OpenRaiser/Intern-Atlas huggingface dataset:
Evaluating LLM Alignment on Personality Inference from Real-World Interview Data
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) are increasingly deployed in roles requiring nuanced psychological understanding, such as emotional support agents, counselors, and decision-making assistants. However, their ability to interpret human personality traits, a critical aspect of such applications, remains unexplored, particularly in ecologically valid conversational settings. While prior work has simulated LLM "personas" using discrete Big Five labels on social media data, the alignment of LLMs with continuous, ground-truth personality assessments derived from natural interactions is largely unexamined. To address this gap, we introduce a novel benchmark comprising semi-structured interview transcripts paired with validated continuous Big Five trait scores. Using this dataset, we systematically evaluate LLM performance across three paradigms: (1) zero-shot and chain-of-thought prompting with GPT-4.1 Mini, (2) LoRA-based fine-tuning applied to both RoBERTa and Meta-LLaMA architectures, and (3) regression using static embeddings from pretrained BERT and OpenAI's text-embedding-3-small. Our results reveal that all Pearson correlations between model predictions and ground-truth personality traits remain below 0.26, highlighting the limited alignment of current LLMs with validated psychological constructs. Chain-of-thought prompting offers minimal gains over zero-shot, suggesting that personality inference relies more on latent semantic representation than explicit reasoning. These findings underscore the challenges of aligning LLMs with complex human attributes and motivate future work on trait-specific prompting, context-aware modeling, and alignment-oriented fine-tuning.
AI Behavioral Science
Venue: arXiv cs.HC 2025
Year: 2025

We discuss the three main areas comprising the new and emerging field of "AI Behavioral Science". This includes not only how AI can enhance research in the behavioral sciences, but also how the behavioral sciences can be used to study and better design AI and to understand how the world will change as AI and humans interact in increasingly layered and complex ways.
Proximity-Based Evidence Retrieval for Uncertainty-Aware Neural Networks
Venue: arXiv cs.CV 2025
Year: 2025

This work proposes an evidence-retrieval mechanism for uncertainty-aware decision-making that replaces a single global cutoff with an evidence-conditioned, instance-adaptive criterion. For each test instance, proximal exemplars are retrieved in an embedding space; their predictive distributions are fused via Dempster-Shafer theory. The resulting fused belief acts as a per-instance thresholding mechanism. Because the supporting evidences are explicit, decisions are transparent and auditable. Experiments on CIFAR-10/100 with BiT and ViT backbones show higher or comparable uncertainty-aware performance with materially fewer confidently incorrect outcomes and a sustainable review load compared with applying threshold on prediction entropy. Notably, only a few evidences are sufficient to realize these gains; increasing the evidence set yields only modest changes. These results indicate that evidence-conditioned tagging provides a more reliable and interpretable alternative to fixed prediction entropy thresholds for operational uncertainty-aware decision-making.
Defining a classification system for augmentation technology in socio-technical terms
Venue: arXiv cs.CY 2025
Year: 2025

This short paper provides a means to classify augmentation technologies to reconceptualize them as sociotechnical, discursive and rhetorical phenomena, rather than only through technological classifications. It identifies a set of value systems that constitute augmentation technologies within discourses, namely, the intent to enhance, automate, and build efficiency. This short paper makes a contribution to digital literacy surrounding augmentation technology emergence, as well as the more specific area of AI literacy, which can help identify unintended consequences implied at the design stages of these technologies.
All Models Are Wrong, But Can They Be Useful? Lessons from COVID-19 Agent-Based Models: A Systematic Review
Venue: arXiv cs.MA 2025
Year: 2025

The COVID-19 pandemic prompted a surge in computational models to simulate disease dynamics and guide interventions. Agent-based models (ABMs) are well-suited to capture population and environmental heterogeneity, but their rapid deployment raised questions about utility for health policy. We systematically reviewed 536 COVID-19 ABM studies published from January 2020 to December 2023, retrieved from Web of Science, PubMed, and Wiley on January 30, 2024. Studies were included if they used ABMs to simulate COVID-19 transmission, where reviews were excluded. Studies were assessed against nine criteria of model usefulness, including transparency and re-use, interdisciplinary collaboration and stakeholder engagement, and evaluation practices. Publications peaked in late 2021 and were concentrated in a few countries. Most models explored behavioral or policy interventions (n = 294, 54.85%) rather than real-time forecasting (n = 9, 1.68%). While most described model assumptions (n = 491, 91.60%), fewer disclosed limitations (n = 349, 65.11%), shared code (n = 219, 40.86%), or built on existing models (n = 195, 36.38%). Standardized reporting protocols (n = 36, 6.72%) and stakeholder engagement were rare (13.62%, n = 73). Only 2.24% (n = 12) described a comprehensive validation framework, though uncertainty was often quantified (n = 407, 75.93%). Limitations of this review include underrepresentation of non-English studies, subjective data extraction, variability in study quality, and limited generalizability. Overall, COVID-19 ABMs advanced quickly, but lacked transparency, accessibility, and participatory engagement. Stronger standards are needed for ABMs to serve as reliable decision-support tools in future public health crises.
Synthetic Data and the Shifting Ground of Truth
Venue: arXiv cs.CY 2025
Year: 2025

The emergence of synthetic data for privacy protection, training data generation, or simply convenient access to quasi-realistic data in any shape or volume complicates the concept of ground truth. Synthetic data mimic real-world observations, but do not refer to external features. This lack of a representational relationship, however, not prevent researchers from using synthetic data as training data for AI models and ground truth repositories. It is claimed that the lack of data realism is not merely an acceptable tradeoff, but often leads to better model performance than realistic data: compensate for known biases, prevent overfitting and support generalization, and make the models more robust in dealing with unexpected outliers. Indeed, injecting noisy and outright implausible data into training sets can be beneficial for the model. This greatly complicates usual assumptions based on which representational accuracy determines data fidelity (garbage in - garbage out). Furthermore, ground truth becomes a self-referential affair, in which the labels used as a ground truth repository are themselves synthetic products of a generative model and as such not connected to real-world observations. My paper examines how ML researchers and practitioners bootstrap ground truth under such paradoxical circumstances without relying on the stable ground of representation and real-world reference. It will also reflect on the broader implications of a shift from a representational to what could be described as a mimetic or iconic concept of data.
To whom did my vote go?
Venue: arXiv cs.CY 2025
Year: 2025

Single Transferable Vote (STV) counting, used in several jurisdictions in Australia, is a system for choosing multiple election winners given voters' preferences among candidates. The system is complex and it is not always obvious how an individual's vote contributes to candidates' tallies across rounds of tabulation. This short paper presents a demonstration system that allows voters to enter an example vote in a past Australian STV election, and see: (i)~how that vote would have been transferred between candidates; and (ii)~how much that vote would have contributed to the tallies of relevant candidates, across rounds of tabulation.
Cooperative Target Detection with AUVs: A Dual-Timescale Hierarchical MARDL Approach
Venue: arXiv cs.RO 2025
Year: 2025

Autonomous Underwater Vehicles (AUVs) have shown great potential for cooperative detection and reconnaissance. However, collaborative AUV communications introduce risks of exposure. In adversarial environments, achieving efficient collaboration while ensuring covert operations becomes a key challenge for underwater cooperative missions. In this paper, we propose a novel dual time-scale Hierarchical Multi-Agent Proximal Policy Optimization (H-MAPPO) framework. The high-level component determines the individuals participating in the task based on a central AUV, while the low-level component reduces exposure probabilities through power and trajectory control by the participating AUVs. Simulation results show that the proposed framework achieves rapid convergence, outperforms benchmark algorithms in terms of performance, and maximizes long-term cooperative efficiency while ensuring covert operations.
Unified Spatiotemporal Physics-Informed Learning (USPIL): A Framework for Modeling Complex Predator-Prey Dynamics
Venue: arXiv cs.LG 2025
Year: 2025

Ecological systems exhibit complex multi-scale dynamics that challenge traditional modeling. New methods must capture temporal oscillations and emergent spatiotemporal patterns while adhering to conservation principles. We present the Unified Spatiotemporal Physics-Informed Learning (USPIL) framework, a deep learning architecture integrating physics-informed neural networks (PINNs) and conservation laws to model predator-prey dynamics across dimensional scales. The framework provides a unified solution for both ordinary (ODE) and partial (PDE) differential equation systems, describing temporal cycles and reaction-diffusion patterns within a single neural network architecture. Our methodology uses automatic differentiation to enforce physics constraints and adaptive loss weighting to balance data fidelity with physical consistency. Applied to the Lotka-Volterra system, USPIL achieves 98.9% correlation for 1D temporal dynamics (loss: 0.0219, MAE: 0.0184) and captures complex spiral waves in 2D systems (loss: 4.7656, pattern correlation: 0.94). Validation confirms conservation law adherence within 0.5% and shows a 10-50x computational speedup for inference compared to numerical solvers. USPIL also enables mechanistic understanding through interpretable physics constraints, facilitating parameter discovery and sensitivity analysis not possible with purely data-driven methods. Its ability to transition between dimensional formulations opens new avenues for multi-scale ecological modeling. These capabilities make USPIL a transformative tool for ecological forecasting, conservation planning, and understanding ecosystem resilience, establishing physics-informed deep learning as a powerful and scientifically rigorous paradigm.
Catalpa: GC for a Low-Variance Software Stack
Venue: arXiv cs.PL 2025
Year: 2025

The performance of an application/runtime is usually conceptualized as a continuous function where, the lower the amount of memory/time used on a given workload, then the better the compiler/runtime is. However, in practice, good performance of an application is viewed as more of a binary function - either the application responds in under, say 100 ms, and provides a good user experience, or it takes a noticeable amount of time, leaving the user waiting and potentially abandoning the task. Thus, performance really means how often the application is fast enough to meet user expectations, leading industrial developers to focus on the 95th and 99th percentile tail-latencies as heavily, or moreso, than average response time. Our vision is to create a software stack that actively supports these needs via programming language and runtime system design. In this paper we present a novel garbage-collector design, the Catalpa collector, for the Bosque programming language and runtime. This allocator is designed to minimize latency and tail-latency variability while maintaining high-throughput and incurring small memory overheads. To achieve these goals we leverage various features of the Bosque language, including immutability and reference-cycle freedom, to construct a collector that has provably bounded collection pauses, incurs a fixed-constant memory overhead, and ensures starvation freedom for the application!
AR-TMT: Investigating the Impact of Distraction Types on Attention and Behavior in AR-based Trail Making Test
Venue: arXiv cs.HC 2025
Year: 2025

Despite the growing use of AR in safety-critical domains, the field lacks a systematic understanding of how different types of distraction affect user behavior in AR environments. To address this gap, we present AR-TMT, an AR adaptation of the Trail Making Test that spatially renders targets for sequential selection on the Magic Leap 2. We implemented distractions in three categories: top-down, bottom-up, and spatial distraction based on Wolfe's Guided Search model, and captured performance, gaze, motor behavior, and subjective load measures to analyze user attention and behavior. A user study with 34 participants revealed that top-down distraction degraded performance through semantic interference, while bottom-up distraction disrupted initial attentional engagement. Spatial distraction destabilized gaze behavior, leading to more scattered and less structured visual scanning patterns. We also found that performance was correlated with attention control ($R^2 = .20$--$.35$) under object-based distraction conditions, where distractors possessed task-relevant features. The study offers insights into distraction mechanisms and their impact on users, providing opportunities for generalization to ecologically relevant AR tasks while underscoring the need to address the unique demands of AR environments.
Semantic-Enhanced Cross-Modal Place Recognition for Robust Robot Localization
Venue: arXiv cs.CV 2025
Year: 2025

Ensuring accurate localization of robots in environments without GPS capability is a challenging task. Visual Place Recognition (VPR) techniques can potentially achieve this goal, but existing RGB-based methods are sensitive to changes in illumination, weather, and other seasonal changes. Existing cross-modal localization methods leverage the geometric properties of RGB images and 3D LiDAR maps to reduce the sensitivity issues highlighted above. Currently, state-of-the-art methods struggle in complex scenes, fine-grained or high-resolution matching, and situations where changes can occur in viewpoint. In this work, we introduce a framework we call Semantic-Enhanced Cross-Modal Place Recognition (SCM-PR) that combines high-level semantics utilizing RGB images for robust localization in LiDAR maps. Our proposed method introduces: a VMamba backbone for feature extraction of RGB images; a Semantic-Aware Feature Fusion (SAFF) module for using both place descriptors and segmentation masks; LiDAR descriptors that incorporate both semantics and geometry; and a cross-modal semantic attention mechanism in NetVLAD to improve matching. Incorporating the semantic information also was instrumental in designing a Multi-View Semantic-Geometric Matching and a Semantic Consistency Loss, both in a contrastive learning framework. Our experimental work on the KITTI and KITTI-360 datasets show that SCM-PR achieves state-of-the-art performance compared to other cross-modal place recognition methods.
Gender-Neutral Rewriting in Italian: Models, Approaches, and Trade-offs
Venue: arXiv cs.CL 2025
Year: 2025

Gender-neutral rewriting (GNR) aims to reformulate text to eliminate unnecessary gender specifications while preserving meaning, a particularly challenging task in grammatical-gender languages like Italian. In this work, we conduct the first systematic evaluation of state-of-the-art large language models (LLMs) for Italian GNR, introducing a two-dimensional framework that measures both neutrality and semantic fidelity to the input. We compare few-shot prompting across multiple LLMs, fine-tune selected models, and apply targeted cleaning to boost task relevance. Our findings show that open-weight LLMs outperform the only existing model dedicated to GNR in Italian, whereas our fine-tuned models match or exceed the best open-weight LLM's performance at a fraction of its size. Finally, we discuss the trade-off between optimizing the training data for neutrality and meaning preservation.
LivePyxel: Accelerating image annotations with a Python-integrated webcam live streaming
Venue: arXiv cs.CV 2025
Year: 2025

The lack of flexible annotation tools has hindered the deployment of AI models in some scientific areas. Most existing image annotation software requires users to upload a precollected dataset, which limits support for on-demand pipelines and introduces unnecessary steps to acquire images. This constraint is particularly problematic in laboratory environments, where on-site data acquisition from instruments such as microscopes is increasingly common. In this work, we introduce \texttt{LivePixel}, a Python-based graphical user interface that integrates with imaging systems, such as webcams, microscopes, and others, to enable on-site image annotation. LivePyxel is designed to be easy to use through a simple interface that allows users to precisely delimit areas for annotation using tools commonly found in commercial graphics editing software. Of particular interest is the availability of Bézier splines and binary masks, and the software's capacity to work with non-destructive layers that enable high-performance editing. LivePyxel also integrates a wide compatibility across video devices, and it's optimized for object detection operations via the use of OpenCV in combination with high-performance libraries designed to handle matrix and linear algebra operations via Numpy effectively. LivePyxel facilitates seamless data collection and labeling, accelerating the development of AI models in experimental workflows. LivePyxel is freely available at https://github.com/UGarCil/LivePyxel
AQUA-LLM: Evaluating Accuracy, Quantization, and Adversarial Robustness Trade-offs in LLMs for Cybersecurity Question Answering
Venue: arXiv cs.CR 2025
Year: 2025

Large Language Models (LLMs) have recently demonstrated strong potential for cybersecurity question answering (QA), supporting decision-making in real-time threat detection and response workflows. However, their substantial computational demands pose significant challenges for deployment on resource-constrained edge devices. Quantization, a widely adopted model compression technique, can alleviate these constraints. Nevertheless, quantization may degrade model accuracy and increase susceptibility to adversarial attacks. Fine-tuning offers a potential means to mitigate these limitations, but its effectiveness when combined with quantization remains insufficiently explored. Hence, it is essential to understand the trade-offs among accuracy, efficiency, and robustness. We propose AQUA-LLM, an evaluation framework designed to benchmark several state-of-the-art small LLMs under four distinct configurations: base, quantized-only, fine-tuned, and fine-tuned combined with quantization, specifically for cybersecurity QA. Our results demonstrate that quantization alone yields the lowest accuracy and robustness despite improving efficiency. In contrast, combining quantization with fine-tuning enhances both LLM robustness and predictive performance, achieving an optimal balance of accuracy, robustness, and efficiency. These findings highlight the critical need for quantization-aware, robustness-preserving fine-tuning methodologies to enable the robust and efficient deployment of LLMs for cybersecurity QA.
ColonCrafter: A Depth Estimation Model for Colonoscopy Videos Using Diffusion Priors
Venue: arXiv cs.CV 2025
Year: 2025

Three-dimensional (3D) scene understanding in colonoscopy presents significant challenges that necessitate automated methods for accurate depth estimation. However, existing depth estimation models for endoscopy struggle with temporal consistency across video sequences, limiting their applicability for 3D reconstruction. We present ColonCrafter, a diffusion-based depth estimation model that generates temporally consistent depth maps from monocular colonoscopy videos. Our approach learns robust geometric priors from synthetic colonoscopy sequences to generate temporally consistent depth maps. We also introduce a style transfer technique that preserves geometric structure while adapting real clinical videos to match our synthetic training domain. ColonCrafter achieves state-of-the-art zero-shot performance on the C3VD dataset, outperforming both general-purpose and endoscopy-specific approaches. Although full trajectory 3D reconstruction remains a challenge, we demonstrate clinically relevant applications of ColonCrafter, including 3D point cloud generation and surface coverage assessment.
GuardianPWA: Enhancing Security Throughout the Progressive Web App Installation Lifecycle
Venue: arXiv cs.CR 2025
Year: 2025

Progressive Web App (PWA) installation is critical for integrating web and mobile app functionalities, offering a seamless user experience. However, ensuring the security of the PWA installation lifecycle is essential for maintaining user trust and privacy. This paper introduces the GUARDIANPWA framework, a comprehensive approach to analyzing the PWA installation mechanism based on the CIA security principles (Confidentiality, Integrity, and Availability) and identifying areas where browser vendors fail to comply with these principles. Our study revealed 203 instances of non-compliance with security principles, highlighting how these irregularities in the PWA installation lifecycle can lead to potential violations of user privacy. For instance, in Firefox, PWAs installed in private mode incorrectly appear in normal mode, risking user confidentiality. Additionally, 29,465 PWAs are at risk because Samsung Internet does not display origins when PWAs navigate to third-party websites, undermining integrity. These findings were reported to browser vendors, leading to Firefox acknowledging four issues, resolving one, and planning to resolve two others. GUARDIANPWA supports developers by analyzing PWA manifest files for syntactic and semantic correctness, offering actionable recommendations, and helping to create PWAs that align with security best practices. By using GUARDIANPWA, developers and users can address critical security gaps and enhance compliance with CIA principles throughout the PWA installation lifecycle.