Structure and Implementation of New Practical English Textbooks Driven by Artificial Intelligence
arXiv:2609.02981v1 Announce Type: new
Abstract: Artificial intelligence is changing the form of applied English materials from fixed paper sequences to adaptive learning systems that can diagnose learners, recommend tasks, and provide formative feedback. This paper studies the structure and applica...
Modern Transformers Are Implicit Hybrids: From Functional Differentiation to Principled Hybrid Architecture Design
arXiv:2609.02986v1 Announce Type: new
Abstract: Hybrid architectures combining Full Attention (FA) and Linear Attention (LA) are increasingly prominent, yet their allocation remains heuristic. We seek an evidence-grounded basis in head-level functional organization learned by RoPE-based Transformer...
arXiv:2609.02987v1 Announce Type: new
Abstract: Reinforcement learning typically optimizes average reward. For generative policies, the average can hide an important distinction: two policies can achieve the same mean reward while having very different chances of producing a rare but high-reward ro...
When Can a Machine Trust a Statute? A Survival Certificate for Machine-Extracted Legal Logic
arXiv:2609.01741v1 Announce Type: new
Abstract: Statutes are increasingly parsed by machines before people read them, and the parsers disagree: on Missouri's statutes, two independently written extractors diverge on numeric-threshold presence at a false-negative rate of 0.43. We ask what formal log...
Meta-ethics and AI: exploring the novel meta-ethical questions in the era of AI
arXiv:2609.01685v1 Announce Type: new
Abstract: With the development of artificial intelligence (AI), the landscape of meta-ethics, which has largely centred on human ethics, faces pressures that may significantly reconfigure it. In particular, if future AI systems were to exhibit sufficiently inte...
Efficient Context-Limited Telescope Bibliography Classification for the WASP-2025 Shared Task Using SciBERT
arXiv:2609.01647v1 Announce Type: new
Abstract: The creation of telescope bibliographies is a crucial part of assessing the scientific impact of observatories and ensuring reproducibility in astronomy. This task involves identifying, categorizing, and linking scientific publications that reference ...
A collective capability boundary in frontier large language models on guideline-conformant and case-specific oncology decision-making
arXiv:2608.28592v1 Announce Type: new
Abstract: Large language models (LLMs) achieve high scores on medical knowledge examinations, yet real-world oncology is not a knowledge test--it is a sequence of guideline-pathway choices, escalation judgments, and commitments under uncertainty. Existing bench...
Unsupervised Latent Space Alignment with Hyperspherical Geodesic Matching
arXiv:2608.28840v1 Announce Type: new
Abstract: Independently trained neural networks tend to encode the same data with similar latent geometries. These latent geometries are not directly compatible, yet they can be nearly the same up to some class of transformations. While there exists many method...
arXiv:2608.27515v1 Announce Type: new
Abstract: The recently introduced block-sparse featurizer (BSF; Fel et al., 2026) is similar to a sparse autoencoder (SAE), but its atomic unit is a small subspace (a block of directions) rather than a single direction. It is designed for features that live on ...
Marginal Coverage Credit Reduces Redundant Exploration in Parallel State-Entropy Optimization
arXiv:2608.27507v1 Announce Type: new
Abstract: Policy Gradient for Parallel State Entropy maximization (PGPSE) expands state-space coverage by training independently parameterized policies in replicated copies of the same environment. However, its pooled team-entropy score measures only collective...
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...
Standalone LLM and a Pre-specified Agentic Pipeline for Explaining ICU Mortality Predictions: a Feasibility Study on the eICU Demo Dataset
arXiv:2608.26109v1 Announce Type: new
Abstract: Machine-learning models can predict ICU mortality accurately, but feature-attribution methods alone rarely provide the clinical narrative needed for bedside use. Large language models (LLMs) may bridge this gap, and multi-step agentic pipelines are a ...
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...
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...
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...
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...
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 ...