NeuronFuzz: Safety Neuron Guided Fuzzing for LLM Safety Evaluation
arXiv:2608.26222v1 Announce Type: new
Abstract: Safety evaluation is critical for assessing whether aligned Large Language Models (LLMs) remain robust against jailbreak attacks. Existing automated testing methods, however, largely rely on response-level feedback: each candidate prompt typically req...
Large Models for Battery Prognostics and Health Management: A Review and Future Roadmap
arXiv:2608.26111v1 Announce Type: new
Abstract: Battery Prognostics and Health Management (BPHM) is critical for ensuring the safe, reliable, and cost-effective operation of batteries across electric vehicles, grid storage, and consumer electronics. Conventional BPHM approaches, including physics-b...
AI-Powered Mental Health Chatbots in Africa: A Systematic Review and Culturally Adaptive Framework
arXiv:2608.24890v1 Announce Type: new
Abstract: Mental health challenges in Africa remain under-addressed due to inadequate infrastructure, stigma, and a chronic shortage of professionals. Artificial Intelligence (AI)-powered chatbots are emerging globally as low-cost, accessible tools that can off...
SIMGUIDE: Procedurally Grounded Multi-Context Representations for Personalized Agent Planning
arXiv:2608.24888v1 Announce Type: new
Abstract: Personalized AI agents overwhelmingly treat users as single entities: a flat profile concatenated into a prompt. This fails when the same person holds different priorities across life contexts -- and fails catastrophically when those priorities confli...
arXiv:2608.24937v1 Announce Type: new
Abstract: Multi-Modal Anomaly Detection (MMAD) detects rare abnormal events from heterogeneous data sources and is increasingly used in safety- and reliability-critical applications such as industrial inspection and cybersecurity. Yet the literature is fragment...
When Does Frequency Decomposition Benefit Physics-Informed Neural Networks? A Preliminary Ablation Study
arXiv:2608.24940v1 Announce Type: new
Abstract: Partial differential equations (PDEs) often have high-frequency and multi-scale features that neural networks struggle to approximate. Physics-Informed Neural Networks (PINNs) build the governing equations directly into training, but suffer from spect...
Learn Vectorized Thinking in Python Through Examples
In this article, you will learn how to think in terms of vectorized operations using NumPy, replacing slow Python loops with efficient array-level computations. Topics...
Comparing Local Tool Calling: Gemma 4 vs. Llama 3 vs. Mistral
In this article, you will learn how Gemma 4, Llama 3, and Mistral implement tool calling locally, and what trade-offs each model family presents for...
AIREP: A Protocol for Per-Decision Evidence in AI Runtime Governance
arXiv:2608.21363v1 Announce Type: new
Abstract: A protocol is presented for recording the governance decisions of automated AI runtimes. When a runtime releases, blocks, defers, redacts, or escalates an individual output, AIREP records that decision as a single signed object that any party can chec...
KVBoost: Chunk-Level Key-Value Cache Reuse with Deviation-Guided Recomputation for Efficient Large Language Model Inference
arXiv:2608.21362v1 Announce Type: new
Abstract: Transformer-based large language models (LLMs) incur high prefill latency because key-value (KV) tensors must be recomputed for each request. Existing prefix-caching systems reduce this cost but require prompts to share a leading contiguous prefix, li...
arXiv:2608.21366v1 Announce Type: new
Abstract: Driven by massive amounts of web-scale data, generative AI (GenAI) has achieved remarkable progress, enabling various applications in diverse sectors. The advances of GenAI have actuated practitioners to use AI-synthesized data for training next-gener...
LitReview Arena: Evaluating Literature Review Agents with Battle-Style Peer Review Platform
arXiv:2608.21374v1 Announce Type: new
Abstract: Literature reviews are essential to scientific progress, but rigorously evaluating automatically generated reviews remains difficult because many aspects of research utility depend on expert judgment rather than reference-overlap metrics. We introduce...
Interpretable Multimodal Classification with Linear Discriminant Tree Ensembles
arXiv:2608.20384v1 Announce Type: new
Abstract: Multimodal affect and behaviour classifiers that fuse heterogeneous text, audio, and visual streams must simultaneously achieve competitive accuracy and produce human-understandable explanations of the cues driving their decisions -- a dual objective ...
Machine Learning and ARIMA Model Averaging for Adaptive Public Health Forecasting: Comparative Evaluation and an Ontario COVID-19 Case Study
arXiv:2608.20406v1 Announce Type: new
Abstract: Public health forecasts must respond to abrupt changes in surveillance data without over-extrapolating noise, reporting artifacts, or temporary trends. We evaluated autoregressive integrated moving average (ARIMA), random forest, and extreme gradient ...
Approximate Homomorphisms and Convergent Representations in Transducers
arXiv:2608.20428v1 Announce Type: new
Abstract: We study the stability of minimal representations of controlled stochastic processes (in particular, transducers) under perturbations. This question is motivated by recent experiments finding predictive-state structure in the latent representations of...
BF1: A Causal Dyadic Sparse-Attention Retrofit for Efficient Long-Context Transformers
arXiv:2608.20427v1 Announce Type: new
Abstract: Dense causal attention remains expensive at long context even when implemented with highly optimized exact kernels. We study BF1, a deterministic block-aligned dyadic sparse-attention route that combines a small exact local neighborhood, a global firs...
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...
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...
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...
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...
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...
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...