OpenRaiser/Intern-Atlas huggingface dataset:
PATH: A Discrete-sequence Dataset for Evaluating Online Unsupervised Anomaly Detection Approaches for Multivariate Time Series
Venue: arXiv cs.LG 2025
Year: 2025

Benchmarking anomaly detection approaches for multivariate time series is a challenging task due to a lack of high-quality datasets. Current publicly available datasets are too small, not diverse and feature trivial anomalies, which hinders measurable progress in this research area. We propose a solution: a diverse, extensive, and non-trivial dataset generated via state-of-the-art simulation tools that reflects realistic behaviour of an automotive powertrain, including its multivariate, dynamic and variable-state properties. Additionally, our dataset represents a discrete-sequence problem, which remains unaddressed by previously-proposed solutions in literature. To cater for both unsupervised and semi-supervised anomaly detection settings, as well as time series generation and forecasting, we make different versions of the dataset available, where training and test subsets are offered in contaminated and clean versions, depending on the task. We also provide baseline results from a selection of approaches based on deterministic and variational autoencoders, as well as a non-parametric approach. As expected, the baseline experimentation shows that the approaches trained on the semi-supervised version of the dataset outperform their unsupervised counterparts, highlighting a need for approaches more robust to contaminated training data. Furthermore, results show that the threshold used can have a large influence on detection performance, hence more work needs to be invested in methods to find a suitable threshold without the need for labelled data.
Physics-Informed Autonomous LLM Agents for Explainable Power Electronics Modulation Design
Venue: arXiv cs.AI 2025
Year: 2025

LLM-based autonomous agents have recently shown strong capabilities in solving complex industrial design tasks. However, in domains aiming for carbon neutrality and high-performance renewable energy systems, current AI-assisted design automation methods face critical challenges in explainability, scalability, and practical usability. To address these limitations, we introduce PHIA (Physics-Informed Autonomous Agent), an LLM-driven system that automates modulation design for power converters in Power Electronics Systems with minimal human intervention. In contrast to traditional pipeline-based methods, PHIA incorporates an LLM-based planning module that interactively acquires and verifies design requirements via a user-friendly chat interface. This planner collaborates with physics-informed simulation and optimization components to autonomously generate and iteratively refine modulation designs. The interactive interface also supports interpretability by providing textual explanations and visual outputs throughout the design process. Experimental results show that PHIA reduces standard mean absolute error by 63.2% compared to the second-best benchmark and accelerates the overall design process by over 33 times. A user study involving 20 domain experts further confirms PHIA's superior design efficiency and usability, highlighting its potential to transform industrial design workflows in power electronics.
Algebras for Deterministic Computation Are Inherently Incomplete
Venue: arXiv cs.PL 2025
Year: 2025

Kleene Algebra with Tests (KAT) provides an elegant algebraic framework for describing non-deterministic finite-state computations. Using a small finite set of non-deterministic programming constructs (sequencing, non-deterministic choice, and iteration) it is able to express all non-deterministic finite state control flow over a finite set of primitives. It is natural to ask whether there exists a similar finite set of constructs that can capture all deterministic computation. We show that this is not the case. More precisely, the deterministic fragment of KAT is not generated by any finite set of regular control flow operations. This generalizes earlier results about the expressivity of the traditional control flow operations, i.e., sequential composition, if-then-else and while.
StereoCrafter-Zero: Zero-Shot Stereo Video Generation with Noisy Restart
Venue: arXiv cs.CV 2025
Year: 2025

Generating high-quality stereo videos that mimic human binocular vision requires consistent depth perception and temporal coherence across frames. Despite advances in image and video synthesis using diffusion models, producing high-quality stereo videos remains a challenging task due to the difficulty of maintaining consistent temporal and spatial coherence between left and right views. We introduce StereoCrafter-Zero, a novel framework for zero-shot stereo video generation that leverages video diffusion priors without requiring paired training data. Our key innovations include a noisy restart strategy to initialize stereo-aware latent representations and an iterative refinement process that progressively harmonizes the latent space, addressing issues like temporal flickering and view inconsistencies. In addition, we propose the use of dissolved depth maps to streamline latent space operations by reducing high-frequency depth information. Our comprehensive evaluations, including quantitative metrics and user studies, demonstrate that StereoCrafter-Zero produces high-quality stereo videos with enhanced depth consistency and temporal smoothness, even when depth estimations are imperfect. Our framework is robust and adaptable across various diffusion models, setting a new benchmark for zero-shot stereo video generation and enabling more immersive visual experiences. Our code is in https://github.com/shijianjian/StereoCrafter-Zero.
ACRIC: Securing Legacy Communication Networks via Authenticated Cyclic Redundancy Integrity Check
Venue: arXiv cs.CR 2025
Year: 2025

The increasing integration of modern IT technologies into OT technologies and industrial systems is expanding the vulnerability surface of legacy infrastructures, which often rely on outdated protocols and resource-constrained devices. Recent security incidents in safety-critical industries exposed how the lack of proper message authentication enables attackers to inject malicious commands or alter system behavior, revealing fundamental security weaknesses in existing architectures. These shortcomings have thus prompted new regulations that emphasize the pressing need to strengthen cybersecurity, particularly in legacy systems. Authentication is widely recognized as a fundamental security measure that enhances system resilience. However, its adoption in legacy industrial environments is limited due to practical challenges like backward compatibility, message format changes, and hardware replacement or upgrades costs. In this paper, we introduce ACRIC, a message authentication solution to secure legacy industrial communications explicitly tailored to overcome those challenges all at once. ACRIC uniquely leverages cryptographic computations applied to the CRC field - already present in most industrial communication protocols - ensuring robust message integrity protection and authentication without requiring additional hardware or modifications to existing message formats. ACRIC's backward compatibility and protocol-agnostic nature enable coexistence with non-secured devices, thus facilitating gradual security upgrades in legacy infrastructures. Formal security assessment and experimental evaluation on an industrial-grade testbed demonstrate that ACRIC provides robust security guarantees with minimal computational overhead (~ 4 us). These results underscore ACRIC's practicality, cost-effectiveness, and suitability for effective adoption in resource-constrained industrial environments.
PRISM: A Personalized, Rapid, and Immersive Skill Mastery framework for personalizing experiential learning through Generative AI
Venue: arXiv cs.CY 2025
Year: 2025

The rise of generative AI (gen-AI) is transforming industries, particularly in education and workforce training. This chapter introduces PRISM (Personalized, Rapid, and Immersive Skill Mastery), a scalable framework leveraging gen-AI and Digital Twins (DTs) to deliver adaptive, experiential learning. PRISM integrates sentiment analysis and Retrieval-Augmented Generation (RAG) to monitor learner comprehension and dynamically adjust content to meet course objectives. We further present the Multi-Fidelity Digital Twin for Education (MFDT-E) framework, aligning DT fidelity levels with Bloom's Taxonomy and the Kirkpatrick evaluation model to support undergraduate, master's, and doctoral training. Experimental validation shows that GPT-4 achieves 91 percent F1 in zero-shot sentiment analysis of teacher-student dialogues, while GPT-3.5 performs robustly in informal language contexts. Additionally, the system's effectiveness and scalability for immersive Industry 4.0 training are demonstrated through four VR modules: Home Scene, Factory Floor Tour, Capping Station DT, and PPE Inspection Training. These results highlight the potential of integrating generative AI with digital twins to enable personalized, efficient, and scalable education.
dc-GAN: Dual-Conditioned GAN for Face Demorphing From a Single Morph
Venue: arXiv cs.CV 2025
Year: 2025

A facial morph is an image strategically created by combining two face images pertaining to two distinct identities. The goal is to create a face image that can be matched to two different identities by a face matcher. Face demorphing inverts this process and attempts to recover the original images constituting a facial morph. Existing demorphing techniques have two major limitations: (a) they assume that some identities are common in the train and test sets; and (b) they are prone to the morph replication problem, where the outputs are merely replicates of the input morph. In this paper, we overcome these issues by proposing dc-GAN (dual-conditioned GAN), a novel demorphing method conditioned on the morph image as well as the embedding extracted from the image. Our method overcomes the morph replication problem and produces high-fidelity reconstructions of the constituent images. Moreover, the proposed method is highly generalizable and applicable to both reference-based and reference-free demorphing methods. Experiments were conducted using the AMSL, FRLL-Morphs, and MorDiff datasets to demonstrate the efficacy of the method.
Understanding World or Predicting Future? A Comprehensive Survey of World Models
Venue: arXiv cs.CL 2025
Year: 2025

The concept of world models has garnered significant attention due to advancements in multimodal large language models such as GPT-4 and video generation models such as Sora, which are central to the pursuit of artificial general intelligence. This survey offers a comprehensive review of the literature on world models. Generally, world models are regarded as tools for either understanding the present state of the world or predicting its future dynamics. This review presents a systematic categorization of world models, emphasizing two primary functions: (1) constructing internal representations to understand the mechanisms of the world, and (2) predicting future states to simulate and guide decision-making. Initially, we examine the current progress in these two categories. We then explore the application of world models in key domains, including generative games, autonomous driving, robotics, and social simulacra, with a focus on how each domain utilizes these aspects. Finally, we outline key challenges and provide insights into potential future research directions. We summarize the representative papers along with their code repositories in https://github.com/tsinghua-fib-lab/World-Model.
U-Motion: Learned Point Cloud Video Compression with U-Structured Temporal Context Generation
Venue: arXiv cs.CV 2025
Year: 2025

Point cloud video (PCV) is a versatile 3D representation of dynamic scenes with emerging applications. This paper introduces U-Motion, a learning-based compression scheme for both PCV geometry and attributes. We propose a U-Structured inter-frame prediction framework, U-Inter, which performs explicit motion estimation and compensation (ME/MC) at different scales with varying levels of detail. It integrates Top-Down (Fine-to-Coarse) Motion Propagation, Bottom-Up Motion Predictive Coding and Multi-scale Group Motion Compensation to enable accurate motion estimation and efficient motion compression at each scale. In addition, we design a multi-scale spatial-temporal predictive coding module to capture the cross-scale spatial redundancy remaining after U-Inter prediction. We conduct experiments following the MPEG Common Test Condition for dense dynamic point clouds and demonstrate that U-Motion can achieve significant gains over MPEG G-PCC-GesTM v3.0 and recently published learning-based methods for both geometry and attribute compression.
Variational Autoencoders for Efficient Simulation-Based Inference
Venue: arXiv cs.LG 2025
Year: 2025

We present a generative modeling approach based on the variational inference framework for likelihood-free simulation-based inference. The method leverages latent variables within variational autoencoders to efficiently estimate complex posterior distributions arising from stochastic simulations. We explore two variations of this approach distinguished by their treatment of the prior distribution. The first model adapts the prior based on observed data using a multivariate prior network, enhancing generalization across various posterior queries. In contrast, the second model utilizes a standard Gaussian prior, offering simplicity while still effectively capturing complex posterior distributions. We demonstrate the ability of the proposed approach to approximate complex posteriors while maintaining computational efficiency on well-established benchmark problems.
Reducibility among NP-Hard graph problems and boundary classes
Venue: arXiv cs.CC 2025
Year: 2025

Many NP-hard graph problems become easy for some classes of graphs. For example, coloring is easy for bipartite graphs, but NP-hard in general. So we can ask question like when does a hard problem become easy? What is the minimum substructure for which the problem remains hard? We use the notion of boundary classes to study such questions. In this paper, we introduce a method for transforming the boundary class of one NP-hard graph problem into a boundary class for another problem. If Π and Γ are two NP-hard graph problems where Π is reducible to Γ, we transform a boundary class of Π into a boundary class of Γ. More formally if Π is reducible to Γ, where the reduction satisfies certain conditions, then X is a boundary class of Π if and only if the image of X under the reduction is a boundary class of Γ. This gives us a relationship between boundary classes and reducibility among several NP-hard problems. To show the strength of our main result, we apply our theorem to obtain some previously unknown boundary classes for a few graph problems namely; vertex-cover, clique, traveling-salesperson, bounded-degree-spanning-tree, subgraph-isomorphism and clique-cover.
Efficient Spatio-Temporal Signal Recognition on Edge Devices Using PointLCA-Net
Venue: arXiv cs.LG 2025
Year: 2025

Recent advancements in machine learning, particularly through deep learning architectures like PointNet, have transformed the processing of three-dimensional (3D) point clouds, significantly improving 3D object classification and segmentation tasks. While 3D point clouds provide detailed spatial information, spatio-temporal signals introduce a dynamic element that accounts for changes over time. However, applying deep learning techniques to spatio-temporal signals and deploying them on edge devices presents challenges, including real-time processing, memory capacity, and power consumption. To address these issues, this paper presents a novel approach that combines PointNet's feature extraction with the in-memory computing capabilities and energy efficiency of neuromorphic systems for spatio-temporal signal recognition. The proposed method consists of a two-stage process: in the first stage, PointNet extracts features from the spatio-temporal signals, which are then stored in non-volatile memristor crossbar arrays. In the second stage, these features are processed by a single-layer spiking neural encoder-decoder that employs the Locally Competitive Algorithm (LCA) for efficient encoding and classification. This work integrates the strengths of both PointNet and LCA, enhancing computational efficiency and energy performance on edge devices. PointLCA-Net achieves high recognition accuracy for spatio-temporal data with substantially lower energy burden during both inference and training than comparable approaches, thus advancing the deployment of advanced neural architectures in energy-constrained environments.
Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare
Venue: arXiv cs.LG 2025
Year: 2025

Hyperdimensional computing (HDC) enables efficient data encoding and processing in high-dimensional space, benefiting machine learning and data analysis. However, underutilization of these spaces can lead to overfitting and reduced model reliability, especially in data-limited systems a critical issue in sectors like healthcare that demand robustness and consistent performance. We introduce BoostHD, an approach that applies boosting algorithms to partition the hyperdimensional space into subspaces, creating an ensemble of weak learners. By integrating boosting with HDC, BoostHD enhances performance and reliability beyond existing HDC methods. Our analysis highlights the importance of efficient utilization of hyperdimensional spaces for improved model performance. Experiments on healthcare datasets show that BoostHD outperforms state-of-the-art methods. On the WESAD dataset, it achieved an accuracy of 98.37%, surpassing Random Forest, XGBoost, and OnlineHD. BoostHD also demonstrated superior inference efficiency and stability, maintaining high accuracy under data imbalance and noise. In person-specific evaluations, it achieved an average accuracy of 96.19%, outperforming other models. By addressing the limitations of both boosting and HDC, BoostHD expands the applicability of HDC in critical domains where reliability and precision are paramount.
Can GNNs Learn Link Heuristics? A Concise Review and Evaluation of Link Prediction Methods
Venue: arXiv cs.SI 2025
Year: 2025

This paper explores the ability of Graph Neural Networks (GNNs) in learning various forms of information for link prediction, alongside a brief review of existing link prediction methods. Our analysis reveals that GNNs cannot effectively learn structural information related to the number of common neighbors between two nodes, primarily due to the nature of set-based pooling of the neighborhood aggregation scheme. Also, our extensive experiments indicate that trainable node embeddings can improve the performance of GNN-based link prediction models. Importantly, we observe that the denser the graph, the greater such the improvement. We attribute this to the characteristics of node embeddings, where the link state of each link sample could be encoded into the embeddings of nodes that are involved in the neighborhood aggregation of the two nodes in that link sample. In denser graphs, every node could have more opportunities to attend the neighborhood aggregation of other nodes and encode states of more link samples to its embedding, thus learning better node embeddings for link prediction. Lastly, we demonstrate that the insights gained from our research carry important implications in identifying the limitations of existing link prediction methods, which could guide the future development of more robust algorithms.
Approximating the Held-Karp Bound for Metric TSP in Nearly Linear Work and Polylogarithmic Depth
Venue: arXiv cs.DS 2025
Year: 2025

We present a nearly linear work parallel algorithm for approximating the Held-Karp bound for the Metric TSP problem. Given an edge-weighted undirected graph $G=(V,E)$ on $m$ edges and $ε>0$, it returns a $(1+ε)$-approximation to the Held-Karp bound with high probability, in $\tilde{O}(m/ε^4)$ work and $\tilde{O}(1/ε^4)$ depth. While a nearly linear time sequential algorithm was known for almost a decade (Chekuri and Quanrud'17), it was not known how to simultaneously achieve nearly linear work alongside polylogarithmic depth. Using a reduction by Chalermsook et al.'22, we also give a parallel algorithm for computing a $(1+ε)$-approximate fractional solution to the $k$-edge-connected spanning subgraph (kECSS) problem, with similar complexity. To obtain these results, we introduce a notion of core-sequences for the parallel Multiplicative Weights Update (MWU) framework (Luby-Nisan'93, Young'01). For the Metric TSP and kECSS problems, core-sequences enable us to exploit the structure of approximate minimum cuts to reduce the cost per iteration and/or the number of iterations. The acceleration technique via core-sequences is generic and of independent interest. In particular, it improves the best-known iteration complexity of MWU algorithms for packing/covering LPs from $poly(\log nnz(A))$ to polylogarithmic in the product of cardinalities of the core-sequence sets, where $A$ is the constraint matrix of the LP. For certain implicitly defined LPs such as the kECSS LP, this yields an exponential improvement in depth.
Enhancing a Hierarchical Graph Rewriting Language based on MELL Cut Elimination
Venue: arXiv cs.PL 2025
Year: 2025

Hierarchical graph rewriting is a highly expressive computational formalism that manipulates graphs enhanced with box structures for representing hierarchies. It has provided the foundations of various graph-based modeling tools, but the design of high-level declarative languages based on hierarchical graph rewriting is still a challenge. For a solid design choice, well-established formalisms with backgrounds other than graph rewriting would provide useful guidelines. Proof nets of Multiplicative Exponential Linear Logic (MELL) is such a framework because its original formulation of cut elimination is essentially graph rewriting involving box structures, where so-called Promotion Boxes with an indefinite number of non-local edges may be cloned, migrated and deleted. This work builds on LMNtal as a declarative language based on hierarchical (port) graph rewriting, and discusses how it can be extended to support the above operations on Promotion Boxes of MELL proof nets. LMNtal thus extended turns out to be a practical graph rewriting language that has strong affinity with MELL proof nets. The language features provided are general enough to encode other well-established models of concurrency. Using the toolchain of LMNtal that provides state-space search and model checking, we implemented cut elimination rules of MELL proof nets in extended LMNtal and demonstrated that the platform could serve as a useful workbench for proof nets.