arXiv:2508.18609v4 Announce Type: replace-cross Abstract: Post-Training Quantization (PTQ) is a critical strategy for efficient Large Language Models (LLMs) deployment. However, existing scaling laws primarily focus on general performance, overlooking crucial fine-grained factors and how quantization differentially impacts diverse knowledge capabilities. To address this, we establish Task-Stratified Knowledge Scaling Laws. By stratifying capabilities into memorization, application, […]
Graph-Theoretic Models for the Prediction of Molecular Measurements
arXiv:2604.19840v1 Announce Type: cross Abstract: Graph-theoretic approaches offer simplicity, interpretability, and low computational cost for molecular property prediction. Among these, the model proposed by Mukwembi and Nyabadza, based on the external activity $D(G)$ and internal activity $zeta(G)$ indices, achieved strong results on a small flavonoid dataset. However, its ability to generalize to larger and chemically […]
EvoForest: A Novel Machine-Learning Paradigm via Open-Ended Evolution of Computational Graphs
arXiv:2604.19761v1 Announce Type: new Abstract: Modern machine learning is still largely organized around a single recipe: choose a parameterized model family and optimize its weights. Although highly successful, this paradigm is too narrow for many structured prediction problems, where the main bottleneck is not parameter fitting but discovering what should be computed from the data. […]
Depression Risk Assessment in Social Media via Large Language Models
arXiv:2604.19887v1 Announce Type: cross Abstract: Depression is one of the most prevalent and debilitating mental health conditions worldwide, frequently underdiagnosed and undertreated. The proliferation of social media platforms provides a rich source of naturalistic linguistic signals for the automated monitoring of psychological well-being. In this work, we propose a system based on Large Language Models […]
Integrated AI Nodule Detection and Diagnosis for Lung Cancer Screening Beyond Size and Growth-Based Standards Compared with Radiologists and Leading Models
arXiv:2512.00281v2 Announce Type: replace-cross Abstract: Early detection of malignant lung nodules remains limited by reliance on size- and growth-based screening criteria, which can delay diagnosis. We present an integrated AI system that – unlike conventional CADe or CADx approaches – jointly performs nodule detection and malignancy assessment directly at the nodule level from low-dose CT […]
Bias in the Tails: How Name-conditioned Evaluative Framing in Resume Summaries Destabilizes LLM-based Hiring
arXiv:2604.19984v1 Announce Type: cross Abstract: Research has documented LLMs’ name-based bias in hiring and salary recommendations. In this paper, we instead consider a setting where LLMs generate candidate summaries for downstream assessment. In a large-scale controlled study, we analyze nearly one million resume summaries produced by 4 models under systematic race-gender name perturbations, using synthetic […]
From Actions to Understanding: Conformal Interpretability of Temporal Concepts in LLM Agents
arXiv:2604.19775v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed as autonomous agents capable of reasoning, planning, and acting within interactive environments. Despite their growing capability to perform multi-step reasoning and decision-making tasks, internal mechanisms guiding their sequential behavior remain opaque. This paper presents a framework for interpreting the temporal evolution of concepts […]
Auditing and Controlling AI Agent Actions in Spreadsheets
arXiv:2604.20070v1 Announce Type: cross Abstract: Advances in AI agent capabilities have outpaced users’ ability to meaningfully oversee their execution. AI agents can perform sophisticated, multi-step knowledge work autonomously from start to finish, yet this process remains effectively inaccessible during execution, often buried within large volumes of intermediate reasoning and outputs: by the time users receive […]
Rays as Pixels: Learning A Joint Distribution of Videos and Camera Trajectories
arXiv:2604.09429v3 Announce Type: replace-cross Abstract: Recovering camera parameters from images and rendering scenes from novel viewpoints have been treated as separate tasks in computer vision and graphics. This separation breaks down when image coverage is sparse or poses are ambiguous, since each task depends on what the other produces. We propose Rays as Pixels, a […]
Physics-Enhanced Deep Learning for Proactive Thermal Runaway Forecasting in Li-Ion Batteries
arXiv:2604.20175v1 Announce Type: cross Abstract: Accurate prediction of thermal runaway in lithium-ion batteries is essential for ensuring the safety, efficiency, and reliability of modern energy storage systems. Conventional data-driven approaches, such as Long Short-Term Memory (LSTM) networks, can capture complex temporal dependencies but often violate thermodynamic principles, resulting in physically inconsistent predictions. Conversely, physics-based thermal […]
Using Learning Theories to Evolve Human-Centered XAI: Future Perspectives and Challenges
arXiv:2604.19788v1 Announce Type: new Abstract: As Artificial Intelligence (AI) systems continue to grow in size and complexity, so does the difficulty of the quest for AI transparency. In a world of large models and complex AI systems, why do we explain AI and what should we explain? While explanations serve multiple functions, in the face […]
uLEAD-TabPFN: Uncertainty-aware Dependency-based Anomaly Detection with TabPFN
arXiv:2604.20255v1 Announce Type: cross Abstract: Anomaly detection in tabular data is challenging due to high dimensionality, complex feature dependencies, and heterogeneous noise. Many existing methods rely on proximity-based cues and may miss anomalies caused by violations of complex feature dependencies. Dependency-based anomaly detection provides a principled alternative by identifying anomalies as violations of dependencies among […]