OpenRaiser/Intern-Atlas huggingface dataset:
LiDAR Loop Closure Detection using Semantic Graphs with Graph Attention Networks
Venue: arXiv cs.CV 2025
Year: 2025

In this paper, we propose a novel loop closure detection algorithm that uses graph attention neural networks to encode semantic graphs to perform place recognition and then use semantic registration to estimate the 6 DoF relative pose constraint. Our place recognition algorithm has two key modules, namely, a semantic graph encoder module and a graph comparison module. The semantic graph encoder employs graph attention networks to efficiently encode spatial, semantic and geometric information from the semantic graph of the input point cloud. We then use self-attention mechanism in both node-embedding and graph-embedding steps to create distinctive graph vectors. The graph vectors of the current scan and a keyframe scan are then compared in the graph comparison module to identify a possible loop closure. Specifically, employing the difference of the two graph vectors showed a significant improvement in performance, as shown in ablation studies. Lastly, we implemented a semantic registration algorithm that takes in loop closure candidate scans and estimates the relative 6 DoF pose constraint for the LiDAR SLAM system. Extensive evaluation on public datasets shows that our model is more accurate and robust, achieving 13% improvement in maximum F1 score on the SemanticKITTI dataset, when compared to the baseline semantic graph algorithm. For the benefit of the community, we open-source the complete implementation of our proposed algorithm and custom implementation of semantic registration at https://github.com/crepuscularlight/SemanticLoopClosure
Online Decision-Making in Tree-Like Multi-Agent Games with Transfers
Venue: arXiv cs.GT 2025
Year: 2025

The widespread deployment of Machine Learning systems everywhere raises challenges, such as dealing with interactions or competition between multiple learners. In that goal, we study multi-agent sequential decision-making by considering principal-agent interactions in a tree structure. In this problem, the reward of a player is influenced by the actions of her children, who are all self-interested and non-cooperative, hence the complexity of making good decisions. Our main finding is that it is possible to steer all the players towards the globally optimal set of actions by simply allowing single-step transfers between them. A transfer is established between a principal and one of her agents: the principal actually offers the proposed payment if the agent picks the recommended action. The analysis poses specific challenges due to the intricate interactions between the nodes of the tree and the propagation of the regret within this tree. Considering a bandit setup, we propose algorithmic solutions for the players to end up being no-regret with respect to the optimal pair of actions and incentives. In the long run, allowing transfers between players makes them act as if they were collaborating together, although they remain self-interested non-cooperative: transfers restore efficiency.
Do LLMs Strategically Reveal, Conceal, and Infer Information? A Theoretical and Empirical Analysis in The Chameleon Game
Venue: arXiv cs.AI 2025
Year: 2025

Large language model-based (LLM-based) agents have become common in settings that include non-cooperative parties. In such settings, agents' decision-making needs to conceal information from their adversaries, reveal information to their cooperators, and infer information to identify the other agents' characteristics. To investigate whether LLMs have these information control and decision-making capabilities, we make LLM agents play the language-based hidden-identity game, The Chameleon. In this game, a group of non-chameleon agents who do not know each other aim to identify the chameleon agent without revealing a secret. The game requires the aforementioned information control capabilities both as a chameleon and a non-chameleon. We begin with a theoretical analysis for a spectrum of strategies, from concealing to revealing, and provide bounds on the non-chameleons' winning probability. The empirical results with GPT, Gemini 2.5 Pro, Llama 3.1, and Qwen3 models show that while non-chameleon LLM agents identify the chameleon, they fail to conceal the secret from the chameleon, and their winning probability is far from the levels of even trivial strategies. Based on these empirical results and our theoretical analysis, we deduce that LLM-based agents may reveal excessive information to agents of unknown identities. Interestingly, we find that, when instructed to adopt an information-revealing level, this level is linearly encoded in the LLM's internal representations. While the instructions alone are often ineffective at making non-chameleon LLMs conceal, we show that steering the internal representations in this linear direction directly can reliably induce concealing behavior.
Redefining Machine Unlearning: A Conformal Prediction-Motivated Approach
Venue: arXiv cs.LG 2025
Year: 2025

Machine unlearning seeks to remove the influence of specified data from a trained model. While metrics such as unlearning accuracy (UA) and membership inference attack (MIA) provide baselines for assessing unlearning performance, they fall short of evaluating the forgetting reliability. In this paper, we find that the data misclassified across UA and MIA still have their ground truth labels included in the prediction set from the uncertainty quantification perspective, which raises a fake unlearning issue. To address this issue, we propose two novel metrics inspired by conformal prediction that more reliably evaluate forgetting quality. Building on these insights, we further propose a conformal prediction-based unlearning framework that integrates conformal prediction into Carlini & Wagner adversarial attack loss, which can significantly push the ground truth label out of the conformal prediction set. Through extensive experiments on image classification task, we demonstrate both the effectiveness of our proposed metrics and the superiority of our unlearning framework, which improves the UA of existing unlearning methods by an average of 6.6% through the incorporation of a tailored loss term alone.
Digital twins in tourism: a systematic literature review
Venue: arXiv cs.CY 2025
Year: 2025

Purpose: This systematic literature review (SLR) characterizes the current state of the art on digital twinning (DT) technology in tourism-related applications. We aim to evaluate the types of DTs described in the literature, identifying their purposes, the areas of tourism where they have been proposed, their main components, and possible future directions based on current work. Design/methodology/approach: We conducted this SLR with bibliometric analysis based on an existing, validated methodology. Thirty-four peer-reviewed studies from three major scientific databases were selected for review. They were categorized using a taxonomy that included tourism type, purpose, spatial scale, data sources, data linkage, visualization, and application. Findings: The topic is at an early, evolving stage, as the oldest study found dates back to 2021. Most reviewed studies deal with cultural tourism, focusing on digitising cultural heritage. Destination management is the primary purpose of these DTs, with mainly site-level spatial scales. In many studies, the physical-digital data linkage is unilateral, lacking twin synchronization. In most DTs considered bilateral, the linkage is indirect. There are more applied than theoretical studies, suggesting progress in applying DTs in the field. Finally, there is an extensive research gap regarding DT technology in tourism, which is worth filling. Originality/Value: This paper presents a novel SLR with a bibliometric analysis of DTs' applied and theoretical application in tourism. Each reviewed publication is assessed and characterized, identifying the current state of the topic, possible research gaps, and future directions.
Determinants of Human Development Index (HDI): A Regression Analysis of Economic and Social Indicators
Venue: arXiv cs.CY 2025
Year: 2025

This study aims to investigate the factors influencing the Human Development Index (HDI). Five variables-GDP per capita, health expenditure, education expenditure, infant mortality rate (per 1,000 live births), and average years of schooling-were analyzed to develop a regression model assessing their impact on HDI. The results indicate that GDP per capita, infant mortality rate, and average years of schooling are significant predictors of HDI. Specifically, the study finds a positive relationship between GDP per capita and average years of schooling with HDI, while infant mortality rate is negatively associated with HDI.
TOAST Framework: A Multidimensional Approach to Ethical and Sustainable AI Integration in Organizations
Venue: arXiv cs.CY 2025
Year: 2025

Artificial Intelligence (AI) has emerged as a transformative technology with the potential to revolutionize various sectors, from healthcare to finance, education, and beyond. However, successfully implementing AI systems remains a complex challenge, requiring a comprehensive and methodologically sound framework. This paper contributes to this challenge by introducing the Trustworthy, Optimized, Adaptable, and Socio-Technologically harmonious (TOAST) framework. It draws on insights from various disciplines to align technical strategy with ethical values, societal responsibilities, and innovation aspirations. The TOAST framework is a novel approach designed to guide the implementation of AI systems, focusing on reliability, accountability, technical advancement, adaptability, and socio-technical harmony. By grounding the TOAST framework in healthcare case studies, this paper provides a robust evaluation of its practicality and theoretical soundness in addressing operational, ethical, and regulatory challenges in high-stakes environments, demonstrating how adaptable AI systems can enhance institutional efficiency, mitigate risks like bias and data privacy, and offer a replicable model for other sectors requiring ethically aligned and efficient AI integration.
Behavioural Analytics: Mathematics of the Mind
Venue: arXiv cs.CY 2025
Year: 2025

Behavioural analytics provides insights into individual and crowd behaviour, enabling analysis of what previously happened and predictions for how people may be likely to act in the future. In defence and security, this analysis allows organisations to achieve tactical and strategic advantage through influence campaigns, a key counterpart to physical activities. Before action can be taken, online and real-world behaviour must be analysed to determine the level of threat. Huge data volumes mean that automated processes are required to attain an accurate understanding of risk. We describe the mathematical basis of technologies to analyse quotes in multiple languages. These include a Bayesian network to understand behavioural factors, state estimation algorithms for time series analysis, and machine learning algorithms for classification. We present results from studies of quotes in English, French, and Arabic, from anti-violence campaigners, politicians, extremists, and terrorists. The algorithms correctly identify extreme statements; and analysis at individual, group, and population levels detects both trends over time and sharp changes attributed to major geopolitical events. Group analysis shows that additional population characteristics can be determined, such as polarisation over particular issues and large-scale shifts in attitude. Finally, MP voting behaviour and statements from publicly-available records are analysed to determine the level of correlation between what people say and what they do.
Large Language Models for Education: ChemTAsk -- An Open-Source Paradigm for Automated Q&A in the Graduate Classroom
Venue: arXiv cs.CY 2025
Year: 2025

Large language models (LLMs) show promise for aiding graduate level education, but are limited by their training data and potential confabulations. We developed ChemTAsk, an open-source pipeline that combines LLMs with retrieval-augmented generation (RAG) to provide accurate, context-specific assistance. ChemTAsk utilizes course materials, including lecture transcripts and primary publications, to generate accurate responses to student queries. Over nine weeks in an advanced biological chemistry course at the University of Pennsylvania, students could opt in to use ChemTAsk for assistance in any assignment or to understand class material. Comparative analysis showed ChemTAsk performed on par with human teaching assistants (TAs) in understanding student queries and providing accurate information, particularly excelling in creative problem-solving tasks. In contrast, TAs were more precise in their responses and tailored their assistance to the specifics of the class. Student feedback indicated that ChemTAsk was perceived as correct, helpful, and faster than TAs. Open-source and proprietary models from Meta and OpenAI respectively were tested on an original biological chemistry benchmark for future iterations of ChemTAsk. It was found that OpenAI models were more tolerant to deviations in the input prompt and excelled in self-assessment to safeguard for potential confabulations. Taken together, ChemTAsk demonstrates the potential of integrating LLMs with RAG to enhance educational support, offering a scalable tool for students and educators.
Growth Patterns of Inference
Venue: arXiv cs.AI 2025
Year: 2025

What properties of a first-order search space support/hinder inference? What kinds of facts would be most effective to learn? Answering these questions is essential for understanding the dynamics of deductive reasoning and creating large-scale knowledge-based learning systems that support efficient inference. We address these questions by developing a model of how the distribution of ground facts affects inference performance in search spaces. Experiments suggest that uniform search spaces are suitable for larger KBs whereas search spaces with skewed degree distribution show better performance in smaller KBs. A sharp transition in Q/A performance is seen in some cases, suggesting that analysis of the structure of search spaces with existing knowledge should be used to guide the acquisition of new ground facts in learning systems.
Towards Efficient Multi-Objective Optimisation for Real-World Power Grid Topology Control
Venue: arXiv cs.AI 2025
Year: 2025

Power grid operators face increasing difficulties in the control room as the increase in energy demand and the shift to renewable energy introduce new complexities in managing congestion and maintaining a stable supply. Effective grid topology control requires advanced tools capable of handling multi-objective trade-offs. While Reinforcement Learning (RL) offers a promising framework for tackling such challenges, existing Multi-Objective Reinforcement Learning (MORL) approaches fail to scale to the large state and action spaces inherent in real-world grid operations. Here we present a two-phase, efficient and scalable Multi-Objective Optimisation (MOO) method designed for grid topology control, combining an efficient RL learning phase with a rapid planning phase to generate day-ahead plans for unseen scenarios. We validate our approach using historical data from TenneT, a European Transmission System Operator (TSO), demonstrating minimal deployment time, generating day-ahead plans within 4-7 minutes with strong performance. These results underline the potential of our scalable method to support real-world power grid management, offering a practical, computationally efficient, and time-effective tool for operational planning. Based on current congestion costs and inefficiencies in grid operations, adopting our approach by TSOs could potentially save millions of euros annually, providing a compelling economic incentive for its integration in the control room.
Safeguarding the Future of Mobility: Cybersecurity Issues and Solutions for Infrastructure Associated with Electric Vehicle Charging
Venue: arXiv cs.CR 2025
Year: 2025

The development of an ecosystem that balances consumer convenience and security is imperative given the expanding market for electric vehicles (EVs). The vast amount of data that EV charging station management systems (EVCSMSs) give is powered by the Internet of Things (IoT) ecosystem. Intrusion Detection Systems (IDSs), which track network traffic to spot potentially dangerous data exchanges in IT and IoT contexts, are constantly improving in terms of efficacy and accuracy. Intrusion detection is becoming a major topic in academia because of the acceleration of IDS development caused by machine learning and deep learning techniques. The goal of the research presented in this paper is to use a machine-learning-based intrusion detection system with low false-positive rates and high accuracy to safeguard the ecosystem of EV charging stations (EVCS).
Optimization Strategies for Enhancing Resource Efficiency in Transformers & Large Language Models
Venue: arXiv cs.LG 2025
Year: 2025

Advancements in Natural Language Processing are heavily reliant on the Transformer architecture, whose improvements come at substantial resource costs due to ever-growing model sizes. This study explores optimization techniques, including Quantization, Knowledge Distillation, and Pruning, focusing on energy and computational efficiency while retaining performance. Among standalone methods, 4-bit Quantization significantly reduces energy use with minimal accuracy loss. Hybrid approaches, like NVIDIA's Minitron approach combining KD and Structured Pruning, further demonstrate promising trade-offs between size reduction and accuracy retention. A novel optimization equation is introduced, offering a flexible framework for comparing various methods. Through the investigation of these compression methods, we provide valuable insights for developing more sustainable and efficient LLMs, shining a light on the often-ignored concern of energy efficiency.
Evaluating Large Language Models in Vulnerability Detection Under Variable Context Windows
Venue: arXiv cs.CR 2025
Year: 2025

This study examines the impact of tokenized Java code length on the accuracy and explicitness of ten major LLMs in vulnerability detection. Using chi-square tests and known ground truth, we found inconsistencies across models: some, like GPT-4, Mistral, and Mixtral, showed robustness, while others exhibited a significant link between tokenized length and performance. We recommend future LLM development focus on minimizing the influence of input length for better vulnerability detection. Additionally, preprocessing techniques that reduce token count while preserving code structure could enhance LLM accuracy and explicitness in these tasks.
Large Capacity Data Hiding in Binary Image black and white mixed regions
Venue: arXiv cs.CR 2025
Year: 2025

Information hiding technology utilizes the insensitivity of human sensory organs to redundant data, hiding confidential information in the redundant data of these public digital media, and then transmitting it. The carrier media after hiding secret information only displays its own characteristics, which can ensure the transmission of confidential information without being detected, thereby greatly improving the security of the information. In theory, any digital media including image, video, audio, and text can serve as a host carrier. Among them, hiding information in binary images poses great challenges. As we know, any information hiding method involves modifying the data of the host carrier. The more information hidden, the more data of the host carrier are modified. In this paper, we propose information hiding in the black-and-white mixed region of binary images, which can greatly reduce visual distortion. In addition, we propose an efficient encoding to achieve high-capacity information hiding while ensuring image semantics. By selecting binary images of different themes, we conduct experiments. The experimental results prove the feasibility of our technique and verify the expected performance. Since the candidate units for information hiding are selected from equally sized blocks that the image is divided into, and the hiding and extraction of information are based on a shared encoding table, the computational cost is very low, making it suitable for real-time information hiding applications.
LLM Cyber Evaluations Don't Capture Real-World Risk
Venue: arXiv cs.CR 2025
Year: 2025

Large language models (LLMs) are demonstrating increasing prowess in cybersecurity applications, creating creating inherent risks alongside their potential for strengthening defenses. In this position paper, we argue that current efforts to evaluate risks posed by these capabilities are misaligned with the goal of understanding real-world impact. Evaluating LLM cybersecurity risk requires more than just measuring model capabilities -- it demands a comprehensive risk assessment that incorporates analysis of threat actor adoption behavior and potential for impact. We propose a risk assessment framework for LLM cyber capabilities and apply it to a case study of language models used as cybersecurity assistants. Our evaluation of frontier models reveals high compliance rates but moderate accuracy on realistic cyber assistance tasks. However, our framework suggests that this particular use case presents only moderate risk due to limited operational advantages and impact potential. Based on these findings, we recommend several improvements to align research priorities with real-world impact assessment, including closer academia-industry collaboration, more realistic modeling of attacker behavior, and inclusion of economic metrics in evaluations. This work represents an important step toward more effective assessment and mitigation of LLM-enabled cybersecurity risks.
CerraData-4MM: A multimodal benchmark dataset on Cerrado for land use and land cover classification
Venue: arXiv cs.CV 2025
Year: 2025

The Cerrado faces increasing environmental pressures, necessitating accurate land use and land cover (LULC) mapping despite challenges such as class imbalance and visually similar categories. To address this, we present CerraData-4MM, a multimodal dataset combining Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 MultiSpectral Imagery (MSI) with 10m spatial resolution. The dataset includes two hierarchical classification levels with 7 and 14 classes, respectively, focusing on the diverse Bico do Papagaio ecoregion. We highlight CerraData-4MM's capacity to benchmark advanced semantic segmentation techniques by evaluating a standard U-Net and a more sophisticated Vision Transformer (ViT) model. The ViT achieves superior performance in multimodal scenarios, with the highest macro F1-score of 57.60% and a mean Intersection over Union (mIoU) of 49.05% at the first hierarchical level. Both models struggle with minority classes, particularly at the second hierarchical level, where U-Net's performance drops to an F1-score of 18.16%. Class balancing improves representation for underrepresented classes but reduces overall accuracy, underscoring the trade-off in weighted training. CerraData-4MM offers a challenging benchmark for advancing deep learning models to handle class imbalance and multimodal data fusion. Code, trained models, and data are publicly available at https://github.com/ai4luc/CerraData-4MM.