arXiv:2509.12282v1 Announce Type: new Abstract: Advances in AI-assisted research have introduced powerful tools for literature retrieval, hypothesis generation, experimentation, and manuscript preparation. However, systems remain fragmented and lack human-centred workflows. To address these gaps, we introduce AIssistant, an agentic, open-source…
arXiv:2509.12423v1 Announce Type: new Abstract: Understanding user intents from UI interaction trajectories remains a challenging, yet crucial, frontier in intelligent agent development. While massive, datacenter-based, multi-modal large language models (MLLMs) possess greater capacity to handle the complexities of such sequences,…
arXiv:2509.12274v1 Announce Type: new Abstract: Controlling environmental conditions and monitoring plant status in greenhouses is critical to promptly making appropriate management decisions aimed at promoting crop production. The primary objective of this research study was to develop and test a…
arXiv:2509.12273v1 Announce Type: new Abstract: The rise of large language models (LLMs) has made natural language-driven route planning an emerging research area that encompasses rich user objectives. Current research exhibits two distinct approaches: direct route planning using LLM-as-Agent and graph-based…
arXiv:2509.12263v1 Announce Type: new Abstract: Large multimodal models (LMMs) encode universal physical laws observed during training, such as momentum conservation, as parametric knowledge. It allows LMMs to answer physical reasoning queries, such as the outcome of a potential collision event…
arXiv:2509.09004v3 Announce Type: replace-cross Abstract: Automatic quantification of intramyocardial motion and strain from tagging MRI remains an important but challenging task. We propose a method using implicit neural representations (INRs), conditioned on learned latent codes, to predict continuous left ventricular…
arXiv:2509.12437v1 Announce Type: new Abstract: A major challenge in deploying world models is the trade-off between size and performance. Large world models can capture rich physical dynamics but require massive computing resources, making them impractical for edge devices. Small world…
arXiv:2509.12923v1 Announce Type: cross Abstract: Interpreting the massive volume of security alerts is a significant challenge in Security Operations Centres (SOCs). Effective contextualisation is important, enabling quick distinction between genuine threats and benign activity to prioritise what needs further analysis.This…
arXiv:2509.12464v1 Announce Type: new Abstract: Reasoning language models such as DeepSeek-R1 produce long chain-of-thought traces during inference time which make them costly to deploy at scale. We show that using compression techniques such as neural network pruning produces greater performance…
arXiv:2509.13077v1 Announce Type: cross Abstract: Although robotic manipulators are used in an ever-growing range of applications, robot manufacturers typically follow a “one-fits-all” philosophy, employing identical manipulators in various settings. This often leads to suboptimal performance, as general-purpose designs fail to…
