arXiv:2512.19909v1 Announce Type: cross Abstract: Recent developments in non-ergodic ground-motion models (GMMs) explicitly model systematic spatial variations in source, site, and path effects, reducing standard deviation to 30-40% of ergodic models and enabling more accurate site-specific seismic hazard analysis. Current non-ergodic GMMs rely on Gaussian Process (GP) methods with prescribed correlation functions and thus have […]
Interpretable Deep Learning for Stock Returns: A Consensus-Bottleneck Asset Pricing Model
arXiv:2512.16251v2 Announce Type: replace-cross Abstract: We introduce the Consensus-Bottleneck Asset Pricing Model (CB-APM), a partially interpretable neural network that replicates the reasoning processes of sell-side analysts by capturing how dispersed investor beliefs are compressed into asset prices through a consensus formation process. By modeling this “bottleneck” to summarize firm- and macro-level information, CB-APM not only […]
TAVID: Text-Driven Audio-Visual Interactive Dialogue Generation
arXiv:2512.20296v1 Announce Type: cross Abstract: The objective of this paper is to jointly synthesize interactive videos and conversational speech from text and reference images. With the ultimate goal of building human-like conversational systems, recent studies have explored talking or listening head generation as well as conversational speech generation. However, these works are typically studied in […]
Conservative Bias in Multi-Teacher Learning: Why Agents Prefer Low-Reward Advisors
arXiv:2512.17180v2 Announce Type: replace-cross Abstract: Interactive reinforcement learning (IRL) has shown promise in enabling autonomous agents and robots to learn complex behaviours from human teachers, yet the dynamics of teacher selection remain poorly understood. This paper reveals an unexpected phenomenon in IRL: when given a choice between teachers with different reward structures, learning agents overwhelmingly […]
Patterns vs. Patients: Evaluating LLMs against Mental Health Professionals on Personality Disorder Diagnosis through First-Person Narratives
arXiv:2512.20298v1 Announce Type: cross Abstract: Growing reliance on LLMs for psychiatric self-assessment raises questions about their ability to interpret qualitative patient narratives. We present the first direct comparison between state-of-the-art LLMs and mental health professionals in diagnosing Borderline (BPD) and Narcissistic (NPD) Personality Disorders utilizing Polish-language first-person autobiographical accounts. We show that the top-performing Gemini […]
Software Vulnerability Management in the Era of Artificial Intelligence: An Industry Perspective
arXiv:2512.18261v2 Announce Type: replace-cross Abstract: Artificial Intelligence (AI) has revolutionized software development, particularly by automating repetitive tasks and improving developer productivity. While these advancements are well-documented, the use of AI-powered tools for Software Vulnerability Management (SVM), such as vulnerability detection and repair, remains underexplored in industry settings. To bridge this gap, our study aims to […]
KnowVal: A Knowledge-Augmented and Value-Guided Autonomous Driving System
arXiv:2512.20299v1 Announce Type: cross Abstract: Visual-language reasoning, driving knowledge, and value alignment are essential for advanced autonomous driving systems. However, existing approaches largely rely on data-driven learning, making it difficult to capture the complex logic underlying decision-making through imitation or limited reinforcement rewards. To address this, we propose KnowVal, a new autonomous driving system that […]
Fusion of Multiscale Features Via Centralized Sparse-attention Network for EEG Decoding
arXiv:2512.18689v2 Announce Type: replace-cross Abstract: Electroencephalography (EEG) signal decoding is a key technology that translates brain activity into executable commands, laying the foundation for direct brain-machine interfacing and intelligent interaction. To address the inherent spatiotemporal heterogeneity of EEG signals, this paper proposes a multi-branch parallel architecture, where each temporal scale is equipped with an independent […]
One Permutation Is All You Need: Fast, Reliable Variable Importance and Model Stress-Testing
arXiv:2512.13892v2 Announce Type: replace-cross Abstract: Reliable estimation of feature contributions in machine learning models is essential for trust, transparency and regulatory compliance, especially when models are proprietary or otherwise operate as black boxes. While permutation-based methods are a standard tool for this task, classical implementations rely on repeated random permutations, introducing computational overhead and stochastic […]
The Erasure Illusion: Stress-Testing the Generalization of LLM Forgetting Evaluation
arXiv:2512.19025v2 Announce Type: replace-cross Abstract: Machine unlearning aims to remove specific data influences from trained models, a capability essential for adhering to copyright laws and ensuring AI safety. Current unlearning metrics typically measure success by monitoring the model’s performance degradation on the specific unlearning dataset ($D_u$). We argue that for Large Language Models (LLMs), this […]