arXiv:2509.10210v1 Announce Type: new Abstract: Automated characterization of porous materials has the potential to accelerate materials discovery, but it remains limited by the complexity of simulation setup and force field selection. We propose a multi-agent framework in which LLM-based agents…
arXiv:2410.20600v2 Announce Type: replace Abstract: Our interest is in the design of software systems involving a human-expert interacting — using natural language — with a large language model (LLM) on data analysis tasks. For complex problems, it is possible that…
arXiv:2509.10222v1 Announce Type: new Abstract: A common assumption holds that scaling data and parameters yields increasingly structured, generalisable internal representations. We interrogate this assumption in clinical natural language inference (NLI) by adopting a benchmark decomposed into four reasoning families, Causal…
arXiv:2304.11631v2 Announce Type: replace-cross Abstract: Skeleton-based action recognition has achieved remarkable results in human action recognition with the development of graph convolutional networks (GCNs). However, the recent works tend to construct complex learning mechanisms with redundant training and exist a…
arXiv:2509.10249v1 Announce Type: new Abstract: Recent advances in Language Models (LMs) have failed to mask their shortcomings particularly in the domain of reasoning. This limitation impacts several tasks, most notably those involving ontology engineering. As part of a PhD research,…
arXiv:2410.21471v3 Announce Type: replace-cross Abstract: Recent advances in diffusion models have significantly enhanced the quality of image synthesis, yet they have also introduced serious safety concerns, particularly the generation of Not Safe for Work (NSFW) content. Previous research has demonstrated…
arXiv:2509.10297v1 Announce Type: new Abstract: Artificial intelligence (AI) is advancing at a pace that raises urgent questions about how to align machine decision-making with human moral values. This working paper investigates how leading AI systems prioritize moral outcomes and what…
arXiv:2502.16446v2 Announce Type: replace-cross Abstract: In this work, we introduce Auxiliary Discriminator Sequence Generative Adversarial Networks (ADSeqGAN), a novel approach for molecular generation in small-sample datasets. Traditional generative models often struggle with limited training data, particularly in drug discovery, where…
arXiv:2509.10326v1 Announce Type: new Abstract: This paper presents State Algebra, a novel framework designed to represent and manipulate propositional logic using algebraic methods. The framework is structured as a hierarchy of three representations: Set, Coordinate, and Row Decomposition. These representations…
arXiv:2509.10401v1 Announce Type: new Abstract: Failure attribution in multi-agent systems — pinpointing the exact step where a decisive error occurs — is a critical yet unsolved challenge. Current methods treat this as a pattern recognition task over long conversation logs,…
