden confounders are present, with a focus on two settings: instrumental variable regression with additional observed confounders, and proxy causal learning. Our approach uses a singular value decomposition of a conditional expectation operator, followed by a saddle-point optimization problem, which, in the context of IV regression, can be thought of as a neural net generalization of the seminal approach due to Darolles et al. [2011]. Saddle-point formulations have gathered considerable attention recently, as they can avoid double sampling bias and are amenable to modern function approximation methods. We provide experimental validation in various settings, and show that our approach outperforms existing methods on common benchmarks.
Error Bounds for the Network Scale-Up Method
Venue: arXiv cs.DC 2025
Year: 2025

Epidemiologists and social scientists have used the Network Scale-Up Method (NSUM) for over thirty years to estimate the size of a hidden sub-population within a social network. This method involves querying a subset of network nodes about the number of their neighbours belonging to the hidden sub-population. In general, NSUM assumes that the social network topology and the hidden sub-population distribution are well-behaved; hence, the NSUM estimate is close to the actual value. However, bounds on NSUM estimation errors have not been analytically proven. This paper provides analytical bounds on the error incurred by the two most popular NSUM estimators. These bounds assume that the queried nodes accurately provide their degree and the number of neighbors belonging to the hidden population. Our key findings are twofold. First, we show that when an adversary designs the network and places the hidden sub-population, then the estimate can be a factor of $Ω(\sqrt{n})$ off from the real value (in a network with $n$ nodes). Second, we also prove error bounds when the underlying network is randomly generated, showing that a small constant factor can be achieved with high probability using samples of logarithmic size $O(\log{n})$. We present improved analytical bounds for Erdos-Renyi and Scale-Free networks. Our theoretical analysis is supported by an extensive set of numerical experiments designed to determine the effect of the sample size on the accuracy of the estimates in both synthetic and real networks.
Gaussian process regression + deep neural network autoencoder for probabilistic surrogate modeling in nonlinear mechanics of solids
Venue: arXiv cs.CE 2025
Year: 2025

Many real-world applications demand accurate and fast predictions, as well as reliable uncertainty estimates. However, quantifying uncertainty on high-dimensional predictions is still a severely under-investigated problem, especially when input-output relationships are non-linear. To handle this problem, the present work introduces an innovative approach that combines autoencoder deep neural networks with the probabilistic regression capabilities of Gaussian processes. The autoencoder provides a low-dimensional representation of the solution space, while the Gaussian process is a Bayesian method that provides a probabilistic mapping between the low-dimensional inputs and outputs. We validate the proposed framework for its application to surrogate modeling of non-linear finite element simulations. Our findings highlight that the proposed framework is computationally efficient as well as accurate in predicting non-linear deformations of solid bodies subjected to external forces, all the while providing insightful uncertainty assessments.
Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks
Venue: arXiv cs.LG 2025
Year: 2025

Generalization of machine learning models can be severely compromised by data poisoning, where adversarial changes are applied to the training data. This vulnerability has led to interest in certifying (i.e., proving) that such changes up to a certain magnitude do not affect test predictions. We, for the first time, certify Graph Neural Networks (GNNs) against poisoning attacks, including backdoors, targeting the node features of a given graph. Our certificates are white-box and based upon $(i)$ the neural tangent kernel, which characterizes the training dynamics of sufficiently wide networks; and $(ii)$ a novel reformulation of the bilevel optimization problem describing poisoning as a mixed-integer linear program. Consequently, we leverage our framework to provide fundamental insights into the role of graph structure and its connectivity on the worst-case robustness behavior of convolution-based and PageRank-based GNNs. We note that our framework is more general and constitutes the first approach to derive white-box poisoning certificates for NNs, which can be of independent interest beyond graph-related tasks.
BECAUSE: Bilinear Causal Representation for Generalizable Offline Model-based Reinforcement Learning
Venue: arXiv cs.LG 2025
Year: 2025

Offline model-based reinforcement learning (MBRL) enhances data efficiency by utilizing pre-collected datasets to learn models and policies, especially in scenarios where exploration is costly or infeasible. Nevertheless, its performance often suffers from the objective mismatch between model and policy learning, resulting in inferior performance despite accurate model predictions. This paper first identifies the primary source of this mismatch comes from the underlying confounders present in offline data for MBRL. Subsequently, we introduce \textbf{B}ilin\textbf{E}ar \textbf{CAUS}al r\textbf{E}presentation~(BECAUSE), an algorithm to capture causal representation for both states and actions to reduce the influence of the distribution shift, thus mitigating the objective mismatch problem. Comprehensive evaluations on 18 tasks that vary in data quality and environment context demonstrate the superior performance of BECAUSE over existing offline RL algorithms. We show the generalizability and robustness of BECAUSE under fewer samples or larger numbers of confounders. Additionally, we offer theoretical analysis of BECAUSE to prove its error bound and sample efficiency when integrating causal representation into offline MBRL.
Exploring the Boundaries of On-Device Inference: When Tiny Falls Short, Go Hierarchical
Venue: arXiv cs.LG 2025
Year: 2025

On-device inference holds great potential for increased energy efficiency, responsiveness, and privacy in edge ML systems. However, due to less capable ML models that can be embedded in resource-limited devices, use cases are limited to simple inference tasks such as visual keyword spotting, gesture recognition, and predictive analytics. In this context, the Hierarchical Inference (HI) system has emerged as a promising solution that augments the capabilities of the local ML by offloading selected samples to an edge server or cloud for remote ML inference. Existing works demonstrate through simulation that HI improves accuracy. However, they do not account for the latency and energy consumption on the device, nor do they consider three key heterogeneous dimensions that characterize ML systems: hardware, network connectivity, and models. In contrast, this paper systematically compares the performance of HI with on-device inference based on measurements of accuracy, latency, and energy for running embedded ML models on five devices with different capabilities and three image classification datasets. For a given accuracy requirement, the HI systems we designed achieved up to 73% lower latency and up to 77% lower device energy consumption than an on-device inference system. The key to building an efficient HI system is the availability of small-size, reasonably accurate on-device models whose outputs can be effectively differentiated for samples that require remote inference. Despite the performance gains, HI requires on-device inference for all samples, which adds a fixed overhead to its latency and energy consumption. Therefore, we design a hybrid system, Early Exit with HI (EE-HI), and demonstrate that compared to HI, EE-HI reduces the latency by up to 59.7% and lowers the device's energy consumption by up to 60.4%.