OpenRaiser/Intern-Atlas huggingface dataset:
Hidden Conflicts in Neural Networks and Their Implications for Explainability
Venue: arXiv cs.LG 2025
Year: 2025

Artificial Neural Networks (ANNs) often represent conflicts between features, arising naturally during training as the network learns to integrate diverse and potentially disagreeing inputs to better predict the target variable. Despite their relevance to the ``reasoning'' processes of these models, the properties and implications of conflicts for understanding and explaining ANNs remain underexplored. In this paper, we develop a rigorous theory of conflicts in ANNs and demonstrate their impact on ANN explainability through two case studies. In the first case study, we use our theory of conflicts to inspire the design of a novel feature attribution method, which we call Conflict-Aware Feature-wise Explanations (CAFE). CAFE separates the positive and negative influences of features and biases, enabling more faithful explanations for models applied to tabular data. In the second case study, we take preliminary steps towards understanding the role of conflicts in out-of-distribution (OOD) scenarios. Through our experiments, we identify potentially useful connections between model conflicts and different kinds of distributional shifts in tabular and image data. Overall, our findings demonstrate the importance of accounting for conflicts in the development of more reliable explanation methods for AI systems, which are crucial for the beneficial use of these systems in the society.
Assessing the Sustainability and Trustworthiness of Federated Learning Models
Venue: arXiv cs.CY 2025
Year: 2025

Artificial intelligence (AI) increasingly influences critical decision-making across sectors. Federated Learning (FL), as a privacy-preserving collaborative AI paradigm, not only enhances data protection but also holds significant promise for intelligent network management, including distributed monitoring, adaptive control, and edge intelligence. Although the trustworthiness of FL systems has received growing attention, the sustainability dimension remains insufficiently explored, despite its importance for scalable real-world deployment. To address this gap, this work introduces sustainability as a distinct pillar within a comprehensive trustworthy FL taxonomy, consistent with AI-HLEG guidelines. This pillar includes three key aspects: hardware efficiency, federation complexity, and the carbon intensity of energy sources. Experiments using the FederatedScope framework under diverse scenarios, including varying participants, system complexity, hardware, and energy configurations, validate the practicality of the approach. Results show that incorporating sustainability into FL evaluation supports environmentally responsible deployment, enabling more efficient, adaptive, and trustworthy network services and management AI models.
Requirement falsification for cyber-physical systems using generative models
Venue: arXiv cs.LG 2025
Year: 2025

We present the OGAN algorithm for automatic requirement falsification of cyber-physical systems. System inputs and outputs are represented as piecewise constant signals over time while requirements are expressed in signal temporal logic. OGAN can find inputs that are counterexamples for the correctness of a system revealing design, software, or hardware defects before the system is taken into operation. The OGAN algorithm works by training a generative machine learning model to produce such counterexamples. It executes tests offline and does not require any previous model of the system under test. We evaluate OGAN using the ARCH-COMP benchmark problems, and the experimental results show that generative models are a viable method for requirement falsification. OGAN can be applied to new systems with little effort, has few requirements for the system under test, and exhibits state-of-the-art CPS falsification efficiency and effectiveness.
Untangling Graphs on Surfaces
Venue: arXiv cs.CG 2025
Year: 2025

Consider a graph drawn on a surface (for example, the plane minus a finite set of obstacle points), possibly with crossings. We provide an algorithm to decide whether such a drawing can be untangled, namely, if one can slide the vertices and edges of the graph on the surface (avoiding the obstacles) to remove all crossings; in other words, whether the drawing is homotopic to an embedding. While the problem boils down to planarity testing when the surface is the sphere or the disk (or equivalently the plane without any obstacle), the other cases have never been studied before, except when the input graph is a cycle, in an abundant literature in topology and more recently by Despré and Lazarus [SoCG 2017, J. ACM 2019]. Our algorithm runs in O(m + poly(g+b) n log n) time, where g >= 0 and b >= 0 are the genus and the number of boundary components of the input orientable surface S, and n is the size of the input graph drawing, lying on some fixed graph of size m cellularly embedded on S. We use various techniques from two-dimensional computational topology and from the theory of hyperbolic surfaces. Most notably, we introduce reducing triangulations, a novel discrete analog of hyperbolic surfaces in the spirit of systems of quads by Lazarus and Rivaud [FOCS 2012] and Erickson and Whittlesey [SODA 2013], which have the additional benefit that reduced paths are unique and stable upon reversal; they are likely of independent interest. Tailored data structures are needed to achieve certain homotopy tests efficiently on these triangulations. As a key subroutine, we rely on an algorithm to test the weak simplicity of a graph drawn on a surface by Akitaya, Fulek, and Tóth [SODA 2018, TALG 2019].
Revealing CNN Architectures via Side-Channel Analysis in Dataflow-based Inference Accelerators
Venue: arXiv cs.CR 2025
Year: 2025

Convolutional Neural Networks (CNNs) are widely used in various domains, including image recognition, medical diagnosis and autonomous driving. Recent advances in dataflow-based CNN accelerators have enabled CNN inference in resource-constrained edge devices. These dataflow accelerators utilize inherent data reuse of convolution layers to process CNN models efficiently. Concealing the architecture of CNN models is critical for privacy and security. This article evaluates memory-based side-channel information to recover CNN architectures from dataflow-based CNN inference accelerators. The proposed attack exploits spatial and temporal data reuse of the dataflow mapping on CNN accelerators and architectural hints to recover the structure of CNN models. Experimental results demonstrate that our proposed side-channel attack can recover the structures of popular CNN models, namely, Lenet, Alexnet, VGGnet16, and YOLOv2.
On Preserving the Knowledge of Long Clinical Texts
Venue: arXiv cs.CL 2025
Year: 2025

Clinical texts, such as admission notes, discharge summaries, and progress notes, contain rich and valuable information that can be used for clinical decision making. However, a severe bottleneck in using transformer encoders for processing clinical texts comes from the input length limit of these models: transformer-based encoders use fixed-length inputs. Therefore, these models discard part of the inputs while processing medical text. There is a risk of losing vital knowledge from clinical text if only part of it is processed. This paper proposes a novel method to preserve the knowledge of long clinical texts in the models using aggregated ensembles of transformer encoders. Previous studies used either ensemble or aggregation, but we studied the effects of fusing these methods. We trained several pre-trained BERT-like transformer encoders on two clinical outcome tasks: mortality prediction and length of stay prediction. Our method achieved better results than all baseline models for prediction tasks on long clinical notes. We conducted extensive experiments on the MIMIC-III clinical database's admission notes by combining multiple unstructured and high-dimensional datasets, demonstrating our method's effectiveness and superiority over existing approaches. This study shows that fusing ensemble and aggregation improves the model performance for clinical prediction tasks, particularly the mortality and the length of hospital stay.
TinyFormer: Efficient Transformer Design and Deployment on Tiny Devices
Venue: arXiv cs.LG 2025
Year: 2025

Developing deep learning models on tiny devices (e.g. Microcontroller units, MCUs) has attracted much attention in various embedded IoT applications. However, it is challenging to efficiently design and deploy recent advanced models (e.g. transformers) on tiny devices due to their severe hardware resource constraints. In this work, we propose TinyFormer, a framework specifically designed to develop and deploy resource-efficient transformer models on MCUs. TinyFormer consists of SuperNAS, SparseNAS, and SparseEngine. Separately, SuperNAS aims to search for an appropriate supernet from a vast search space. SparseNAS evaluates the best sparse single-path transformer model from the identified supernet. Finally, SparseEngine efficiently deploys the searched sparse models onto MCUs. To the best of our knowledge, SparseEngine is the first deployment framework capable of performing inference of sparse transformer models on MCUs. Evaluation results on the CIFAR-10 dataset demonstrate that TinyFormer can design efficient transformers with an accuracy of 96.1% while adhering to hardware constraints of 1MB storage and 320KB memory. Additionally, TinyFormer achieves significant speedups in sparse inference, up to 12.2x comparing to the CMSIS-NN library. TinyFormer is believed to bring powerful transformers into TinyML scenarios and to greatly expand the scope of deep learning applications
On the Generalization Properties of Diffusion Models
Venue: arXiv cs.LG 2025
Year: 2025

Diffusion models are a class of generative models that serve to establish a stochastic transport map between an empirically observed, yet unknown, target distribution and a known prior. Despite their remarkable success in real-world applications, a theoretical understanding of their generalization capabilities remains underdeveloped. This work embarks on a comprehensive theoretical exploration of the generalization attributes of diffusion models. We establish theoretical estimates of the generalization gap that evolves in tandem with the training dynamics of score-based diffusion models, suggesting a polynomially small generalization error ($O(n^{-2/5}+m^{-4/5})$) on both the sample size $n$ and the model capacity $m$, evading the curse of dimensionality (i.e., not exponentially large in the data dimension) when early-stopped. Furthermore, we extend our quantitative analysis to a data-dependent scenario, wherein target distributions are portrayed as a succession of densities with progressively increasing distances between modes. This precisely elucidates the adverse effect of "modes shift" in ground truths on the model generalization. Moreover, these estimates are not solely theoretical constructs but have also been confirmed through numerical simulations. Our findings contribute to the rigorous understanding of diffusion models' generalization properties and provide insights that may guide practical applications.
Fast Approximation Algorithms for Piercing Boxes by Points
Venue: arXiv cs.CG 2025
Year: 2025

$\newcommand{\popt}{\mathcal{p}} \newcommand{\Re}{\mathbb{R}}\newcommand{\N}{\mathcal{N}} \newcommand{\BX}{\mathcal{B}} \newcommand{\bb}{\mathsf{b}} \newcommand{\eps}{\varepsilon} \newcommand{\polylog}{\mathrm{polylog}} $ Let $\mathcal{B}=\{\mathsf{b}_1, \ldots ,\mathsf{b}_n\}$ be a set of $n$ axis-aligned boxes in $\Re^d$ where $d\geq2$ is a constant. The \emph{piercing problem} is to compute a smallest set of points $\N \subset \Re^d$ that hits every box in $\mathcal{B}$, i.e., $\N\cap \mathsf{b}_i\neq \emptyset$, for $i=1,\ldots, n$. Let $\popt=\popt(\mathcal{B})$, the \emph{piercing number} be the minimum size of a piercing set of $\mathcal{B}$. We present a randomized $O(d^2\log\log \popt)$-approximation algorithm with expected running time $O(n^{d/2}\polylog n)$. Next, we present a faster $O(n^{\log d+1})$-time algorithm but with a slightly inferior approximation factor of $O(2^{4d}\log\log\popt)$. The running time of both algorithms can be improved to near-linear using a sampling-based technique, if $\popt = O(n^{1/d})$. For the dynamic version of the problem in the plane, we obtain a randomized $O(\log\log\popt)$-approximation algorithm with $O(n^{1/2}\polylog n )$ amortized expected update time for insertion or deletion of boxes. For squares in $\Re^2$, the update time can be improved to $O(n^{1/3}\polylog n )$.
Can ChatGPT support software verification?
Venue: arXiv cs.SE 2025
Year: 2025

Large language models have become increasingly effective in software engineering tasks such as code generation, debugging and repair. Language models like ChatGPT can not only generate code, but also explain its inner workings and in particular its correctness. This raises the question whether we can utilize ChatGPT to support formal software verification. In this paper, we take some first steps towards answering this question. More specifically, we investigate whether ChatGPT can generate loop invariants. Loop invariant generation is a core task in software verification, and the generation of valid and useful invariants would likely help formal verifiers. To provide some first evidence on this hypothesis, we ask ChatGPT to annotate 106 C programs with loop invariants. We check validity and usefulness of the generated invariants by passing them to two verifiers, Frama-C and CPAchecker. Our evaluation shows that ChatGPT is able to produce valid and useful invariants allowing Frama-C to verify tasks that it could not solve before. Based on our initial insights, we propose ways of combining ChatGPT (or large language models in general) and software verifiers, and discuss current limitations and open issues.
Ephemeral Rollups are All you Need
Venue: arXiv cs.DC 2025
Year: 2025

In the realm of open and composable gaming, we envision platforms where users actively expand, create, engage, and immerse themselves in a rich world of entertainment. One promising avenue for achieving this vision is through fully on-chain (FOC) games, where both game state and logic reside on the blockchain, maximizing composability. However, we must grapple with inherent limitations and trade-offs, particularly in terms of costs and scalability. This paper proposes a framework that leverages the Solana Virtual Machine (SVM) to scale FOC games without state fragmentation or compromised trust assumptions. The framework introduces a systematic approach for discovering, utilizing, and publishing modular pieces of logic as components deeply rooted in the Entity-Component-System (ECS) pattern. To enhance scalability and resource optimization, we introduce the concept of Ephemeral Rollups (ERs) that overcome the tradeoffs of L2 horizontal scaling. These dedicated runtimes can be customized to provide higher operational speed, configurable ticking mechanisms, provable sessions and gasless transactions without composability-scalability tradeoffs.
Improving Collaborative Filtering Recommendation via Graph Learning
Venue: arXiv cs.IR 2025
Year: 2025

Recommendation systems aim to provide personalized predictions by identifying items that are most appealing to individual users. Among various recommendation approaches, k-nearest-neighbor (kNN)-based collaborative filtering (CF) remains one of the most widely used in practice. However, the kNN scheme often results in running the algorithm on a highly dense graph, which degrades computational efficiency. In addition, enforcing a uniform neighborhood size is not well suited to capturing the true underlying structure of the data. In this paper, we leverage recent advances in graph signal processing (GSP) to learn a sparse yet high-quality graph, improving the efficiency of collaborative filtering without sacrificing recommendation accuracy. Experiments on benchmark datasets demonstrate that our method can successfully perform CF-based recommendation using an extremely sparse graph while maintaining competitive performance.
Orion: A Fully Homomorphic Encryption Framework for Deep Learning
Venue: arXiv cs.CR 2025
Year: 2025

Fully Homomorphic Encryption (FHE) has the potential to substantially improve privacy and security by enabling computation directly on encrypted data. This is especially true with deep learning, as today, many popular user services are powered by neural networks in the cloud. Beyond its well-known high computational costs, one of the major challenges facing wide-scale deployment of FHE-secured neural inference is effectively mapping these networks to FHE primitives. FHE poses many programming challenges including packing large vectors, managing accumulated noise, and translating arbitrary and general-purpose programs to the limited instruction set provided by FHE. These challenges make building large FHE neural networks intractable using the tools available today. In this paper we address these challenges with Orion, a fully-automated framework for private neural inference using FHE. Orion accepts deep neural networks written in PyTorch and translates them into efficient FHE programs. We achieve this by proposing a novel single-shot multiplexed packing strategy for arbitrary convolutions and through a new, efficient technique to automate bootstrap placement and scale management. We evaluate Orion on common benchmarks used by the FHE deep learning community and outperform state-of-the-art by 2.38x on ResNet-20, the largest network they report. Orion's techniques enable processing much deeper and larger networks. We demonstrate this by evaluating ResNet-50 on ImageNet and present the first high-resolution FHE object detection experiments using a YOLO-v1 model with 139 million parameters. Orion is open-source for all to use at: https://github.com/baahl-nyu/orion
BlockEmulator: An Emulator Enabling to Test Blockchain Sharding Protocols
Venue: arXiv cs.CR 2025
Year: 2025

Numerous blockchain simulators have been proposed to allow researchers to simulate mainstream blockchains. However, we have not yet found a testbed that enables researchers to develop and evaluate their new consensus algorithms or new protocols for blockchain sharding systems. To fill this gap, we developed BlockEmulator, which is designed as an experimental platform, particularly for emulating blockchain sharding mechanisms. BlockEmulator adopts a lightweight blockchain architecture so developers can only focus on implementing their new protocols or mechanisms. Using layered modules and useful programming interfaces offered by BlockEmulator, researchers can implement a new protocol with minimum effort. Through experiments, we test various functionalities of BlockEmulator in two steps. Firstly, we prove the correctness of the emulation results yielded by BlockEmulator by comparing the theoretical analysis with the observed experiment results. Secondly, other experimental results demonstrate that BlockEmulator can facilitate measuring a series of metrics, including throughput, transaction confirmation latency, cross-shard transaction ratio, the queuing status of transaction pools, workload distribution across blockchain shards, etc. We have made BlockEmulator open-source in Github.
Hypothesis Network Planned Exploration for Rapid Meta-Reinforcement Learning Adaptation
Venue: arXiv cs.AI 2025
Year: 2025

Meta-Reinforcement Learning (Meta-RL) learns optimal policies across a series of related tasks. A central challenge in Meta-RL is rapidly identifying which previously learned task is most similar to a new one, in order to adapt to it quickly. Prior approaches, despite significant success, typically rely on passive exploration strategies such as periods of random action to characterize the new task in relation to the learned ones. While sufficient when tasks are clearly distinguishable, passive exploration limits adaptation speed when informative transitions are rare or revealed only by specific behaviors. We introduce Hypothesis-Planned Exploration (HyPE), a method that actively plans sequences of actions during adaptation to efficiently identify the most similar previously learned task. HyPE operates within a joint latent space, where state-action transitions from different tasks form distinct paths. This latent-space planning approach enables HyPE to serve as a drop-in improvement for most model-based Meta-RL algorithms. By using planned exploration, HyPE achieves exponentially lower failure probability compared to passive strategies when informative transitions are sparse. On a natural language Alchemy game, HyPE identified the closest task in 65-75% of trials, far outperforming the 18-28% passive exploration baseline, and yielding up to 4x more successful adaptations under the same sample budget.
A Nearly Linear-Time Distributed Algorithm for Maximum Cardinality Matching
Venue: arXiv cs.DC 2025
Year: 2025

In this paper, we propose a randomized $\tilde{O}(μ(G))$-round algorithm for the maximum cardinality matching problem in the CONGEST model, where $μ(G)$ means the maximum size of a matching of the input graph $G$. The proposed algorithm substantially improves the current best worst-case running time. The key technical ingredient is a new randomized algorithm of finding an augmenting path of length $\ell$ with high probability within $\tilde{O}(\ell)$ rounds, which positively settles an open problem left in the prior work by Ahmadi and Kuhn [DISC'20]. The idea of our augmenting path algorithm is based on a recent result by Kitamura and Izumi [IEICE Trans.'22], which efficiently identifies a sparse substructure of the input graph containing an augmenting path, following a new concept called \emph{alternating base trees}. Their algorithm, however, resorts in part to a centralized approach of collecting the entire information of the substructure into a single vertex for constructing a long augmenting path. The technical highlight of this paper is to provide a fully-decentralized counterpart of such a centralized method. To develop the algorithm, we prove several new structural properties of alternating base trees, which are of independent interest.
Foundational theories of hesitant fuzzy sets and families of hesitant fuzzy sets
Venue: arXiv cs.AI 2025
Year: 2025

Hesitant fuzzy sets find extensive application in specific scenarios involving uncertainty and hesitation. In the context of set theory, the concept of inclusion relationship holds significant importance as a fundamental definition. Consequently, as a type of sets, hesitant fuzzy sets necessitate a clear and explicit definition of the inclusion relationship. Based on the discrete form of hesitant fuzzy membership degrees, this study proposes multiple types of inclusion relationships for hesitant fuzzy sets. Subsequently, this paper introduces foundational propositions related to hesitant fuzzy sets, as well as propositions concerning families of hesitant fuzzy sets.