OpenRaiser/Intern-Atlas huggingface dataset:
BrainHGT: A Hierarchical Graph Transformer for Interpretable Brain Network Analysis
Venue: arXiv cs.LG 2025
Year: 2025

Graph Transformer shows remarkable potential in brain network analysis due to its ability to model graph structures and complex node relationships. Most existing methods typically model the brain as a flat network, ignoring its modular structure, and their attention mechanisms treat all brain region connections equally, ignoring distance-related node connection patterns. However, brain information processing is a hierarchical process that involves local and long-range interactions between brain regions, interactions between regions and sub-functional modules, and interactions among functional modules themselves. This hierarchical interaction mechanism enables the brain to efficiently integrate local computations and global information flow, supporting the execution of complex cognitive functions. To address this issue, we propose BrainHGT, a hierarchical Graph Transformer that simulates the brain's natural information processing from local regions to global communities. Specifically, we design a novel long-short range attention encoder that utilizes parallel pathways to handle dense local interactions and sparse long-range connections, thereby effectively alleviating the over-globalizing issue. To further capture the brain's modular architecture, we designe a prior-guided clustering module that utilizes a cross-attention mechanism to group brain regions into functional communities and leverage neuroanatomical prior to guide the clustering process, thereby improving the biological plausibility and interpretability. Experimental results indicate that our proposed method significantly improves performance of disease identification, and can reliably capture the sub-functional modules of the brain, demonstrating its interpretability.
Robustness of Structured Data Extraction from Perspectively Distorted Documents
Venue: arXiv cs.CV 2025
Year: 2025

Optical Character Recognition (OCR) for data extraction from documents is essential to intelligent informatics, such as digitizing medical records and recognizing road signs. Multi-modal Large Language Models (LLMs) can solve this task and have shown remarkable performance. Recently, it has been noticed that the accuracy of data extraction by multi-modal LLMs can be affected when in-plane rotations are present in the documents. However, real-world document images are usually not only in-plane rotated but also perspectively distorted. This study investigates the impacts of such perturbations on the data extraction accuracy for the state-of-the-art model, Gemini-1.5-pro. Because perspective distortions have a high degree of freedom, designing experiments in the same manner as single-parametric rotations is difficult. We observed typical distortions of document images and showed that most of them approximately follow an isosceles-trapezoidal transformation, which allows us to evaluate distortions with a small number of parameters. We were able to reduce the number of independent parameters from eight to two, i.e. rotation angle and distortion ratio. Then, specific entities were extracted from synthetically generated sample documents with varying these parameters. As the performance of LLMs, we evaluated not only a character-recognition accuracy but also a structure-recognition accuracy. Whereas the former represents the classical indicators for optical character recognition, the latter is related to the correctness of reading order. In particular, the structure-recognition accuracy was found to be significantly degraded by document distortion. In addition, we found that this accuracy can be improved by a simple rotational correction. This insight will contribute to the practical use of multi-modal LLMs for OCR tasks.
Uncertainty Removal in Verification of Nonlinear Systems against Signal Temporal Logic via Incremental Reachability Analysis
Venue: arXiv cs.LO 2025
Year: 2025

A framework is presented for the verification of Signal Temporal Logic (STL) specifications over continuous-time nonlinear systems under uncertainty. Based on reachability analysis, the proposed method addresses indeterminate satisfaction caused by over-approximated reachable sets or incomplete simulations. STL semantics is extended via Boolean interval arithmetic, enabling the decomposition of satisfaction signals into unitary components with traceable uncertainty markers. These are propagated through the satisfaction tree, supporting precise identification even in nested formulas. To improve efficiency, only the reachable sets contributing to uncertainty are refined, identified through the associated markers. The framework allows online or offline monitoring to adapt to incremental system evolution while avoiding unnecessary recomputation. A case study on a nonlinear oscillator demonstrates a significant reduction in satisfaction ambiguity, highlighting the effectiveness of the approach.
Rethinking the Encoding and Annotating of 3D Bounding Box: Corner-Aware 3D Object Detection from Point Clouds
Venue: arXiv cs.CV 2025
Year: 2025

Center-aligned regression remains dominant in LiDAR-based 3D object detection, yet it suffers from fundamental instability: object centers often fall in sparse or empty regions of the bird's-eye-view (BEV) due to the front-surface-biased nature of LiDAR point clouds, leading to noisy and inaccurate bounding box predictions. To circumvent this limitation, we revisit bounding box representation and propose corner-aligned regression, which shifts the prediction target from unstable centers to geometrically informative corners that reside in dense, observable regions. Leveraging the inherent geometric constraints among corners and image 2D boxes, partial parameters of 3D bounding boxes can be recovered from corner annotations, enabling a weakly supervised paradigm without requiring complete 3D labels. We design a simple yet effective corner-aware detection head that can be plugged into existing detectors. Experiments on KITTI show our method improves performance by 3.5% AP over center-based baseline, and achieves 83% of fully supervised accuracy using only BEV corner clicks, demonstrating the effectiveness of our corner-aware regression strategy.
Neurocircuitry-Inspired Hierarchical Graph Causal Attention Networks for Explainable Depression Identification
Venue: arXiv cs.LG 2025
Year: 2025

Major Depressive Disorder (MDD), affecting millions worldwide, exhibits complex pathophysiology manifested through disrupted brain network dynamics. Although graph neural networks that leverage neuroimaging data have shown promise in depression diagnosis, existing approaches are predominantly data-driven and operate largely as black-box models, lacking neurobiological interpretability. Here, we present NH-GCAT (Neurocircuitry-Inspired Hierarchical Graph Causal Attention Networks), a novel framework that bridges neuroscience domain knowledge with deep learning by explicitly and hierarchically modeling depression-specific mechanisms at different spatial scales. Our approach introduces three key technical contributions: (1) at the local brain regional level, we design a residual gated fusion module that integrates temporal blood oxygenation level dependent (BOLD) dynamics with functional connectivity patterns, specifically engineered to capture local depression-relevant low-frequency neural oscillations; (2) at the multi-regional circuit level, we propose a hierarchical circuit encoding scheme that aggregates regional node representations following established depression neurocircuitry organization, and (3) at the multi-circuit network level, we develop a variational latent causal attention mechanism that leverages a continuous probabilistic latent space to infer directed information flow among critical circuits, characterizing disease-altered whole-brain inter-circuit interactions. Rigorous leave-one-site-out cross-validation on the REST-meta-MDD dataset demonstrates NH-GCAT's state-of-the-art performance in depression classification, achieving a sample-size weighted-average accuracy of 73.3\% and an AUROC of 76.4\%, while simultaneously providing neurobiologically meaningful explanations.
Smart Manufacturing: MLOps-Enabled Event-Driven Architecture for Enhanced Control in Steel Production
Venue: arXiv cs.LG 2025
Year: 2025

We explore a Digital Twin-Based Approach for Smart Manufacturing to improve Sustainability, Efficiency, and Cost-Effectiveness for a steel production plant. Our system is based on a micro-service edge-compute platform that ingests real-time sensor data from the process into a digital twin over a converged network infrastructure. We implement agile machine learning-based control loops in the digital twin to optimize induction furnace heating, enhance operational quality, and reduce process waste. Key to our approach is a Deep Reinforcement learning-based agent used in our machine learning operation (MLOps) driven system to autonomously correlate the system state with its digital twin to identify correction actions that aim to optimize power settings for the plant. We present the theoretical basis, architectural details, and practical implications of our approach to reduce manufacturing waste and increase production quality. We design the system for flexibility so that our scalable event-driven architecture can be adapted to various industrial applications. With this research, we propose a pivotal step towards the transformation of traditional processes into intelligent systems, aligning with sustainability goals and emphasizing the role of MLOps in shaping the future of data-driven manufacturing.
Hybrid Neuro-Symbolic Models for Ethical AI in Risk-Sensitive Domains
Venue: arXiv cs.AI 2025
Year: 2025

Artificial intelligence deployed in risk-sensitive domains such as healthcare, finance, and security must not only achieve predictive accuracy but also ensure transparency, ethical alignment, and compliance with regulatory expectations. Hybrid neuro symbolic models combine the pattern-recognition strengths of neural networks with the interpretability and logical rigor of symbolic reasoning, making them well-suited for these contexts. This paper surveys hybrid architectures, ethical design considerations, and deployment patterns that balance accuracy with accountability. We highlight techniques for integrating knowledge graphs with deep inference, embedding fairness-aware rules, and generating human-readable explanations. Through case studies in healthcare decision support, financial risk management, and autonomous infrastructure, we show how hybrid systems can deliver reliable and auditable AI. Finally, we outline evaluation protocols and future directions for scaling neuro symbolic frameworks in complex, high stakes environments.
CubeletWorld: A New Abstraction for Scalable 3D Modeling
Venue: arXiv cs.LG 2025
Year: 2025

Modern cities produce vast streams of heterogeneous data, from infrastructure maps to mobility logs and satellite imagery. However, integrating these sources into coherent spatial models for planning and prediction remains a major challenge. Existing agent-centric methods often rely on direct environmental sensing, limiting scalability and raising privacy concerns. This paper introduces CubeletWorld, a novel framework for representing and analyzing urban environments through a discretized 3D grid of spatial units called cubelets. This abstraction enables privacy-preserving modeling by embedding diverse data signals, such as infrastructure, movement, or environmental indicators, into localized cubelet states. CubeletWorld supports downstream tasks such as planning, navigation, and occupancy prediction without requiring agent-driven sensing. To evaluate this paradigm, we propose the CubeletWorld State Prediction task, which involves predicting the cubelet state using a realistic dataset containing various urban elements like streets and buildings through this discretized representation. We explore a range of modified core models suitable for our setting and analyze challenges posed by increasing spatial granularity, specifically the issue of sparsity in representation and scalability of baselines. In contrast to existing 3D occupancy prediction models, our cubelet-centric approach focuses on inferring state at the spatial unit level, enabling greater generalizability across regions and improved privacy compliance. Our results demonstrate that CubeletWorld offers a flexible and extensible framework for learning from complex urban data, and it opens up new possibilities for scalable simulation and decision support in domains such as socio-demographic modeling, environmental monitoring, and emergency response. The code and datasets can be downloaded from here.
Cognitive Inception: Agentic Reasoning against Visual Deceptions by Injecting Skepticism
Venue: arXiv cs.AI 2025
Year: 2025

As the development of AI-generated contents (AIGC), multi-modal Large Language Models (LLM) struggle to identify generated visual inputs from real ones. Such shortcoming causes vulnerability against visual deceptions, where the models are deceived by generated contents, and the reliability of reasoning processes is jeopardized. Therefore, facing rapidly emerging generative models and diverse data distribution, it is of vital importance to improve LLMs' generalizable reasoning to verify the authenticity of visual inputs against potential deceptions. Inspired by human cognitive processes, we discovered that LLMs exhibit tendency of over-trusting the visual inputs, while injecting skepticism could significantly improve the models visual cognitive capability against visual deceptions. Based on this discovery, we propose \textbf{Inception}, a fully reasoning-based agentic reasoning framework to conduct generalizable authenticity verification by injecting skepticism, where LLMs' reasoning logic is iteratively enhanced between External Skeptic and Internal Skeptic agents. To the best of our knowledge, this is the first fully reasoning-based framework against AIGC visual deceptions. Our approach achieved a large margin of performance improvement over the strongest existing LLM baselines and SOTA performance on AEGIS benchmark.
Datacenters in the Desert: Feasibility and Sustainability of LLM Inference in the Middle East
Venue: arXiv cs.CY 2025
Year: 2025

As the Middle East emerges as a strategic hub for artificial intelligence (AI) infrastructure, the feasibility of deploying sustainable datacenters in desert environments has become a topic of growing relevance. This paper presents an empirical study analyzing the energy consumption and carbon footprint of large language model (LLM) inference across four countries: the United Arab Emirates, Iceland, Germany, and the United States of America using DeepSeek Coder 1.3B and the HumanEval dataset on the task of code generation. We use the CodeCarbon library to track energy and carbon emissions andcompare geographical trade-offs for climate-aware AI deployment. Our findings highlight both the challenges and potential of datacenters in desert regions and provide a balanced outlook on their role in global AI expansion.
Understanding Counting Mechanisms in Large Language and Vision-Language Models
Venue: arXiv cs.CV 2025
Year: 2025

This paper examines how large language models (LLMs) and large vision-language models (LVLMs) represent and compute numerical information in counting tasks. We use controlled experiments with repeated textual and visual items and analyze model behavior through causal mediation and activation patching. To this end, we design a specialized tool, CountScope, for mechanistic interpretability of numerical content. Results show that individual tokens or visual features encode latent positional count information that can be extracted and transferred across contexts. Layerwise analyses reveal a progressive emergence of numerical representations, with lower layers encoding small counts and higher layers representing larger ones. We identify an internal counter mechanism that updates with each item, stored mainly in the final token or region and transferable between contexts. In LVLMs, numerical information also appears in visual embeddings, shifting between background and foreground regions depending on spatial composition. Models rely on structural cues such as separators in text, which act as shortcuts for tracking item counts and influence the accuracy of numerical predictions. Overall, counting emerges as a structured, layerwise process in LLMs and follows the same general pattern in LVLMs, shaped by the properties of the vision encoder.
Can Vision-Language Models Count? A Synthetic Benchmark and Analysis of Attention-Based Interventions
Venue: arXiv cs.CV 2025
Year: 2025

Recent research suggests that Vision Language Models (VLMs) often rely on inherent biases learned during training when responding to queries about visual properties of images. These biases are exacerbated when VLMs are asked highly specific questions that require them to focus on particular areas of the image in tasks such as counting. We build upon this research by developing a synthetic benchmark dataset and evaluation framework to systematically determine how counting performance varies as image and prompt properties change. Using open-source VLMs, we then analyze how attention allocation fluctuates with varying input parameters (e.g. number of objects in the image, objects color, background color, objects texture, background texture, and prompt specificity). We further implement attention-based interventions to modulate focus on visual tokens at different layers and evaluate their impact on counting performance across a range of visual conditions. Our experiments reveal that while VLM counting performance remains challenging, especially under high visual or linguistic complexity, certain attention interventions can lead to modest gains in counting performance.
Pre-cache: A Microarchitectural Solution to prevent Meltdown and Spectre
Venue: arXiv cs.CR 2025
Year: 2025

Recent work has shown that out-of-order and speculative execution mechanisms used to increase performance in the majority of processors expose the processors to critical attacks. These attacks, called Meltdown and Spectre, exploit the side effects of performance-enhancing features in modern microprocessors to expose secret data through side channels in the microarchitecture. The well known implementations of these attacks exploit cache-based side channels since they are the least noisy channels to exfiltrate data. While some software patches attempted to mitigate these attacks, they are ad-hoc and only try to fix the side effects of the vulnerabilites. They may also impose a performance overhead of up to 30%. In this paper, we present a microarchitecture-based solution for Meltdown and Spectre that addresses the vulnerabilities exploited by the attacks. Our solution prevents flushed instructions from exposing data to the cache. Our approach can also be extended to other memory structures in the microarchitecture thereby preventing variants of the attacks which exploit these memory structures. We further identify two new variant attacks based on exploiting the side effects of speculative and out-of-order execution and show how our solution can be used to prevent these attacks. Evaluation results show that our microarchitectural solution not only restores secure out-of-order and speculative execution, but also has relatively low overhead and does not significantly impact performance for most applications.
The Dark Side of Flexibility: How Aggregated Cyberattacks Threaten the Power Grid
Venue: arXiv cs.CR 2025
Year: 2025

Flexible energy resources are increasingly becoming common in smart grids. These resources are typically managed and controlled by aggregators that coordinate many resources to provide flexibility services. However, these aggregators and flexible energy resources are vulnerable, which could allow attackers to remotely control flexible energy resources to launch large-scale attacks on the grid. This paper investigates and evaluates the potential attack strategies that can be used to manipulate flexible energy resources to challenge the effectiveness of traditional grid stability measures and disrupt the first-swing stability of the power grid. Our work shows that although a large amount of power is required, the current flexibility capacities could potentially be sufficient to disrupt the grid on a national level.
StealthCup: Realistic, Multi-Stage, Evasion-Focused CTF for Benchmarking IDS
Venue: arXiv cs.CR 2025
Year: 2025

Intrusion Detection Systems (IDS) are critical to defending enterprise and industrial control environments, yet evaluating their effectiveness under realistic conditions remains an open challenge. Existing benchmarks rely on synthetic datasets (e.g., NSL-KDD, CICIDS2017) or scripted replay frameworks, which fail to capture adaptive adversary behavior. Even MITRE ATT&CK Evaluations, while influential, are host-centric and assume malware-driven compromise, thereby under-representing stealthy, multi-stage intrusions across IT and OT domains. We present StealthCup, a novel evaluation methodology that operationalizes IDS benchmarking as an evasion-focused Capture-the-Flag competition. Professional penetration testers engaged in multi-stage attack chains on a realistic IT/OT testbed, with scoring penalizing IDS detections. The event generated structured attacker writeups, validated detections, and PCAPs, host logs, and alerts. Our results reveal that out of 32 exercised attack techniques, 11 were not detected by any IDS configuration. Open-source systems (Wazuh, Suricata) produced high false-positive rates >90%, while commercial tools generated fewer false positives but also missed more attacks. Comparison with the Volt Typhoon APT advisory confirmed strong realism: all 28 applicable techniques were exercised, 19 appeared in writeups, and 9 in forensic traces. These findings demonstrate that StealthCup elicits attacker behavior closely aligned with state-sponsored TTPs, while exposing blind spots across both open-source and commercial IDS. The resulting datasets and methodology provide a reproducible foundation for future stealth-focused IDS evaluation.
LEARN: Learning End-to-End Aerial Resource-Constrained Multi-Robot Navigation
Venue: arXiv cs.RO 2025
Year: 2025

Nano-UAV teams offer great agility yet face severe navigation challenges due to constrained onboard sensing, communication, and computation. Existing approaches rely on high-resolution vision or compute-intensive planners, rendering them infeasible for these platforms. We introduce LEARN, a lightweight, two-stage safety-guided reinforcement learning (RL) framework for multi-UAV navigation in cluttered spaces. Our system combines low-resolution Time-of-Flight (ToF) sensors and a simple motion planner with a compact, attention-based RL policy. In simulation, LEARN outperforms two state-of-the-art planners by $10\%$ while using substantially fewer resources. We demonstrate LEARN's viability on six Crazyflie quadrotors, achieving fully onboard flight in diverse indoor and outdoor environments at speeds up to $2.0 m/s$ and traversing $0.2 m$ gaps.