arXiv:2511.03948v1 Announce Type: new Abstract: A longstanding goal in computational educational research is to develop explainable knowledge tracing (KT) models. Deep Knowledge Tracing (DKT), which leverages a Recurrent Neural Network (RNN) to predict student knowledge and performance on exercises, has been proposed as a major advancement over traditional KT methods. Several studies suggest that its […]
Memory- and Latency-Constrained Inference of Large Language Models via Adaptive Split Computing
arXiv:2511.04002v1 Announce Type: cross Abstract: Large language models (LLMs) have achieved near-human performance across diverse reasoning tasks, yet their deployment on resource-constrained Internet-of-Things (IoT) devices remains impractical due to massive parameter footprints and memory-intensive autoregressive decoding. While split computing offers a promising solution by partitioning model execution between edge devices and cloud servers, existing approaches […]
Hierarchical Retrieval with Evidence Curation for Open-Domain Financial Question Answering on Standardized Documents
arXiv:2505.20368v3 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) based large language models (LLMs) are widely used in finance for their excellent performance on knowledge-intensive tasks. However, standardized documents (e.g., SEC filing) share similar formats such as repetitive boilerplate texts, and similar table structures. This similarity forces traditional RAG methods to misidentify near-duplicate text, leading to […]
Pediatric Appendicitis Detection from Ultrasound Images
arXiv:2511.04069v1 Announce Type: cross Abstract: Pediatric appendicitis remains one of the most common causes of acute abdominal pain in children, and its diagnosis continues to challenge clinicians due to overlapping symptoms and variable imaging quality. This study aims to develop and evaluate a deep learning model based on a pretrained ResNet architecture for automated detection […]
LLMs and Cultural Values: the Impact of Prompt Language and Explicit Cultural Framing
arXiv:2511.03980v1 Announce Type: new Abstract: Large Language Models (LLMs) are rapidly being adopted by users across the globe, who interact with them in a diverse range of languages. At the same time, there are well-documented imbalances in the training data and optimisation objectives of this technology, raising doubts as to whether LLMs can represent the […]
An Automated Theorem Generator with Theoretical Foundation Based on Rectangular Standard Contradiction
arXiv:2511.04092v1 Announce Type: cross Abstract: Currently, there is a lack of rigorous theoretical system for systematically generating non-trivial and logically valid theorems. Addressing this critical gap, this paper conducts research to propose a novel automated theorem generation theory and tool. Based on the concept of standard contradiction which possesses unique deductive advantages, this paper defines […]
Balancing Quality and Variation: Spam Filtering Distorts Data Label Distributions
arXiv:2509.08217v2 Announce Type: replace-cross Abstract: For machine learning datasets to accurately represent diverse opinions in a population, they must preserve variation in data labels while filtering out spam or low-quality responses. How can we balance annotator reliability and representation? We empirically evaluate how a range of heuristics for annotator filtering affect the preservation of variation […]
DMSORT: An efficient parallel maritime multi-object tracking architecture for unmanned vessel platforms
arXiv:2511.04128v1 Announce Type: cross Abstract: Accurate perception of the marine environment through robust multi-object tracking (MOT) is essential for ensuring safe vessel navigation and effective maritime surveillance. However, the complicated maritime environment often causes camera motion and subsequent visual degradation, posing significant challenges to MOT. To address this challenge, we propose an efficient Dual-branch Maritime […]
ArchPilot: A Proxy-Guided Multi-Agent Approach for Machine Learning Engineering
arXiv:2511.03985v1 Announce Type: new Abstract: Recent LLM-based agents have demonstrated strong capabilities in automated ML engineering. However, they heavily rely on repeated full training runs to evaluate candidate solutions, resulting in significant computational overhead, limited scalability to large search spaces, and slow iteration cycles. To address these challenges, we introduce ArchPilot, a multi-agent system that […]
Are We Aligned? A Preliminary Investigation of the Alignment of Responsible AI Values between LLMs and Human Judgment
arXiv:2511.04157v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly employed in software engineering tasks such as requirements elicitation, design, and evaluation, raising critical questions regarding their alignment with human judgments on responsible AI values. This study investigates how closely LLMs’ value preferences align with those of two human groups: a US-representative sample and […]