arXiv:2608.19234v1 Announce Type: new
Abstract: Decision-making in complex systems often involves dealing with imprecise or uncertain information, frequently represented using fuzzy sets, particularly Triangular Fuzzy Numbers (TFNs). A crucial aspect of many fuzzy methods is the quantification of d...
Quantum Kernel Estimation for the Discovery of Early Lung Cancer Detection
arXiv:2608.19304v1 Announce Type: new
Abstract: Lung cancer screening with low-dose chest computed tomography reduces mortality, but its impact is limited by uptake, adherence, and management challenges. Blood-based cell-free DNA (cfDNA) biomarkers offer a complementary approach, although early det...
How to Build a Robust RAG System with Minimal Resources
In this article, you will learn how to design, assemble, and tune a retrieval-augmented generation system that runs entirely on a standard laptop, without cloud...
What Can Artificial Intelligence Learn from Medicine? Generative Analogies and Reliable Machine Learning Systems
arXiv:2608.18186v1 Announce Type: new
Abstract: In the past few years, machine learning (ML) has been widely (and to an extent, successfully) implemented in medicine. However, uncertainties surrounding ML have made it difficult to establish the bases of its epistemic and methodological warrants. In...
Large Language Models in Mental Health: A Systematic Review of Applications, Innovations, and Ethical Challenges
arXiv:2608.18080v1 Announce Type: new
Abstract: We present a review on the applications of large language models (LLMs) in health, e.g., social media analysis, clinical conversational agents, therapy support tools, prompt engineering, multimodal learning, and ethical considerations. We integrate fi...
FedPref: Federated Preference Learning for Structured Radiology Report Extraction
arXiv:2608.16971v1 Announce Type: new
Abstract: Radiology reports describe findings and locations in free text, but downstream search and analysis require these relations in a fixed schema. Learning this extraction requires labels that are unevenly distributed across institutions: smaller hospitals...
Proactive Road Safety Intervention in Australia: Predicting Risky Driving Hotspots from Connected Vehicle Data
arXiv:2608.16913v1 Announce Type: new
Abstract: Road safety monitoring has historically been reactive, relying on crash-record analysis after fatalities and injuries have already occurred. Proactive identification of high-risk locations and dangerous driving behaviour before incidents occur is a cr...
Global AI Regulations for FAIR and Ethics in High-Risk Use Cases: A Comparative Review
arXiv:2608.14562v1 Announce Type: new
Abstract: AI governance is shifting from voluntary ethics to enforceable, risk-based regulation, yet cross-jurisdictional divergence creates compliance uncertainty for operators of high-stakes AI. We present a comparative matrix for the EU, US, and China that m...
FLOPs vs Real Work: The Importance of Replication in AI Efficiency Assessment
arXiv:2608.14550v1 Announce Type: new
Abstract: AI efficiency has recently taken the spotlight in both academy and industry due to massive model scales, high energy demands, and environmental costs. While reporting Floating Point Operations (FLOPs) is a traditional approach for assessing computatio...
Geometry Is Not Robustness: A Trajectory-Level Study of PGD Evaluation
arXiv:2608.14594v1 Announce Type: new
Abstract: Projected Gradient Descent (PGD) is widely used to evaluate adversarial robustness, typically via final adversarial accuracy, which does not capture model behaviour throughout the attack. Recent work proposes trajectory-level diagnostics, such as loss...
arXiv:2608.13590v1 Announce Type: new
Abstract: XGBoost is a very popular and powerful method for prediction. It iteratively fits simple decision trees to the residuals of the previous step. An efficient and scalable implementation is available. The standard loss function for XGBoost is the quadrat...
Training-Free Knowledge Transfer Across Model Scales through Activation-Guided Pruning
arXiv:2608.13596v1 Announce Type: new
Abstract: Heterogeneous model fusion seeks to combine models that differ in tasks, initializations, architectures, or scales. We study an underexplored cross-scale setting: improving a small recipient language model with a stronger donor despite substantial arc...
Which Site, and When: A Free-Satellite-Data Test of Himalayan Glacial Lake Bursts, Landslides, and Ice Floods
arXiv:2608.12422v1 Announce Type: new
Abstract: Two free satellite signals carry real information about glacial-lake outburst risk in the Nepal Himalaya: radar interferometry sees a moraine dam slowly sagging, and satellite weather marks the weeks when a primed lake is under stress. A companion fea...
MARCH: Scaling Recurrent Memory with Content-Routed State Anchors
arXiv:2608.12435v1 Announce Type: new
Abstract: Transformers owe much of their strong long-context retrieval capability to a token-level memory that grows with context length. This flexibility, however, incurs a quadratic computation complexity during training and a key--value cache that grows line...
Detecting a Route Flip Is Easier Than Knowing Whether to Fix It: Causal Route-Mediated Damage in Quantized Mixture-of-Experts
arXiv:2608.11212v1 Announce Type: new
Abstract: Top-k Mixture-of-Experts (MoE) routing is discontinuous, so a deployment-motivated numerical disturbance -- simulated 4-bit KV-cache quantization read by a protected BF16 gate -- pushes tokens across decision boundaries and flips which experts fire. T...
Terminal Symmetry as a Decision Resource: Statewise Refinement for Anytime Verified Construction
arXiv:2608.11318v1 Announce Type: new
Abstract: Many sequential construction tasks exhibit exact symmetry at completion while their execution remains directed and history-dependent. We develop a decision-resource view of terminal symmetry: process evidence supplies directionality, terminal correspo...
arXiv:2608.11256v1 Announce Type: new
Abstract: Institutions use commercial AI detectors for academic integrity, yet detectors cannot distinguish AI editing from full LLM drafts and may treat both as misconduct. In a controlled study of published English abstracts (four domains; 2013 to 2015 vs. 20...
In this article, you will learn the conceptual and practical differences between retrieval and memory in agentic AI systems, and how to combine both effectively....
Towards Sustainable Artificial Intelligence: A Comprehensive Review and Comparative Analysis of Deep Learning Models' Carbon Footprint
arXiv:2608.09998v1 Announce Type: new
Abstract: Artificial Intelligence (AI) and Machine Learning (ML) have become powerful tools for supporting and automating complex human tasks. Despite their benefits, growing attention has been directed toward their environmental implications, primarily due to ...
MIDAS: Mutual Information Disentanglement with Uncertainty-Aware Fusion for Incomplete Multimodal Sentiment Analysis
arXiv:2608.09986v1 Announce Type: new
Abstract: Most existing multimodal sentiment analysis approaches assume access to complete multimodal inputs. However, real-world applications frequently encounter incomplete or corrupted modalities, posing a critical challenge. Although several methods have be...
arXiv:2608.09997v1 Announce Type: new
Abstract: Transformers have had a profound impact on the world of language processing and computer vision. As efforts to answer the million-dollar question of ``How does a Transformer learn?" have been increasing, existing interpretability studies primarily ana...
Uncertainty-Aware Ensemble Deep Randomized Neural Networks for Classification
arXiv:2608.10007v1 Announce Type: new
Abstract: The current state-of-the-art (SOTA) deep randomized neural networks, such as deep Random Vector Functional Link (dRVFL) and ensemble deep RVFL (edRVFL), treat all training samples uniformly, which limits their robustness and effectiveness when applied...
Towards an Argumentative Foundation for Evaluative AI
arXiv:2608.07473v1 Announce Type: new
Abstract: Evaluative AI (EAI) has been recently proposed as a way to support human decision-making, not by producing a single recommendation, but by presenting competing hypotheses together with evidence for and against each. In this position paper, we advocate...
Tracing sources of epistemic uncertainty in deep learning predictions: homo- and hetero-scedastic linearized estimators
arXiv:2608.07630v1 Announce Type: new
Abstract: We adapt two classical statistical estimators for quantifying uncertainty to modern deep learning, in order to provide clearer insights into uncertainty attributable to two sources : aleatoric uncertainty, or locally scarce data. Our approach leverage...