arXiv:2604.25689v1 Announce Type: cross Abstract: This paper investigates how GPT-based tools can assist in building reusable analytical spreadsheet models. After a screening, we evaluate five GPT extensions and select Excel AI by pulsrai.com for detailed testing. Through structured experiments on simple problem statements, we assess Excel AI’s performance against the ERFR criteria (each input in […]
CF-VLA: Efficient Coarse-to-Fine Action Generation for Vision-Language-Action Policies
arXiv:2604.24622v2 Announce Type: replace-cross Abstract: Flow-based vision-language-action (VLA) policies offer strong expressivity for action generation, but suffer from a fundamental inefficiency: multi-step inference is required to recover action structure from uninformative Gaussian noise, leading to a poor efficiency-quality trade-off under real-time constraints. We address this issue by rethinking the role of the starting point in […]
Use of What-if Scenarios to Help Explain Artificial Intelligence Models for Neonatal Health
arXiv:2410.09635v2 Announce Type: replace-cross Abstract: Early detection of intrapartum risks enables timely interventions to prevent or mitigate adverse labor outcomes such as cerebral palsy. However, accurate automated systems to support clinical decision-making during delivery are currently lacking. To address this gap, we propose Artificial Intelligence for Modeling and Explaining Neonatal Health (AIMEN), a deep learning […]
Prefill-Time Intervention for Mitigating Hallucination in Large Vision-Language Models
arXiv:2604.25642v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have achieved remarkable progress in visual-textual understanding, yet their reliability is critically undermined by hallucinations, i.e., the generation of factually incorrect or inconsistent responses. While recent studies using steering vectors demonstrated promise in reducing hallucinations, a notable challenge remains: they inadvertently amplify the severity of residual […]
From Ambiguity to Accuracy: The Transformative Effect of Coreference Resolution on Retrieval-Augmented Generation systems
arXiv:2507.07847v3 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has emerged as a crucial framework in natural language processing (NLP), improving factual consistency and reducing hallucinations by integrating external document retrieval with large language models (LLMs). However, the effectiveness of RAG is often hindered by coreferential complexity in retrieved documents, introducing ambiguity that disrupts in-context learning. […]
OptProver: Bridging Olympiad and Optimization through Continual Training in Formal Theorem Proving
arXiv:2604.23712v2 Announce Type: replace-cross Abstract: Recent advances in formal theorem proving have focused on Olympiad-level mathematics, leaving undergraduate domains largely unexplored. Optimization, fundamental to machine learning, operations research, and scientific computing, remains underserved by existing provers. Its reliance on domain-specific formalisms (convexity, optimality conditions, and algorithmic analysis) creates significant distribution shift, making naive domain transfer […]
Physics-Guided Tiny-Mamba Transformer for Reliability-Aware Early Fault Warning
arXiv:2601.21293v2 Announce Type: replace-cross Abstract: Reliability-centered prognostics for rotating machinery requires early-warning signals that remain accurate under nonstationary operating conditions, domain shifts across speed, load, sensors, and machines, and severe class imbalance, while keeping false-alarm rates small and predictable. We propose the Physics-Guided Tiny-Mamba Transformer (PG-TMT), a compact tri-branch encoder tailored for online condition monitoring. […]
Large language models eroding science understanding: an experimental study
arXiv:2604.25639v1 Announce Type: cross Abstract: This paper is under review in AI and Ethics This study examines whether large language models (LLMs) can reliably answer scientific questions and demonstrates how easily they can be influenced by fringe scientific material. The authors modified custom LLMs to prioritise knowledge in selected fringe papers on the Fine Structure […]
Responsible Evaluation of AI for Mental Health
arXiv:2602.00065v2 Announce Type: replace-cross Abstract: Although artificial intelligence (AI) shows growing promise for mental health care, current approaches to evaluating AI tools in this domain remain fragmented and poorly aligned with clinical practice, social context, and first-hand user experience. This paper argues for a rethinking of responsible evaluation — what is measured, by whom, and […]
A Milestone in Formalization: The Sphere Packing Problem in Dimension 8
arXiv:2604.23468v2 Announce Type: replace-cross Abstract: In 2016, Viazovska famously solved the sphere packing problem in dimension $8$, using modular forms to construct a ‘magic’ function satisfying optimality conditions determined by Cohn and Elkies in 2003. In March 2024, Hariharan and Viazovska launched a project to formalize this solution and related mathematical facts in the Lean […]
JumpLoRA: Sparse Adapters for Continual Learning in Large Language Models
arXiv:2604.16171v3 Announce Type: replace-cross Abstract: Adapter-based methods have become a cost-effective approach to continual learning (CL) for Large Language Models (LLMs), by sequentially learning a low-rank update matrix for each task. To mitigate catastrophic forgetting, state-of-the-art approaches impose constraints on new adapters with respect to the previous ones, by targeting either subspace or coordinate-wise interference. […]
Health System Scale Semantic Search Across Unstructured Clinical Notes
arXiv:2604.25605v1 Announce Type: cross Abstract: Introduction: Semantic search, which retrieves documents based on conceptual similarity rather than keyword matching, offers substantial advantages for retrieval of clinical information. However, deploying semantic search across entire health systems, comprising hundreds of millions of clinical notes, presents formidable engineering, cost, and governance challenges that have prevented adoption. Methods: We […]