OpenRaiser/Intern-Atlas huggingface dataset:
Evaluating YOLO Architectures: Implications for Real-Time Vehicle Detection in Urban Environments of Bangladesh
Venue: arXiv cs.CV 2025
Year: 2025

Vehicle detection systems trained on Non-Bangladeshi datasets struggle to accurately identify local vehicle types in Bangladesh's unique road environments, creating critical gaps in autonomous driving technology for developing regions. This study evaluates six YOLO model variants on a custom dataset featuring 29 distinct vehicle classes, including region-specific vehicles such as ``Desi Nosimon'', ``Leguna'', ``Battery Rickshaw'', and ``CNG''. The dataset comprises high-resolution images (1920x1080) captured across various Bangladeshi roads using mobile phone cameras and manually annotated using LabelImg with YOLO format bounding boxes. Performance evaluation revealed YOLOv11x as the top performer, achieving 63.7\% mAP@0.5, 43.8\% mAP@0.5:0.95, 61.4\% recall, and 61.6\% F1-score, though requiring 45.8 milliseconds per image for inference. Medium variants (YOLOv8m, YOLOv11m) struck an optimal balance, delivering robust detection performance with mAP@0.5 values of 62.5\% and 61.8\% respectively, while maintaining moderate inference times around 14-15 milliseconds. The study identified significant detection challenges for rare vehicle classes, with Construction Vehicles and Desi Nosimons showing near-zero accuracy due to dataset imbalances and insufficient training samples. Confusion matrices revealed frequent misclassifications between visually similar vehicles, particularly Mini Trucks versus Mini Covered Vans. This research provides a foundation for developing robust object detection systems specifically adapted to Bangladesh traffic conditions, addressing critical needs in autonomous vehicle technology advancement for developing regions where conventional generic-trained models fail to perform adequately.
MSRFormer: Road Network Representation Learning using Multi-scale Feature Fusion of Heterogeneous Spatial Interactions
Venue: arXiv cs.AI 2025
Year: 2025

Transforming road network data into vector representations using deep learning has proven effective for road network analysis. However, urban road networks' heterogeneous and hierarchical nature poses challenges for accurate representation learning. Graph neural networks, which aggregate features from neighboring nodes, often struggle due to their homogeneity assumption and focus on a single structural scale. To address these issues, this paper presents MSRFormer, a novel road network representation learning framework that integrates multi-scale spatial interactions by addressing their flow heterogeneity and long-distance dependencies. It uses spatial flow convolution to extract small-scale features from large trajectory datasets, and identifies scale-dependent spatial interaction regions to capture the spatial structure of road networks and flow heterogeneity. By employing a graph transformer, MSRFormer effectively captures complex spatial dependencies across multiple scales. The spatial interaction features are fused using residual connections, which are fed to a contrastive learning algorithm to derive the final road network representation. Validation on two real-world datasets demonstrates that MSRFormer outperforms baseline methods in two road network analysis tasks. The performance gains of MSRFormer suggest the traffic-related task benefits more from incorporating trajectory data, also resulting in greater improvements in complex road network structures with up to 16% improvements compared to the most competitive baseline method. This research provides a practical framework for developing task-agnostic road network representation models and highlights distinct association patterns of the interplay between scale effects and flow heterogeneity of spatial interactions.
KnowHow: Automatically Applying High-Level CTI Knowledge for Interpretable and Accurate Provenance Analysis
Venue: arXiv cs.CR 2025
Year: 2025

High-level natural language knowledge in CTI reports, such as the ATT&CK framework, is beneficial to counter APT attacks. However, how to automatically apply the high-level knowledge in CTI reports in realistic attack detection systems, such as provenance analysis systems, is still an open problem. The challenge stems from the semantic gap between the knowledge and the low-level security logs: while the knowledge in CTI reports is written in natural language, attack detection systems can only process low-level system events like file accesses or network IP manipulations. Manual approaches can be labor-intensive and error-prone. In this paper, we propose KnowHow, a CTI-knowledge-driven online provenance analysis approach that can automatically apply high-level attack knowledge from CTI reports written in natural languages to detect low-level system events. The core of KnowHow is a novel attack knowledge representation, gIoC, that represents the subject, object, and actions of attacks. By lifting system identifiers, such as file paths, in system events to natural language terms, KnowHow can match system events to gIoC and further match them to techniques described in natural languages. Finally, based on the techniques matched to system events, KnowHow reasons about the temporal logic of attack steps and detects potential APT attacks in system events. Our evaluation shows that KnowHow can accurately detect all 16 APT campaigns in the open-source and industrial datasets, while existing approaches all introduce large numbers of false positives. Meanwhile, our evaluation also shows that KnowHow reduces at most 90% of node-level false positives while having a higher node-level recall and is robust against several unknown attacks and mimicry attacks.
Towards Meta-Cognitive Knowledge Editing for Multimodal LLMs
Venue: arXiv cs.AI 2025
Year: 2025

Knowledge editing enables multimodal large language models (MLLMs) to efficiently update outdated or incorrect information. However, existing benchmarks primarily emphasize cognitive-level modifications while lacking a focus on deeper meta-cognitive processes. To bridge this gap, we introduce CogEdit, a novel benchmark designed to evaluate MLLMs' meta-cognitive knowledge editing abilities across three levels: (1) Counterfactual-Driven Editing, assessing self-awareness of knowledge correctness changes; (2) Boundary Constraint Editing, ensuring appropriate generalization without unintended interference; and (3) Noise-Robust Editing, promoting reflective evaluation of uncertain information. To advance meta-cognitive editing, we propose MIND (Meta-cognitive INtegrated Dynamic Knowledge Editing), a framework that constructs a meta-knowledge memory for self-awareness, employs game-theoretic interactions to monitor knowledge activation, and incorporates label refinement for noise-robust updates. Extensive experiments show that MIND significantly outperforms existing cognitive editing approaches, achieving strong performance on both traditional and meta-cognitive knowledge editing benchmarks.
Depth-Aware Super-Resolution via Distance-Adaptive Variational Formulation
Venue: arXiv cs.CV 2025
Year: 2025

Single image super-resolution traditionally assumes spatially-invariant degradation models, yet real-world imaging systems exhibit complex distance-dependent effects including atmospheric scattering, depth-of-field variations, and perspective distortions. This fundamental limitation necessitates spatially-adaptive reconstruction strategies that explicitly incorporate geometric scene understanding for optimal performance. We propose a rigorous variational framework that characterizes super-resolution as a spatially-varying inverse problem, formulating the degradation operator as a pseudodifferential operator with distance-dependent spectral characteristics that enable theoretical analysis of reconstruction limits across depth ranges. Our neural architecture implements discrete gradient flow dynamics through cascaded residual blocks with depth-conditional convolution kernels, ensuring convergence to stationary points of the theoretical energy functional while incorporating learned distance-adaptive regularization terms that dynamically adjust smoothness constraints based on local geometric structure. Spectral constraints derived from atmospheric scattering theory prevent bandwidth violations and noise amplification in far-field regions, while adaptive kernel generation networks learn continuous mappings from depth to reconstruction filters. Comprehensive evaluation across five benchmark datasets demonstrates state-of-the-art performance, achieving 36.89/0.9516 and 30.54/0.8721 PSNR/SSIM at 2 and 4 scales on KITTI outdoor scenes, outperforming existing methods by 0.44dB and 0.36dB respectively. This work establishes the first theoretically-grounded distance-adaptive super-resolution framework and demonstrates significant improvements on depth-variant scenarios while maintaining competitive performance across traditional benchmarks.
Unleashing Hierarchical Reasoning: An LLM-Driven Framework for Training-Free Referring Video Object Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Referring Video Object Segmentation (RVOS) aims to segment an object of interest throughout a video based on a language description. The prominent challenge lies in aligning static text with dynamic visual content, particularly when objects exhibiting similar appearances with inconsistent motion and poses. However, current methods often rely on a holistic visual-language fusion that struggles with complex, compositional descriptions. In this paper, we propose \textbf{PARSE-VOS}, a novel, training-free framework powered by Large Language Models (LLMs), for a hierarchical, coarse-to-fine reasoning across text and video domains. Our approach begins by parsing the natural language query into structured semantic commands. Next, we introduce a spatio-temporal grounding module that generates all candidate trajectories for all potential target objects, guided by the parsed semantics. Finally, a hierarchical identification module select the correct target through a two-stage reasoning process: it first performs coarse-grained motion reasoning with an LLM to narrow down candidates; if ambiguity remains, a fine-grained pose verification stage is conditionally triggered to disambiguate. The final output is an accurate segmentation mask for the target object. \textbf{PARSE-VOS} achieved state-of-the-art performance on three major benchmarks: Ref-YouTube-VOS, Ref-DAVIS17, and MeViS.
Tell-Tale Watermarks for Explanatory Reasoning in Synthetic Media Forensics
Venue: arXiv cs.CR 2025
Year: 2025

The rise of synthetic media has blurred the boundary between reality and fabrication under the evolving power of artificial intelligence, fueling an infodemic that erodes public trust in cyberspace. For digital imagery, a multitude of editing applications further complicates the forensic analysis, including semantic edits that alter content, photometric adjustments that recalibrate colour characteristics, and geometric projections that reshape viewpoints. Collectively, these transformations manipulate and control perceptual interpretation of digital imagery. This susceptibility calls for forensic enquiry into reconstructing the chain of events, thereby revealing deeper evidential insight into the presence or absence of criminal intent. This study seeks to address an inverse problem of tracing the underlying generation chain that gives rise to the observed synthetic media. A tell-tale watermarking system is developed for explanatory reasoning over the nature and extent of transformations across the lifecycle of synthetic media. Tell-tale watermarks are tailored to different classes of transformations, responding in a manner that is neither strictly robust nor fragile but instead interpretable. These watermarks function as reference clues that evolve under the same transformation dynamics as the carrier media, leaving interpretable traces when subjected to transformations. Explanatory reasoning is then performed to infer the most plausible account across the combinatorial parameter space of composite transformations. Experimental evaluations demonstrate the validity of tell-tale watermarking with respect to fidelity, synchronicity and traceability.
Real-E: A Foundation Benchmark for Advancing Robust and Generalizable Electricity Forecasting
Venue: arXiv cs.LG 2025
Year: 2025

Energy forecasting is vital for grid reliability and operational efficiency. Although recent advances in time series forecasting have led to progress, existing benchmarks remain limited in spatial and temporal scope and lack multi-energy features. This raises concerns about their reliability and applicability in real-world deployment. To address this, we present the Real-E dataset, covering over 74 power stations across 30+ European countries over a 10-year span with rich metadata. Using Real- E, we conduct an extensive data analysis and benchmark over 20 baselines across various model types. We introduce a new metric to quantify shifts in correlation structures and show that existing methods struggle on our dataset, which exhibits more complex and non-stationary correlation dynamics. Our findings highlight key limitations of current methods and offer a strong empirical basis for building more robust forecasting models
Dual-Mode Deep Anomaly Detection for Medical Manufacturing: Structural Similarity and Feature Distance
Venue: arXiv cs.CV 2025
Year: 2025

Automated visual inspection in medical-device manufacturing faces unique challenges, including extremely low defect rates, limited annotated data, hardware restrictions on production lines, and the need for validated, explainable artificial-intelligence systems. This paper presents two attention-guided autoencoder architectures that address these constraints through complementary anomaly-detection strategies. The first employs a multi-scale structural-similarity (4-MS-SSIM) index for inline inspection, enabling interpretable, real-time defect detection on constrained hardware. The second applies a Mahalanobis-distance analysis of randomly reduced latent features for efficient feature-space monitoring and lifecycle verification. Both approaches share a lightweight backbone optimised for high-resolution imagery for typical manufacturing conditions. Evaluations on the Surface Seal Image (SSI) dataset-representing sterile-barrier packaging inspection-demonstrate that the proposed methods outperform reference baselines, including MOCCA, CPCAE, and RAG-PaDiM, under realistic industrial constraints. Cross-domain validation on the MVTec-Zipper benchmark confirms comparable accuracy to state-of-the-art anomaly-detection methods. The dual-mode framework integrates inline anomaly detection and supervisory monitoring, advancing explainable AI architectures toward greater reliability, observability, and lifecycle monitoring in safety-critical manufacturing environments. To facilitate reproducibility, the source code developed for the experiments has been released in the project repository, while the datasets were obtained from publicly available sources.
time2time: Causal Intervention in Hidden States to Simulate Rare Events in Time Series Foundation Models
Venue: arXiv cs.LG 2025
Year: 2025

While transformer-based foundation models excel at forecasting routine patterns, two questions remain: do they internalize semantic concepts such as market regimes, or merely fit curves? And can their internal representations be leveraged to simulate rare, high-stakes events such as market crashes? To investigate this, we introduce activation transplantation, a causal intervention that manipulates hidden states by imposing the statistical moments of one event (e.g., a historical crash) onto another (e.g., a calm period) during the forward pass. This procedure deterministically steers forecasts: injecting crash semantics induces downturn predictions, while injecting calm semantics suppresses crashes and restores stability. Beyond binary control, we find that models encode a graded notion of event severity, with the latent vector norm directly correlating with the magnitude of systemic shocks. Validated across two architecturally distinct TSFMs, Toto (decoder only) and Chronos (encoder-decoder), our results demonstrate that steerable, semantically grounded representations are a robust property of large time series transformers. Our findings provide evidence for a latent concept space that governs model predictions, shifting interpretability from post-hoc attribution to direct causal intervention, and enabling semantic "what-if" analysis for strategic stress-testing.
A Probabilistic Segment Anything Model for Ambiguity-Aware Medical Image Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Recent advances in promptable segmentation, such as the Segment Anything Model (SAM), have enabled flexible, high-quality mask generation across a wide range of visual domains. However, SAM and similar models remain fundamentally deterministic, producing a single segmentation per object per prompt, and fail to capture the inherent ambiguity present in many real-world tasks. This limitation is particularly troublesome in medical imaging, where multiple plausible segmentations may exist due to annotation uncertainty or inter-expert variability. In this paper, we introduce Probabilistic SAM, a probabilistic extension of SAM that models a distribution over segmentations conditioned on both the input image and prompt. By incorporating a latent variable space and training with a variational objective, our model learns to generate diverse and plausible segmentation masks reflecting the variability in human annotations. The architecture integrates a prior and posterior network into the SAM framework, allowing latent codes to modulate the prompt embeddings during inference. The latent space allows for efficient sampling during inference, enabling uncertainty-aware outputs with minimal overhead. We evaluate Probabilistic SAM on the public LIDC-IDRI lung nodule dataset and demonstrate its ability to produce diverse outputs that align with expert disagreement, outperforming existing probabilistic baselines on uncertainty-aware metrics. Our code is available at: https://github.com/tbwa233/Probabilistic-SAM/.
Subsequence Covers of Words
Venue: arXiv cs.DS 2025
Year: 2025

We introduce subsequence covers (s-covers, in short), a new type of covers of a word. A word $C$ is an s-cover of a word $S$ if the occurrences of $C$ in $S$ as subsequences cover all the positions in $S$. The s-covers seem to be computationally much harder than standard covers of words (cf. Apostolico et al., Inf. Process. Lett. 1991), but, on the other hand, much easier than the related shuffle powers (Warmuth and Haussler, J. Comput. Syst. Sci. 1984). We give a linear-time algorithm for testing if a candidate word $C$ is an s-cover of a word $S$ over a polynomially-bounded integer alphabet. We also give an algorithm for finding a shortest s-cover of a word $S$, which in the case of a constant-sized alphabet, also runs in linear time. The words without proper s-cover are called s-primitive. We complement our algorithmic results with explicit lower and an upper bound on the length of a longest s-primitive word. Both bounds are exponential in the size of the alphabet. The upper bound presented here improves the bound given in the conference version of this paper [SPIRE 2022].
LatinX: Aligning a Multilingual TTS Model with Direct Preference Optimization
Venue: arXiv cs.CL 2025
Year: 2025

We present LatinX, a multilingual text-to-speech (TTS) model for cascaded speech-to-speech translation that preserves the source speaker's identity across languages. LatinX is a 12-layer decoder-only Transformer trained in three stages: (i) pre-training for text-to-audio mapping, (ii) supervised fine-tuning for zero-shot voice cloning, and (iii) alignment with Direct Preference Optimization (DPO) using automatically labeled pairs based on Word Error Rate (WER) and speaker-similarity metrics. Trained on English and Romance languages with emphasis on Portuguese, LatinX with DPO consistently reduces WER and improves objective similarity over the fine-tuned baseline. Human evaluations further indicate stronger perceived speaker similarity than a strong baseline (XTTSv2), revealing gaps between objective and subjective measures. We provide cross-lingual analyses and discuss balanced preference signals and lower-latency architectures as future work.
Learning to Construct Knowledge through Sparse Reference Selection with Reinforcement Learning
Venue: arXiv cs.LG 2025
Year: 2025

The rapid expansion of scientific literature makes it increasingly difficult to acquire new knowledge, particularly in specialized domains where reasoning is complex, full-text access is restricted, and target references are sparse among a large set of candidates. We present a Deep Reinforcement Learning framework for sparse reference selection that emulates human knowledge construction, prioritizing which papers to read under limited time and cost. Evaluated on drug--gene relation discovery with access restricted to titles and abstracts, our approach demonstrates that both humans and machines can construct knowledge effectively from partial information.
On-Dyn-CDA: A Real-Time Cost-Driven Task Offloading Algorithm for Vehicular Networks with Reduced Latency and Task Loss
Venue: arXiv cs.NI 2025
Year: 2025

Real-time task processing is a critical challenge in vehicular networks, where achieving low latency and minimizing dropped task ratio depend on efficient task execution. Our primary objective is to maximize the number of completed tasks while minimizing overall latency, with a particular focus on reducing number of dropped tasks. To this end, we investigate both static and dynamic versions of an optimization algorithm. The static version assumes full task availability, while the dynamic version manages tasks as they arrive. We also distinguish between online and offline cases: the online version incorporates execution time into the offloading decision process, whereas the offline version excludes it, serving as a theoretical benchmark for optimal performance. We evaluate our proposed Online Dynamic Cost-Driven Algorithm (On-Dyn-CDA) against these baselines. Notably, the static Particle Swarm Optimization (PSO) baseline assumes all tasks are transferred to the RSU and processed by the MEC, and its offline version disregards execution time, making it infeasible for real-time applications despite its optimal performance in theory. Our novel On-Dyn-CDA completes execution in just 0.05 seconds under the most complex scenario, compared to 1330.05 seconds required by Dynamic PSO. It also outperforms Dynamic PSO by 3.42% in task loss and achieves a 29.22% reduction in average latency in complex scenarios. Furthermore, it requires neither a dataset nor a training phase, and its low computational complexity ensures efficiency and scalability in dynamic environments.