OpenRaiser/Intern-Atlas huggingface dataset:
Contrastive Time Series Forecasting with Anomalies
Venue: arXiv cs.LG 2025
Year: 2025

Time series forecasting predicts future values from past data. In real-world settings, some anomalous events have lasting effects and influence the forecast, while others are short-lived and should be ignored. Standard forecasting models fail to make this distinction, often either overreacting to noise or missing persistent shifts. We propose Co-TSFA (Contrastive Time Series Forecasting with Anomalies), a regularization framework that learns when to ignore anomalies and when to respond. Co-TSFA generates input-only and input-output augmentations to model forecast-irrelevant and forecast-relevant anomalies, and introduces a latent-output alignment loss that ties representation changes to forecast changes. This encourages invariance to irrelevant perturbations while preserving sensitivity to meaningful distributional shifts. Experiments on the Traffic and Electricity benchmarks, as well as on a real-world cash-demand dataset, demonstrate that Co-TSFA improves performance under anomalous conditions while maintaining accuracy on normal data. An anonymized GitHub repository with the implementation of Co-TSFA is provided and will be made public upon acceptance.
Mini-SFC: A Comprehensive Simulation Framework for Orchestration and Management of Service Function Chains
Venue: arXiv cs.SE 2025
Year: 2025

In the continuously evolving cloud computing and network environment, service function chain (SFC) plays a crucial role in implementing complex services in the network with its flexible deployment capabilities. To address the limitations of existing SFC simulation tools, this paper introduces Mini-SFC, a modular simulation framework that supports both numerical and container-based virtual simulations, while also supporting online dynamic topology adjustments. As an open-source platform emphasizing user-friendliness, Mini-SFC facilitates rapid algorithm verification and realistic service deployment validation. By simplifying module design and providing standardized solver interfaces, Mini-SFC significantly shortens the learning curve for researchers and enhances the flexibility and scalability required for advanced SFC management and optimization. For readers interested in exploring or utilizing Mini-SFC, more information is available on the official project page.
Visualizing token importance for black-box language models
Venue: arXiv cs.CL 2025
Year: 2025

We consider the problem of auditing black-box large language models (LLMs) to ensure they behave reliably when deployed in production settings, particularly in high-stakes domains such as legal, medical, and regulatory compliance. Existing approaches for LLM auditing often focus on isolated aspects of model behavior, such as detecting specific biases or evaluating fairness. We are interested in a more general question -- can we understand how the outputs of black-box LLMs depend on each input token? There is a critical need to have such tools in real-world applications that rely on inaccessible API endpoints to language models. However, this is a highly non-trivial problem, as LLMs are stochastic functions (i.e. two outputs will be different by chance), while computing prompt-level gradients to approximate input sensitivity is infeasible. To address this, we propose Distribution-Based Sensitivity Analysis (DBSA), a lightweight model-agnostic procedure to evaluate the sensitivity of the output of a language model for each input token, without making any distributional assumptions about the LLM. DBSA is developed as a practical tool for practitioners, enabling quick, plug-and-play visual exploration of LLMs reliance on specific input tokens. Through illustrative examples, we demonstrate how DBSA can enable users to inspect LLM inputs and find sensitivities that may be overlooked by existing LLM interpretability methods.
In-Context Learning for Seismic Data Processing
Venue: arXiv cs.CV 2025
Year: 2025

Seismic processing transforms raw data into subsurface images essential for geophysical applications. Traditional methods face challenges, such as noisy data, and manual parameter tuning, among others. Recently deep learning approaches have proposed alternative solutions to some of these problems. However, important challenges of existing deep learning approaches are spatially inconsistent results across neighboring seismic gathers and lack of user-control. We address these limitations by introducing ContextSeisNet, an in-context learning model, to seismic demultiple processing. Our approach conditions predictions on a support set of spatially related example pairs: neighboring common-depth point gathers from the same seismic line and their corresponding labels. This allows the model to learn task-specific processing behavior at inference time by observing how similar gathers should be processed, without any retraining. This method provides both flexibility through user-defined examples and improved lateral consistency across seismic lines. On synthetic data, ContextSeisNet outperforms a U-Net baseline quantitatively and demonstrates enhanced spatial coherence between neighboring gathers. On field data, our model achieves superior lateral consistency compared to both traditional Radon demultiple and the U-Net baseline. Relative to the U-Net, ContextSeisNet also delivers improved near-offset performance and more complete multiple removal. Notably, ContextSeisNet achieves comparable field data performance despite being trained on 90% less data, demonstrating substantial data efficiency. These results establish ContextSeisNet as a practical approach for spatially consistent seismic demultiple with potential applicability to other seismic processing tasks.
Context-Dependent Effects and Concurrency in Guarded Interaction Trees
Venue: arXiv cs.LO 2025
Year: 2025

Guarded Interaction Trees are a structure and a fully formalized framework for representing higher-order computations with higher-order effects in Rocq. We present an extension of Guarded Interaction Trees to support formal reasoning about context-dependent effects. That is, effects whose behaviors depend on the evaluation context, e.g., call/cc, shift and reset. Using and reasoning about such effects is challenging since certain compositionality principles no longer hold in the presence of such effects. For example, the so-called ``bind rule'' in modern program logics is no longer valid. The goal of our extension is to support representation and reasoning about context-dependent effects in the most painless way possible. To that end, our extension is conservative: the reasoning principles for context-independent effects remain the same. We use it to give direct-style denotational semantics for higher-order programming languages with call/cc and with delimited continuations. We extend the program logic for Guarded Interaction Trees to account for context-dependent effects, and we use the program logic to prove that the denotational semantics is adequate with respect to the operational semantics. Additionally, we retain the ability to combine multiple effects in a modular way, which we demonstrate by showing type soundness for safe interoperability of a programming language with delimited continuations and a programming language with higher-order store. Furthermore, as another contribution, in addition to context-dependent effects, we show how to extend Guarded Interaction Trees with preemptive concurrency. To support implementation and verification of concurrent data structures and algorithms in the presence of preemptive concurrency one requires atomic state modification operations, e.g., compare-and-exchange.
Variants of Wythoff game with terminal positions or blocking maneuvers
Venue: arXiv cs.DM 2025
Year: 2025

We show how the software Walnut can be used to obtain concise proofs of results concerning variants of the famous Wythoff game, in which blocking maneuvers or terminal positions are added, as discussed respectively by Larsson (2011) and Komak et al. (2025). Our approach provides automatic proofs that both confirm and extend their results, and the same techniques apply to newly introduced variants as well. Then, using classic techniques, we obtain new recursive and morphic characterizations of Wythoff-type games where the set of terminal positions $(x,y)$ satisfy $x+y\le\ell$. The use of Walnut in combinatorial game theory is relatively recent, and only a few examples have been explored so far. The Wythoff game, being directly connected to the Fibonacci numeration system, proves especially well-suited to this kind of approach. It permits us to solve instances for a fixed value of a parameter.
Stable low-rank matrix recovery from 3-designs
Venue: arXiv cs.IT 2025
Year: 2025

We study the recovery of low-rank Hermitian matrices from rank-one measurements obtained by uniform sampling from complex projective 3-designs, using nuclear-norm minimization. This framework includes phase retrieval as a special case via the PhaseLift method. In general, complex projective $t$-designs provide a practical means of partially derandomizing Gaussian measurement models. While near-optimal recovery guarantees are known for $4$-designs, and it is known that $2$-designs do not permit recovery with a subquadratic number of measurements, the case of $3$-designs has remained open. In this work, we close this gap by establishing recovery guarantees for (exact and approximate) $3$-designs that parallel the best-known results for $4$-designs. In particular, we derive bounds on the number of measurements sufficient for stable and robust low-rank recovery via nuclear-norm minimization. Our results are especially relevant in practice, as explicit constructions of $4$-designs are significantly more challenging than those of $3$-designs.
Cross-modal Context-aware Learning for Visual Prompt Guided Multimodal Image Understanding in Remote Sensing
Venue: arXiv cs.CV 2025
Year: 2025

Recent advances in image understanding have enabled methods that leverage large language models for multimodal reasoning in remote sensing. However, existing approaches still struggle to steer models to the user-relevant regions when only simple, generic text prompts are available. Moreover, in large-scale aerial imagery many objects exhibit highly similar visual appearances and carry rich inter-object relationships, which further complicates accurate recognition. To address these challenges, we propose Cross-modal Context-aware Learning for Visual Prompt-Guided Multimodal Image Understanding (CLV-Net). CLV-Net lets users supply a simple visual cue, a bounding box, to indicate a region of interest, and uses that cue to guide the model to generate correlated segmentation masks and captions that faithfully reflect user intent. Central to our design is a Context-Aware Mask Decoder that models and integrates inter-object relationships to strengthen target representations and improve mask quality. In addition, we introduce a Semantic and Relationship Alignment module: a Cross-modal Semantic Consistency Loss enhances fine-grained discrimination among visually similar targets, while a Relationship Consistency Loss enforces alignment between textual relations and visual interactions. Comprehensive experiments on two benchmark datasets show that CLV-Net outperforms existing methods and establishes new state-of-the-art results. The model effectively captures user intent and produces precise, intention-aligned multimodal outputs.
ECCO: Leveraging Cross-Camera Correlations for Efficient Live Video Continuous Learning
Venue: arXiv cs.DC 2025
Year: 2025

Recent advances in video analytics address real-time data drift by continuously retraining specialized, lightweight DNN models for individual cameras. However, the current practice of retraining a separate model for each camera suffers from high compute and communication costs, making it unscalable. We present ECCO, a new video analytics framework designed for resource-efficient continuous learning. The key insight is that the data drift, which necessitates model retraining, often shows temporal and spatial correlations across nearby cameras. By identifying cameras that experience similar drift and retraining a shared model for them, ECCO can substantially reduce the associated compute and communication costs. Specifically, ECCO introduces: (i) a lightweight grouping algorithm that dynamically forms and updates camera groups; (ii) a GPU allocator that dynamically assigns GPU resources across different groups to improve retraining accuracy and ensure fairness; and (iii) a transmission controller at each camera that configures frame sampling and coordinates bandwidth sharing with other cameras based on its assigned GPU resources. We conducted extensive evaluations on three distinctive datasets for two vision tasks. Compared to leading baselines, ECCO improves retraining accuracy by 6.7%-18.1% using the same compute and communication resources, or supports 3.3 times more concurrent cameras at the same accuracy.
Structure From Tracking: Distilling Structure-Preserving Motion for Video Generation
Venue: arXiv cs.CV 2025
Year: 2025

Reality is a dance between rigid constraints and deformable structures. For video models, that means generating motion that preserves fidelity as well as structure. Despite progress in diffusion models, producing realistic structure-preserving motion remains challenging, especially for articulated and deformable objects such as humans and animals. Scaling training data alone, so far, has failed to resolve physically implausible transitions. Existing approaches rely on conditioning with noisy motion representations, such as optical flow or skeletons extracted using an external imperfect model. To address these challenges, we introduce an algorithm to distill structure-preserving motion priors from an autoregressive video tracking model (SAM2) into a bidirectional video diffusion model (CogVideoX). With our method, we train SAM2VideoX, which contains two innovations: (1) a bidirectional feature fusion module that extracts global structure-preserving motion priors from a recurrent model like SAM2; (2) a Local Gram Flow loss that aligns how local features move together. Experiments on VBench and in human studies show that SAM2VideoX delivers consistent gains (+2.60\% on VBench, 21-22\% lower FVD, and 71.4\% human preference) over prior baselines. Specifically, on VBench, we achieve 95.51\%, surpassing REPA (92.91\%) by 2.60\%, and reduce FVD to 360.57, a 21.20\% and 22.46\% improvement over REPA- and LoRA-finetuning, respectively. The project website can be found at https://sam2videox.github.io/ .
Particulate: Feed-Forward 3D Object Articulation
Venue: arXiv cs.CV 2025
Year: 2025

We present Particulate, a feed-forward approach that, given a single static 3D mesh of an everyday object, directly infers all attributes of the underlying articulated structure, including its 3D parts, kinematic structure, and motion constraints. At its core is a transformer network, Part Articulation Transformer, which processes a point cloud of the input mesh using a flexible and scalable architecture to predict all the aforementioned attributes with native multi-joint support. We train the network end-to-end on a diverse collection of articulated 3D assets from public datasets. During inference, Particulate lifts the network's feed-forward prediction to the input mesh, yielding a fully articulated 3D model in seconds, much faster than prior approaches that require per-object optimization. Particulate can also accurately infer the articulated structure of AI-generated 3D assets, enabling full-fledged extraction of articulated 3D objects from a single (real or synthetic) image when combined with an off-the-shelf image-to-3D generator. We further introduce a new challenging benchmark for 3D articulation estimation curated from high-quality public 3D assets, and redesign the evaluation protocol to be more consistent with human preferences. Quantitative and qualitative results show that Particulate significantly outperforms state-of-the-art approaches.
AI Integration In ERP Evaluation Across Trends and Architectures
Venue: arXiv cs.CY 2025
Year: 2025

The incorporation of Artificial Intelligence (AI) into Enterprise Resource Planning (ERP) is a dramatic transition from static, on-premises systems to systems that can adapt and operate in cloud-native architectures. Cloud ERP solutions like Workday illustrate this evolution by incorporating machine learning, deep learning, and natural language processing into a centralized data-driven ecosystem. As the complexity of AI-driven ERP solutions expands, traditional evaluation frameworks that look at cost, function, and user satisfaction suffer from a lack of consideration for algorithmic transparency, adaptability, or ethics. This review will systematically investigate the latest trends, models of computing architecture, and analytical methods applied in assessing the performance of AI-integrated ERP services, specifically on cloud-based platforms. Based on academic and industry sources, the paper distills current research in line with architectural integration, analytical methodologies, and organizational impact. It identifies critical performance metrics and emphasizes the absence of any standard assessment frameworks or AI-aware systems capable of evaluating automation efficiency, security concerns as well as flexible learning modes. We put forward a theoretical model that brings AI-enabled capabilities -- such as predictive intelligence or adaptive automation -- into alignment with metrics in performance assessment for ERPs. By combining current literature and identifying major gaps in research, this paper attempts to present a complete picture of how innovations in AI are changing ERP evaluation. These research and methodological findings are intended to steer researchers and practitioners towards developing rigorous, data-driven assessment approaches, aligning with the fast-developing world of intelligent self-optimizing enterprise ecosystems
Enhancing Geo-localization for Crowdsourced Flood Imagery via LLM-Guided Attention
Venue: arXiv cs.CL 2025
Year: 2025

Crowdsourced street-view imagery from social media provides real-time visual evidence of urban flooding and other crisis events, yet it often lacks reliable geographic metadata for emergency response. Existing image geo-localization approaches, also known as Visual Place Recognition (VPR) models, exhibit substantial performance degradation when applied to such imagery due to visual distortions and domain shifts in cross-source scenarios. This paper presents VPR-AttLLM, a model-agnostic framework that integrates the semantic reasoning and geo-knowledge of Large Language Models (LLMs) into established VPR pipelines through attention-guided descriptor enhancement. By leveraging LLMs to identify location-informative regions within the city context and suppress visual noise, VPR-AttLLM improves retrieval performance without requiring model retraining or additional data. Comprehensive evaluations are conducted on extended benchmarks including SF-XL enriched with real social-media flood images, synthetic flooding scenarios over established query sets and Mapillary photos, and a new HK-URBAN dataset capturing morphologically distinct cityscapes. Integrating VPR-AttLLM with three state-of-the-art VPR models-CosPlace, EigenPlaces, and SALAD-consistently improves recall performance, yielding relative gains typically between 1-3% and reaching up to 8% on the most challenging real flood imagery. Beyond measurable gains in retrieval accuracy, this study establishes a generalizable paradigm for LLM-guided multimodal fusion in visual retrieval systems. By embedding principles from urban perception theory into attention mechanisms, VPR-AttLLM bridges human-like spatial reasoning with modern VPR architectures. Its plug-and-play design, strong cross-source robustness, and interpretability highlight its potential for scalable urban monitoring and rapid geo-localization of crowdsourced crisis imagery.
A Reproducible Workflow for Scraping, Structuring, and Segmenting Legacy Archaeological Artifact Images
Venue: arXiv cs.CY 2025
Year: 2025

This technical note presents a reproducible workflow for converting a legacy archaeological image collection into a structured and segmentation ready dataset. The case study focuses on the Lower Palaeolithic hand axe and biface collection curated by the Archaeology Data Service (ADS), a dataset that provides thousands of standardised photographs but no mechanism for bulk download or automated processing. To address this, two open source tools were developed: a web scraping script that retrieves all record pages, extracts associated metadata, and downloads the available images while respecting ADS Terms of Use and ethical scraping guidelines; and an image processing pipeline that renames files using UUIDs, generates binary masks and bounding boxes through classical computer vision, and stores all derived information in a COCO compatible Json file enriched with archaeological metadata. The original images are not redistributed, and only derived products such as masks, outlines, and annotations are shared. Together, these components provide a lightweight and reusable approach for transforming web based archaeological image collections into machine learning friendly formats, facilitating downstream analysis and contributing to more reproducible research practices in digital archaeology.
Trust, Usefulness, and Dependency on AI in Programming: A Hierarchical Clustering Approach
Venue: arXiv cs.CY 2025
Year: 2025

While AI tools are transforming programming education, their adoption in underrepresented countries remains insufficiently studied. Understanding students' trust, perceived usefulness, and dependency on AI tools is essential to improving their integration into education. For these purposes, this study surveyed 508 first-year programming students in Pampanga, Philippines and analyzed their perceptions using hierarchical clustering. Results showed four unique student profiles with varying in trust and usage intensity. While students acknowledged AI tools' benefits, dependency remained low due to limited infrastructure and insufficient exposure. High-frequency users did not necessarily report greater trust or usefulness which may indicates a complex relationship between usage patterns and perception. This study recommends that to maximize AI's educational impact, targeted interventions such as infrastructure development, training programs, and curriculum integration are necessary. This study provides empirical insights to support equitable and effective AI adoption in programming education within developing regions.
Performance and Efficiency of Climate In-Situ Data Reconstruction: Why Optimized IDW Outperforms kriging and Implicit Neural Representation
Venue: arXiv cs.LG 2025
Year: 2025

This study evaluates three reconstruction methods for sparse climate data: the simple inverse distance weighting (IDW), the statistically grounded ordinary kriging (OK), and the advanced implicit neural representation model (MMGN architecture). All methods were optimized through hyper-parameter tuning using validation splits. An extensive set of experiments was conducted, followed by a comprehensive statistical analysis. The results demonstrate the superiority of the simple IDW method over the other reference methods in terms of both reconstruction accuracy and computational efficiency. IDW achieved the lowest RMSE ($3.00 \pm 1.93$), MAE ($1.32 \pm 0.77$), and $Δ_{MAX}$ ($24.06 \pm 17.15$), as well as the highest $R^2$ ($0.68 \pm 0.16$), across 100 randomly sampled sparse datasets from the ECA\&D database. Differences in RMSE, MAE, and $R^2$ were statistically significant and exhibited moderate to large effect sizes. The Dunn post-hoc test further confirmed the consistent superiority of IDW across all evaluated quality measures [...]