OpenRaiser/Intern-Atlas huggingface dataset:
Improving Resiliency of Vital Services in Flood-Affected Regions of Bangladesh Using Next-Generation Opportunistic DTN Edge Ad Hoc Networks
Venue: arXiv cs.NI 2025
Year: 2025

Opportunistic routing architectures offer a resilient communication paradigm in environments where conventional networks fail due to disrupted infrastructure, dynamic node mobility, and intermittent connectivity conditions that commonly arise during large-scale disasters. In Bangladesh, recurring floods severely hinder communication systems, isolating affected populations and obstructing emergency response efforts. To address these challenges, there is a growing demand for intelligent and adaptive routing solutions capable of sustaining critical communication and services without relying on fixed infrastructure. This research presents AZIZA (Adaptive Zone-based Intelligent Fully Distributed Trust-Aware Routing Protocol), a next-generation opportunistic protocol designed to improve the resiliency of critical communication and services in disaster-prone and flood-affected regions. AZIZA supports adaptive data delivery for emergency alerts, sensor readings, and inter-zone coordination by integrating (1) zone-based forwarding to optimize localized transmission, (2) trust-aware logic to bypass uncooperative or malicious nodes, and (3) context-driven decision-making based on trust metrics, residual energy, and historical delivery patterns. AZIZA operates over lightweight, infrastructure-less edge ad hoc networks comprising mobile phones, UAVs, and ground vehicles acting as decentralized service relays. Simulation results using The Opportunistic Network Environment (ONE) Simulator configured with real-world mobility traces and flood data from Bangladesh demonstrate that AZIZA significantly outperforms benchmark approaches in delivery reliability, energy efficiency, and routing resilience. As a scalable and deployable framework, AZIZA advances the use of next-generation opportunistic routing in environments where traditional systems routinely collapse.
Integrated 4D/5D Digital-Twin Framework for Cost Estimation and Probabilistic Schedule Control: A Texas Mid-Rise Case Study
Venue: arXiv cs.CE 2025
Year: 2025

Persistent cost and schedule overruns in U.S. building projects expose limitations of conventional, document-based estimating and deterministic Critical Path Method (CPM) scheduling, which remain inflexible under uncertainty and lag dynamic field conditions. This study presents an integrated 4D/5D digital-twin framework unifying Building Information Modeling (BIM), natural language processing (NLP), reality capture, computer vision, Bayesian risk modeling, and deep reinforcement learning (DRL) for construction cost and schedule control. The system automates project-control functions by: (a) mapping contract documents to standardized cost items using transformer-based NLP (0.883 weighted F1 score); (b) aligning photogrammetry and LiDAR data with BIM to compute earned value; (c) deriving real-time activity completion from site imagery (0.891 micro accuracy); (d) updating probabilistic CPM forecasts via Bayesian inference and Monte Carlo simulation; (e) using DRL for adaptive resource allocation (75% adoption rate); and (f) providing 4D/5D decision sandbox for predictive analysis. A Texas mid-rise case study demonstrates localized cost adjustment using RSMeans City Cost Index and Bureau of Labor Statistics wage data. Results show 43% reduction in estimating labor, 6% overtime reduction (91 hours), and project completion matching P50 probabilistic forecast of 128 days, confirming improved estimation accuracy and responsiveness.
Majority Rules: LLM Ensemble is a Winning Approach for Content Categorization
Venue: arXiv cs.AI 2025
Year: 2025

This study introduces an ensemble framework for unstructured text categorization using large language models (LLMs). By integrating multiple models, the ensemble large language model (eLLM) framework addresses common weaknesses of individual systems, including inconsistency, hallucination, category inflation, and misclassification. The eLLM approach yields a substantial performance improvement of up to 65\% in F1-score over the strongest single model. We formalize the ensemble process through a mathematical model of collective decision-making and establish principled aggregation criteria. Using the Interactive Advertising Bureau (IAB) hierarchical taxonomy, we evaluate ten state-of-the-art LLMs under identical zero-shot conditions on a human-annotated corpus of 8{,}660 samples. Results show that individual models plateau in performance due to the compression of semantically rich text into sparse categorical representations, while eLLM improves both robustness and accuracy. With a diverse consortium of models, eLLM achieves near human-expert-level performance, offering a scalable and reliable solution for taxonomy-based classification that may significantly reduce dependence on human expert labeling.
Sovereign AI: Rethinking Autonomy in the Age of Global Interdependence
Venue: arXiv cs.CY 2025
Year: 2025

Artificial intelligence (AI) is emerging as a foundational general-purpose technology, raising new dilemmas of sovereignty in an interconnected world. While governments seek greater control over it, the very foundations of AI--global data pipelines, semiconductor supply chains, open-source ecosystems, and international standards--resist enclosure. This paper develops a conceptual and formal framework for understanding sovereign AI as a continuum rather than a binary condition, balancing autonomy with interdependence. Drawing on classical theories, historical analogies, and contemporary debates on networked autonomy, we present a planner's model that identifies two policy heuristics: equalizing marginal returns across the four sovereignty pillars and setting openness where global benefits equal exposure risks. We apply the model to India, highlighting sovereign footholds in data, compute, and norms but weaker model autonomy. The near-term challenge is integration via coupled Data x Compute investment, lifecycle governance (ModelOps), and safeguarded procurement. We then apply the model to the Middle East (Saudi Arabia and the UAE), where large public investment in Arabic-first models and sovereign cloud implies high sovereignty weights, lower effective fiscal constraints, and strong Data x Compute complementarities. An interior openness setting with guardrails emerges as optimal. Across contexts, the lesson is that sovereignty in AI needs managed interdependence, not isolation.
BlueScript: A Disaggregated Virtual Machine for Microcontrollers
Venue: arXiv cs.PL 2025
Year: 2025

Virtual machines (VMs) are highly beneficial for microcontroller development. In particular, interactive programming environments greatly facilitate iterative development processes, and higher execution speeds expand the range of applications that can be developed. However, due to their limited memory size, microcontroller VMs provide a limited set of features. Widely used VMs for microcontrollers often lack interactive responsiveness and/or high execution speed. While researchers have investigated offloading certain VM components to other machines,the types of components that can be offloaded are still restricted. In this paper, we propose a disaggregated VM that offloads as many components as possible to a host machine. This makes it possible to exploit the abundant memory of the host machine and its powerful processing capability to provide rich features through the VM. As an instance of a disaggregated VM, we design and implement a BlueScript VM. The BlueScript VM is a virtual machine for microcontrollers that provides an interactive development environment. We offload most of the components of the BlueScript VM to a host machine. To reduce communication overhead between the host machine and the microcontroller, we employed a data structure called a shadow machine on the host machine, which mirrors the execution state of the microcontroller. Through our experiments, we confirmed that offloading components does not seriously compromise their expected benefits. We assess that an offloaded incremental compiler results in faster execution speed than MicroPython and Espruino, while keeping interactivity comparable with MicroPython. In addition, our experiments observe that the offloaded dynamic compiler improves VM performance. Through this investigation, we demonstrate the feasibility of providing rich features even on VMs for memory-limited microcontrollers.
Scalable Privilege Analysis for Multi-Cloud Big Data Platforms: A Hypergraph Approach
Venue: arXiv cs.CR 2025
Year: 2025

The rapid adoption of multi-cloud environments has amplified risks associated with privileged access mismanagement. Traditional Privileged Access Management (PAM) solutions based on Attribute-Based Access Control (ABAC) exhibit cubic O(n^3) complexity, rendering real-time privilege analysis intractable at enterprise scale. We present a novel PAM framework integrating NIST's Next Generation Access Control (NGAC) with hypergraph semantics to address this scalability crisis. Our approach leverages hypergraphs with labeled hyperedges to model complex, multi-dimensional privilege relationships, achieving sub-linear O(sqrt n) traversal complexity and O(nlogn) detection time-rigorously proven through formal complexity analysis. We introduce a 3-Dimensional Privilege Analysis framework encompassing Attack Surface, Attack Window, and Attack Identity to systematically identify privilege vulnerabilities. Experimental validation on AWS-based systems with 200-4000 users demonstrates 10x improvement over ABAC and 4x improvement over standard NGAC-DAG, enabling sub-second privilege detection at scale. Real-world use cases validate detection of privilege escalation chains, over-privileged users, and lateral movement pathways in multi-cloud infrastructures.
The Loss of Control Playbook: Degrees, Dynamics, and Preparedness
Venue: arXiv cs.CY 2025
Year: 2025

This research report addresses the absence of an actionable definition for Loss of Control (LoC) in AI systems by developing a novel taxonomy and preparedness framework. Despite increasing policy and research attention, existing LoC definitions vary significantly in scope and timeline, hindering effective LoC assessment and mitigation. To address this issue, we draw from an extensive literature review and propose a graded LoC taxonomy, based on the metrics of severity and persistence, that distinguishes between Deviation, Bounded LoC, and Strict LoC. We model pathways toward a societal state of vulnerability in which sufficiently advanced AI systems have acquired or could acquire the means to cause Bounded or Strict LoC once a catalyst, either misalignment or pure malfunction, materializes. We argue that this state becomes increasingly likely over time, absent strategic intervention, and propose a strategy to avoid reaching a state of vulnerability. Rather than focusing solely on intervening on AI capabilities and propensities potentially relevant for LoC or on preventing potential catalysts, we introduce a complementary framework that emphasizes three extrinsic factors: Deployment context, Affordances, and Permissions (the DAP framework). Compared to work on intrinsic factors and catalysts, this framework has the unfair advantage of being actionable today. Finally, we put forward a plan to maintain preparedness and prevent the occurrence of LoC outcomes should a state of societal vulnerability be reached, focusing on governance measures (threat modeling, deployment policies, emergency response) and technical controls (pre-deployment testing, control measures, monitoring) that could maintain a condition of perennial suspension.
AquaSentinel: Next-Generation AI System Integrating Sensor Networks for Urban Underground Water Pipeline Anomaly Detection via Collaborative MoE-LLM Agent Architecture
Venue: arXiv cs.CE 2025
Year: 2025

Underground pipeline leaks and infiltrations pose significant threats to water security and environmental safety. Traditional manual inspection methods provide limited coverage and delayed response, often missing critical anomalies. This paper proposes AquaSentinel, a novel physics-informed AI system for real-time anomaly detection in urban underground water pipeline networks. We introduce four key innovations: (1) strategic sparse sensor deployment at high-centrality nodes combined with physics-based state augmentation to achieve network-wide observability from minimal infrastructure; (2) the RTCA (Real-Time Cumulative Anomaly) detection algorithm, which employs dual-threshold monitoring with adaptive statistics to distinguish transient fluctuations from genuine anomalies; (3) a Mixture of Experts (MoE) ensemble of spatiotemporal graph neural networks that provides robust predictions by dynamically weighting model contributions; (4) causal flow-based leak localization that traces anomalies upstream to identify source nodes and affected pipe segments. Our system strategically deploys sensors at critical network junctions and leverages physics-based modeling to propagate measurements to unmonitored nodes, creating virtual sensors that enhance data availability across the entire network. Experimental evaluation using 110 leak scenarios demonstrates that AquaSentinel achieves 100% detection accuracy. This work advances pipeline monitoring by demonstrating that physics-informed sparse sensing can match the performance of dense deployments at a fraction of the cost, providing a practical solution for aging urban infrastructure.
AI-Assisted Writing Is Growing Fastest Among Non-English-Speaking and Less Established Scientists
Venue: arXiv cs.DL 2025
Year: 2025

The dominance of English in global science has long created significant barriers for non-native speakers. The recent emergence of generative artificial intelligence (GenAI) dramatically reduces drafting and revision costs, but, simultaneously, raises a critical question: how is the technology being adopted by the global scientific community, and is it mitigating existing inequities? This study provides first large-scale empirical evidence by analyzing over two million full-text biomedical publications from PubMed Central from 2021 to 2024, estimating the fraction of AI-generated content using a distribution-based framework. We observe a significant post-ChatGPT surge in AI-assisted writing, with adoption growing fastest in contexts where language barriers are most pronounced: approximately 400% in non-English-speaking countries compared to 183% in English-speaking countries. This adoption is highest among less-established scientists, including those with fewer publications and citations, as well as those in early career stages at lower-ranked institutions. Prior AI research experience also predicted higher adoption. Finally, increased AI usage was associated with a modest increase in productivity, narrowing the publication gap between scientists from English-speaking and non-English-speaking countries with higher levels of AI adoption. These findings provide large-scale evidence that generative AI is being adopted unevenly, reflecting existing structural disparities while also offering a potential opportunity to mitigate long-standing linguistic inequalities.
Decomposing Theory of Mind: How Emotional Processing Mediates ToM Abilities in LLMs
Venue: arXiv cs.AI 2025
Year: 2025

Recent work shows activation steering substantially improves language models' Theory of Mind (ToM) (Bortoletto et al. 2024), yet the mechanisms of what changes occur internally that leads to different outputs remains unclear. We propose decomposing ToM in LLMs by comparing steered versus baseline LLMs' activations using linear probes trained on 45 cognitive actions. We applied Contrastive Activation Addition (CAA) steering to Gemma-3-4B and evaluated it on 1,000 BigToM forward belief scenarios (Gandhi et al. 2023), we find improved performance on belief attribution tasks (32.5\% to 46.7\% accuracy) is mediated by activations processing emotional content : emotion perception (+2.23), emotion valuing (+2.20), while suppressing analytical processes: questioning (-0.78), convergent thinking (-1.59). This suggests that successful ToM abilities in LLMs are mediated by emotional understanding, not analytical reasoning.
Lifefin: Escaping Mempool Explosions in DAG-based BFT
Venue: arXiv cs.CR 2025
Year: 2025

Directed Acyclic Graph (DAG)-based Byzantine Fault-Tolerant (BFT) protocols have emerged as promising solutions for high-throughput blockchains. By decoupling data dissemination from transaction ordering and constructing a well-connected DAG in the mempool, these protocols enable zero-message ordering and implicit view changes. However, we identify a fundamental liveness vulnerability: an adversary can trigger mempool explosions to prevent transaction commitment, ultimately compromising the protocol's liveness. In response, this work presents Lifefin, a generic and self-stabilizing protocol designed to integrate seamlessly with existing DAG-based BFT protocols and circumvent such vulnerabilities. Lifefin leverages the Agreement on Common Subset (ACS) mechanism, allowing nodes to escape mempool explosions by committing transactions with bounded resource usage even in adverse conditions. As a result, Lifefin imposes (almost) zero overhead in typical cases while effectively eliminating liveness vulnerabilities. To demonstrate the effectiveness of Lifefin, we integrate it into two state-of-the-art DAG-based BFT protocols, Sailfish and Mysticeti, resulting in two enhanced variants: Sailfish-Lifefin and Mysticeti-Lifefin. We implement these variants and compare them with the original Sailfish and Mysticeti systems. Our evaluation demonstrates that Lifefin achieves comparable transaction throughput while introducing only minimal additional latency to resist similar attacks.
Click2Graph: Interactive Panoptic Video Scene Graphs from a Single Click
Venue: arXiv cs.CV 2025
Year: 2025

State-of-the-art Video Scene Graph Generation (VSGG) systems provide structured visual understanding but operate as closed, feed-forward pipelines with no ability to incorporate human guidance. In contrast, promptable segmentation models such as SAM2 enable precise user interaction but lack semantic or relational reasoning. We introduce Click2Graph, the first interactive framework for Panoptic Video Scene Graph Generation (PVSG) that unifies visual prompting with spatial, temporal, and semantic understanding. From a single user cue, such as a click or bounding box, Click2Graph segments and tracks the subject across time, autonomously discovers interacting objects, and predicts <subject, object, predicate> triplets to form a temporally consistent scene graph. Our framework introduces two key components: a Dynamic Interaction Discovery Module that generates subject-conditioned object prompts, and a Semantic Classification Head that performs joint entity and predicate reasoning. Experiments on the OpenPVSG benchmark demonstrate that Click2Graph establishes a strong foundation for user-guided PVSG, showing how human prompting can be combined with panoptic grounding and relational inference to enable controllable and interpretable video scene understanding.
InfCode-C++: Intent-Guided Semantic Retrieval and AST-Structured Search for C++ Issue Resolution
Venue: arXiv cs.SE 2025
Year: 2025

Large language model (LLM) agents have recently shown strong performance on repository-level issue resolution, but existing systems are almost exclusively designed for Python and rely heavily on lexical retrieval and shallow code navigation. These approaches transfer poorly to C++ projects, where overloaded identifiers, nested namespaces, template instantiations, and deep control-flow structures make context retrieval and fault localization substantially more difficult. As a result, state-of-the-art Python-oriented agents show a drastic performance drop on the C++ subset of MultiSWE-bench. We introduce INFCODE-C++, the first C++-aware autonomous system for end-to-end issue resolution. The system combines two complementary retrieval mechanisms -- semantic code-intent retrieval and deterministic AST-structured querying -- to construct accurate, language-aware context for repair.These components enable precise localization and robust patch synthesis in large, statically typed C++ repositories. Evaluated on the \texttt{MultiSWE-bench-CPP} benchmark, INFCODE-C++ achieves a resolution rate of 25.58\%, outperforming the strongest prior agent by 10.85 percentage points and more than doubling the performance of MSWE-agent. Ablation and behavioral studies further demonstrate the critical role of semantic retrieval, structural analysis, and accurate reproduction in C++ issue resolution. INFCODE-C++ highlights the need for language-aware reasoning in multi-language software agents and establishes a foundation for future research on scalable, LLM-driven repair for complex, statically typed ecosystems.
Graph-Aware Temporal Encoder Based Service Migration and Resource Allocation in Satellite Networks
Venue: arXiv cs.NI 2025
Year: 2025

The rapid expansion of latency-sensitive applications has sparked renewed interest in deploying edge computing capabilities aboard satellite constellations, aiming to achieve truly global and seamless service coverage. On one hand, it is essential to allocate the limited onboard computational and communication resources efficiently to serve geographically distributed users. On the other hand, the dynamic nature of satellite orbits necessitates effective service migration strategies to maintain service continuity and quality as the coverage areas of satellites evolve. We formulate this problem as a spatio-temporal Markov decision process, where satellites, ground users, and flight users are modeled as nodes in a time-varying graph. The node features incorporate queuing dynamics to characterize packet loss probabilities. To solve this problem, we propose a Graph-Aware Temporal Encoder (GATE) that jointly models spatial correlations and temporal dynamics. GATE uses a two-layer graph convolutional network to extract inter-satellite and user dependencies and a temporal convolutional network to capture their short-term evolution, producing unified spatio-temporal representations. The resulting spatial-temporal representations are passed into a Hybrid Proximal Policy Optimization (HPPO) framework. This framework features a multi-head actor that outputs both discrete service migration decisions and continuous resource allocation ratios, along with a critic for value estimation. We conduct extensive simulations involving both persistent and intermittent users distributed across real-world population centers.
MUSEKG: A Knowledge Graph Over Museum Collections
Venue: arXiv cs.AI 2025
Year: 2025

Digital transformation in the cultural heritage sector has produced vast yet fragmented collections of artefact data. Existing frameworks for museum information systems struggle to integrate heterogeneous metadata, unstructured documents, and multimodal artefacts into a coherent and queryable form. We present MuseKG, an end-to-end knowledge-graph framework that unifies structured and unstructured museum data through symbolic-neural integration. MuseKG constructs a typed property graph linking objects, people, organisations, and visual or textual labels, and supports natural language queries. Evaluations on real museum collections demonstrate robust performance across queries over attributes, relations, and related entities, surpassing large-language-model zero-shot, few-shot and SPARQL prompt baselines. The results highlight the importance of symbolic grounding for interpretable and scalable cultural heritage reasoning, and pave the way for web-scale integration of digital heritage knowledge.