OpenRaiser/Intern-Atlas huggingface dataset:
Learning Lifted STRIPS Models from Action Traces Alone: A Simple, General, and Scalable Solution
Venue: arXiv cs.AI 2025
Year: 2025

Learning STRIPS action models from action traces alone is a challenging problem as it involves learning the domain predicates as well. In this work, a novel approach is introduced which, like the well-known LOCM systems, is scalable, but like SAT approaches, is sound and complete. Furthermore, the approach is general and imposes no restrictions on the hidden domain or the number or arity of the predicates. The new learning method is based on an \emph{efficient, novel test} that checks whether the assumption that a predicate is affected by a set of action patterns, namely, actions with specific argument positions, is consistent with the traces. The predicates and action patterns that pass the test provide the basis for the learned domain that is then easily completed with preconditions and static predicates. The new method is studied theoretically and experimentally. For the latter, the method is evaluated on traces and graphs obtained from standard classical domains like the 8-puzzle, which involve hundreds of thousands of states and transitions. The learned representations are then verified on larger instances.
Somesite I Used To Crawl: Awareness, Agency and Efficacy in Protecting Content Creators From AI Crawlers
Venue: arXiv cs.HC 2025
Year: 2025

The success of generative AI relies heavily on training on data scraped through extensive crawling of the Internet, a practice that has raised significant copyright, privacy, and ethical concerns. While few measures are designed to resist a resource-rich adversary determined to scrape a site, crawlers can be impacted by a range of existing tools such as robots.txt, NoAI meta tags, and active crawler blocking by reverse proxies. In this work, we seek to understand the ability and efficacy of today's networking tools to protect content creators against AI-related crawling. For targeted populations like human artists, do they have the technical knowledge and agency to utilize crawler-blocking tools such as robots.txt, and can such tools be effective? Using large scale measurements and a targeted user study of 203 professional artists, we find strong demand for tools like robots.txt, but significantly constrained by critical hurdles in technical awareness, agency in deploying them, and limited efficacy against unresponsive crawlers. We further test and evaluate network-level crawler blockers provided by reverse proxies. Despite relatively limited deployment today, they offer stronger protections against AI crawlers, but still come with their own set of limitations.
Quantized symbolic time series approximation
Venue: arXiv cs.LG 2025
Year: 2025

Time series are ubiquitous in numerous science and engineering domains, e.g., signal processing, bioinformatics, and astronomy. Previous work has verified the efficacy of symbolic time series representation in a variety of engineering applications due to its storage efficiency and numerosity reduction. The most recent symbolic aggregate approximation technique, ABBA, has been shown to preserve essential shape information of time series and improve downstream applications, e.g., neural network inference regarding prediction and anomaly detection in time series. Motivated by the emergence of high-performance hardware which enables efficient computation for low bit-width representations, we present a new quantization-based ABBA symbolic approximation technique, QABBA, which exhibits improved storage efficiency while retaining the original speed and accuracy of symbolic reconstruction. We prove an upper bound for the error arising from quantization and discuss how the number of bits should be chosen to balance this with other errors. An application of QABBA with large language models (LLMs) for time series regression is also presented, and its utility is investigated. By representing the symbolic chain of patterns on time series, QABBA not only avoids the training of embedding from scratch, but also achieves a new state-of-the-art on Monash regression dataset. The symbolic approximation to the time series offers a more efficient way to fine-tune LLMs on the time series regression task which contains various application domains. We further present a set of extensive experiments performed across various well-established datasets to demonstrate the advantages of the QABBA method for symbolic approximation.
Influence functions and regularity tangents for efficient active learning
Venue: arXiv cs.LG 2025
Year: 2025

In this paper we describe an efficient method for providing a regression model with a sense of curiosity about its data. In the field of machine learning, our framework for representing curiosity is called Active Learning, which concerns the problem of automatically choosing data points for which to query labels in the semi-supervised setting. The methods we propose are based on computing a "regularity tangent" vector that can be calculated (with only a constant slow-down) together with the model's parameter vector during training. We then take the inner product of this tangent vector with the gradient vector of the model's loss at a given data point to obtain a measure of the influence of that point on the complexity of the model. In the simplest instantiation, there is only a single regularity tangent vector, of the same dimension as the parameter vector. Thus, in the proposed technique, once training is complete, evaluating our "curiosity" about a potential query data point can be done as quickly as calculating the model's loss gradient at that point. The new vector only doubles the amount of storage required by the model. We show that the quantity computed by our technique is an example of an "influence function", and that it measures the expected squared change in model complexity incurred by up-weighting a given data point. We propose a number of ways for using this and other related quantities to choose new training data points for a regression model.
A Contrast-Agnostic Method for Ultra-High Resolution Claustrum Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

The claustrum is a band-like gray matter structure located between putamen and insula whose exact functions are still actively researched. Its sheet-like structure makes it barely visible in in vivo Magnetic Resonance Imaging (MRI) scans at typical resolutions and neuroimaging tools for its study, including methods for automatic segmentation, are currently very limited. In this paper, we propose a contrast- and resolution-agnostic method for claustrum segmentation at ultra-high resolution (0.35 mm isotropic); the method is based on the SynthSeg segmentation framework (Billot et al., 2023), which leverages the use of synthetic training intensity images to achieve excellent generalization. In particular, SynthSeg requires only label maps to be trained, since corresponding intensity images are synthesized on the fly with random contrast and resolution. We trained a deep learning network for automatic claustrum segmentation, using claustrum manual labels obtained from 18 ultra-high resolution MRI scans (mostly ex vivo). We demonstrated the method to work on these 18 high resolution cases (Dice score = 0.632, mean surface distance = 0.458 mm, and volumetric similarity = 0.867 using 6-fold Cross Validation (CV)), and also on in vivo T1-weighted MRI scans at typical resolutions (~1 mm isotropic). We also demonstrated that the method is robust in a test-retest setting and when applied to multimodal imaging (T2-weighted, Proton Density and quantitative T1 scans). To the best of our knowledge this is the first accurate method for automatic ultra-high resolution claustrum segmentation, which is robust against changes in contrast and resolution. The method is released at https://github.com/chiara-mauri/claustrum_segmentation and as part of the neuroimaging package Freesurfer (Fischl, 2012).
Partial Knowledge Distillation for Alleviating the Inherent Inter-Class Discrepancy in Federated Learning
Venue: arXiv cs.LG 2025
Year: 2025

Substantial efforts have been devoted to alleviating the impact of the long-tailed class distribution in federated learning. In this work, we observe an interesting phenomenon that certain weak classes consistently exist even for class-balanced learning. These weak classes, different from the minority classes in the previous works, are inherent to data and remain fairly consistent for various network structures, learning paradigms, and data partitioning methods. The inherent inter-class accuracy discrepancy can reach over 36.9% for federated learning on the FashionMNIST and CIFAR-10 datasets, even when the class distribution is balanced both globally and locally. In this study, we empirically analyze the potential reason for this phenomenon. Furthermore, a partial knowledge distillation (PKD) method is proposed to improve the model's classification accuracy for weak classes. In this approach, knowledge transfer is initiated upon the occurrence of specific misclassifications within certain weak classes. Experimental results show that the accuracy of weak classes can be improved by 10.7%, reducing the inherent inter-class discrepancy effectively.
Monte Carlo Throughput Estimation in Unstable LEO Satellite Networks
Venue: arXiv cs.NI 2025
Year: 2025

This study introduces a new framework for analyzing capacity dynamics and throughput performance in Low Earth Orbit satellite networks (LSNs). It focuses on addressing critical gaps in existing models, particularly those concerning unreliable ISLs. Our work systematically resolves two inherent deficiencies in prior research: (1) the conflation of network capacity with maximum throughput, the latter being highly dependent on routing policies and thus failing to reflect the intrinsic characteristics of the system; and (2) the overestimation problem in flow network based throughput calculations, which often generate flow paths that are inconsistent with actual traffic paths. To address these issues, we develop the CAP-uLSN (Capacity under unstable LEO satellites networks) model to characterize time-varying network capacity under stochastic ISL availability. Furthermore, we propose a Monte Carlo Throughput Estimation (MCTE) framework that probabilistically evaluates aggregate throughput performance under dynamic traffic patterns and diverse routing schemes. These insights derived from the CAP-uLSN model and MCTE framework, provide theoretical guidance for optimizing routing schemes (e.g., path selection under throughput fluctuations) and designing adaptive billing models (e.g., distance-based pricing) in future LEO satellite networks.
What Makes a Scene ? Scene Graph-based Evaluation and Feedback for Controllable Generation
Venue: arXiv cs.CV 2025
Year: 2025

While text-to-image generation has been extensively studied, generating images from scene graphs remains relatively underexplored, primarily due to challenges in accurately modeling spatial relationships and object interactions. To fill this gap, we introduce Scene-Bench, a comprehensive benchmark designed to evaluate and enhance the factual consistency in generating natural scenes. Scene-Bench comprises MegaSG, a large-scale dataset of one million images annotated with scene graphs, facilitating the training and fair comparison of models across diverse and complex scenes. Additionally, we propose SGScore, a novel evaluation metric that leverages chain-of-thought reasoning capabilities of multimodal large language models (LLMs) to assess both object presence and relationship accuracy, offering a more effective measure of factual consistency than traditional metrics like FID and CLIPScore. Building upon this evaluation framework, we develop a scene graph feedback pipeline that iteratively refines generated images by identifying and correcting discrepancies between the scene graph and the image. Extensive experiments demonstrate that Scene-Bench provides a more comprehensive and effective evaluation framework compared to existing benchmarks, particularly for complex scene generation. Furthermore, our feedback strategy significantly enhances the factual consistency of image generation models, advancing the field of controllable image generation.
A Prototype-Based Framework to Design Scalable Heterogeneous SoCs with Fine-Grained DFS
Venue: arXiv cs.AR 2025
Year: 2025

Frameworks for the agile development of modern system-on-chips are crucial to dealing with the complexity of designing such architectures. The open-source Vespa framework for designing large, FPGA-based, multi-core heterogeneous system-on-chips enables a faster and more flexible design space exploration of such architectures and their run-time optimization. Vespa, built on ESP, introduces the capabilities to instantiate multiple replicas of the same accelerator in a single network-on-chip node and to partition the system-on-chips into frequency islands with independent dynamic frequency scaling actuators, as well as a dedicated run-time monitoring infrastructure. Experiments on 4-by-4 tile-based system-on-chips demonstrate the possibility of effectively exploring a multitude of solutions that differ in the replication of accelerators, the clock frequencies of the frequency islands, and the tiles' placement, as well as monitoring a variety of statistics related to the traffic on the interconnect and the accelerators' performance at run time.
Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks
Venue: arXiv cs.LG 2025
Year: 2025

State-space models are a popular statistical framework for analysing sequential data. Within this framework, particle filters are often used to perform inference on non-linear state-space models. We introduce a new method, StateMixNN, that uses a pair of neural networks to learn the proposal distribution and transition distribution of a particle filter. Both distributions are approximated using multivariate Gaussian mixtures. The component means and covariances of these mixtures are learnt as outputs of learned functions. Our method is trained targeting the log-likelihood, thereby requiring only the observation series, and combines the interpretability of state-space models with the flexibility and approximation power of artificial neural networks. The proposed method significantly improves recovery of the hidden state in comparison with the state-of-the-art, showing greater improvement in highly non-linear scenarios.
Space-ground Fluid AI for 6G Edge Intelligence
Venue: arXiv cs.NI 2025
Year: 2025

Edge artificial intelligence (AI) and space-ground integrated networks (SGINs) are two main usage scenarios of the sixth-generation (6G) mobile networks. Edge AI supports pervasive low-latency AI services to users, whereas SGINs provide digital services to spatial, aerial, maritime, and ground users. This article advocates the integration of the two technologies by extending edge AI to space, thereby delivering AI services to every corner of the planet. Beyond a simple combination, our novel framework, called space-ground fluid AI, leverages the predictive mobility of satellites to facilitate fluid horizontal and vertical task/model migration in the networks. This ensures non-disruptive AI service provisioning in spite of the high mobility of satellite servers. The aim of the article is to introduce the (space-ground) fluid AI technology. First, we outline the network architecture and unique characteristics of fluid AI. Then, we delve into three key components of fluid AI, i.e., fluid learning, fluid inference, and fluid model downloading. They share the common feature of coping with satellite mobility via inter-satellite and space-ground cooperation to support AI services. Finally, we discuss the considerations for the real-world deployment of fluid AI and identify further research opportunities.
Do LLMs Really Think Step-by-step In Implicit Reasoning?
Venue: arXiv cs.CL 2025
Year: 2025

It has been well-known that Chain-of-Thought can remarkably enhance LLMs' performance on complex tasks. However, because it also introduces slower inference speeds and higher computational costs, many researches have attempted to use implicit CoT, which does not need LLMs to explicitly generate the intermediate steps. However, the invisible reasoning process leaves us a doubt that, can implicit CoT really be equal to explicit CoT? Therefore, in this study, we address this question through experiments. We probe the information of intermediate steps from the model's hidden states when it is either trained or prompted to perform implicit CoT. The results surprisingly indicate that when prompted, LLMs hardly think about intermediate steps, suggesting they may just rely on experience rather than strict step-by-step reasoning. But when trained, they indeed calculate intermediate steps. Moreover, in both situations, we find the effect of using implicit CoT is susceptible to the format of the problem, reaffirming the current deficiency of implicit CoT.
Stylus: Repurposing Stable Diffusion for Training-Free Music Style Transfer on Mel-Spectrograms
Venue: arXiv cs.SD 2025
Year: 2025

Music style transfer enables personalized music creation by blending the structure of a source with the stylistic attributes of a reference. Existing text-conditioned and diffusion-based approaches show promise but often require paired datasets, extensive training, or detailed annotations. We present Stylus, a training-free framework that repurposes a pre-trained Stable Diffusion model for music style transfer in the mel-spectrogram domain. Stylus manipulates self-attention by injecting style key-value features while preserving source queries to maintain musical structure. To improve fidelity, we introduce a phase-preserving reconstruction strategy that avoids artifacts from Griffin-Lim reconstruction, and we adopt classifier-free-guidance-inspired control for adjustable stylization and multi-style blending. In extensive evaluations, Stylus outperforms state-of-the-art baselines, achieving 34.1% higher content preservation and 25.7% better perceptual quality without any additional training.
Multi-Robot Scan-n-Print for Wire Arc Additive Manufacturing
Venue: arXiv cs.RO 2025
Year: 2025

Robotic Wire Arc Additive Manufacturing (WAAM) is a metal additive manufacturing technology, offering flexible 3D printing while ensuring high quality near-net-shape final parts. However, WAAM also suffers from geometric imprecision, especially for low-melting-point metal such as aluminum alloys. In this paper, we present a multi-robot framework for WAAM process monitoring and control. We consider a three-robot setup: a 6-dof welding robot, a 2-dof trunnion platform, and a 6-dof sensing robot with a wrist-mounted laser line scanner measuring the printed part height profile. The welding parameters, including the wire feed rate, are held constant based on the materials used, so the control input is the robot path speed. The measured output is the part height profile. The planning phase decomposes the target shape into slices of uniform height. During runtime, the sensing robot scans each printed layer, and the robot path speed for the next layer is adjusted based on the deviation from the desired profile. The adjustment is based on an identified model correlating the path speed to change in height. The control architecture coordinates the synchronous motion and data acquisition between all robots and sensors. Using a three-robot WAAM testbed, we demonstrate significant improvements of the closed loop scan-n-print approach over the current open loop result on both a flat wall and a more complex turbine blade shape.
From Dashcam Videos to Driving Simulations: Stress Testing Automated Vehicles against Rare Events
Venue: arXiv cs.CV 2025
Year: 2025

Testing Automated Driving Systems (ADS) in simulation with realistic driving scenarios is important for verifying their performance. However, converting real-world driving videos into simulation scenarios is a significant challenge due to the complexity of interpreting high-dimensional video data and the time-consuming nature of precise manual scenario reconstruction. In this work, we propose a novel framework that automates the conversion of real-world car crash videos into detailed simulation scenarios for ADS testing. Our approach leverages prompt-engineered Video Language Models(VLM) to transform dashcam footage into SCENIC scripts, which define the environment and driving behaviors in the CARLA simulator, enabling the generation of realistic simulation scenarios. Importantly, rather than solely aiming for one-to-one scenario reconstruction, our framework focuses on capturing the essential driving behaviors from the original video while offering flexibility in parameters such as weather or road conditions to facilitate search-based testing. Additionally, we introduce a similarity metric that helps iteratively refine the generated scenario through feedback by comparing key features of driving behaviors between the real and simulated videos. Our preliminary results demonstrate substantial time efficiency, finishing the real-to-sim conversion in minutes with full automation and no human intervention, while maintaining high fidelity to the original driving events.
Soft-TransFormers for Continual Learning
Venue: arXiv cs.LG 2025
Year: 2025

Inspired by the Well-initialized Lottery Ticket Hypothesis (WLTH), which provides suboptimal fine-tuning solutions, we propose a novel fully fine-tuned continual learning (CL) method referred to as Soft-TransFormers (Soft-TF). Soft-TF sequentially learns and selects an optimal soft-network for each task. During sequential training in CL, a well-initialized Soft-TF mask optimizes the weights of sparse layers to obtain task-adaptive soft (real-valued) networks, while keeping the well-pre-trained layer parameters frozen. In inference, the identified task-adaptive network of Soft-TF masks the parameters of the pre-trained network, mapping to an optimal solution for each task and minimizing Catastrophic Forgetting (CF) - the soft-masking preserves the knowledge of the pre-trained network. Extensive experiments on the Vision Transformer (ViT) and the Language Transformer (Bert) demonstrate the effectiveness of Soft-TF, achieving state-of-the-art performance across Vision and Language Class Incremental Learning (CIL) scenarios.
From Collapse to Stability: A Knowledge-Driven Ensemble Framework for Scaling Up Click-Through Rate Prediction Models
Venue: arXiv cs.IR 2025
Year: 2025

Click-through rate (CTR) prediction plays a crucial role in modern recommender systems. While many existing methods utilize ensemble networks to improve CTR model performance, they typically restrict the ensemble to only two or three sub-networks. Whether increasing the number of sub-networks consistently enhances CTR model performance to align with scaling laws remains unclear. In this paper, we investigate larger ensemble networks and find three inherent limitations in commonly used ensemble methods: (1) performance degradation as the number of sub-networks increases; (2) sharp declines and high variance in sub-network performance; and (3) significant discrepancies between sub-network and ensemble predictions. Meanwhile, we analyze the underlying causes of these limitations from the perspective of dimensional collapse: the collapse within sub-networks becomes increasingly severe as the number of sub-networks grows, leading to a lower knowledge abundance. In this paper, we employ knowledge transfer methods, such as Knowledge Distillation (KD) and Deep Mutual Learning (DML), to address the aforementioned limitations. We find that KD enables CTR models to better follow scaling laws, while DML reduces variance among sub-networks and minimizes discrepancies with ensemble predictions. Furthermore, by combining KD and DML, we propose a model-agnostic and hyperparameter-free Knowledge-Driven Ensemble Framework (KDEF) for CTR Prediction.