arXiv:2603.29023v1 Announce Type: cross Abstract: Large language models lack persistent, structured memory for long-term interaction and context-sensitive retrieval. Expanding context windows does not solve this: recent evidence shows that context length alone degrades reasoning by up to 85% – even with perfect retrieval. We propose a bio-inspired memory framework grounded in complementary learning systems theory, […]
Mixed updating in structured populations
arXiv:2512.11164v2 Announce Type: replace Abstract: Evolutionary graph theory (EGT) studies the effect of population structure on evolutionary dynamics. The vertices of the graph represent the $N$ individuals. The edges denote interactions for competitive replacement. Two standard update rules are death-Birth (dB) and Birth-death (Bd). Under dB, an individual is chosen uniformly at random to die, […]
A Latent Risk-Aware Machine Learning Approach for Predicting Operational Success in Clinical Trials based on TrialsBank
arXiv:2603.29041v1 Announce Type: cross Abstract: Clinical trials are characterized by high costs, extended timelines, and substantial operational risk, yet reliable prospective methods for predicting trial success before initiation remain limited. Existing artificial intelligence approaches often focus on isolated metrics or specific development stages and frequently rely on variables unavailable at the trial design phase, limiting […]
Webscraper: Leverage Multimodal Large Language Models for Index-Content Web Scraping
arXiv:2603.29161v1 Announce Type: new Abstract: Modern web scraping struggles with dynamic, interactive websites that require more than static HTML parsing. Current methods are often brittle and require manual customization for each site. To address this, we introduce Webscraper, a framework designed to handle the challenges of modern, dynamic web applications. It leverages a Multimodal Large […]
WorldFlow3D: Flowing Through 3D Distributions for Unbounded World Generation
arXiv:2603.29089v1 Announce Type: cross Abstract: Unbounded 3D world generation is emerging as a foundational task for scene modeling in computer vision, graphics, and robotics. In this work, we present WorldFlow3D, a novel method capable of generating unbounded 3D worlds. Building upon a foundational property of flow matching – namely, defining a path of transport between […]
A Multi-Agent Rhizomatic Pipeline for Non-Linear Literature Analysis
arXiv:2603.28336v2 Announce Type: replace Abstract: Systematic literature reviews in the social sciences overwhelmingly follow arborescent logics — hierarchical keyword filtering, linear screening, and taxonomic classification — that suppress the lateral connections, ruptures, and emergent patterns characteristic of complex research landscapes. This research note presents the Rhizomatic Research Agent (V3), a multi-agent computational pipeline grounded in […]
Towards Explainable Stakeholder-Aware Requirements Prioritisation in Aged-Care Digital Health
arXiv:2603.29114v1 Announce Type: cross Abstract: Requirements engineering for aged-care digital health must account for human aspects, because requirement priorities are shaped not only by technical functionality but also by stakeholders’ health conditions, socioeconomics, and lived experience. Knowing which human aspects matter most, and for whom, is critical for inclusive and evidence-based requirements prioritisation. Yet in […]
Predicting Neuromodulation Outcome for Parkinson’s Disease with Generative Virtual Brain Model
arXiv:2603.29176v1 Announce Type: new Abstract: Parkinson’s disease (PD) affects over ten million people worldwide. Although temporal interference (TI) and deep brain stimulation (DBS) are promising therapies, inter-individual variability limits empirical treatment selection, increasing non-negligible surgical risk and cost. Previous explorations either resort to limited statistical biomarkers that are insufficient to characterize variability, or employ AI-driven […]
Efficient and Scalable Granular-ball Graph Coarsening Method for Large-scale Graph Node Classification
arXiv:2603.29148v1 Announce Type: cross Abstract: Graph Convolutional Network (GCN) is a model that can effectively handle graph data tasks and has been successfully applied. However, for large-scale graph datasets, GCN still faces the challenge of high computational overhead, especially when the number of convolutional layers in the graph is large. Currently, there are many advanced […]
3D Architect: An Automated Approach to Three-Dimensional Modeling
arXiv:2603.29191v1 Announce Type: cross Abstract: The aim of our paper is to render an object in 3-dimension using a set of its orthographic views. Corner detector (Harris Detector) is applied on the input views to obtain control points. These control points are projected perpendicular to respective views, in order to construct an envelope. A set […]
AEC-Bench: A Multimodal Benchmark for Agentic Systems in Architecture, Engineering, and Construction
arXiv:2603.29199v1 Announce Type: new Abstract: The AEC-Bench is a multimodal benchmark for evaluating agentic systems on real-world tasks in the Architecture, Engineering, and Construction (AEC) domain. The benchmark covers tasks requiring drawing understanding, cross-sheet reasoning, and construction project-level coordination. This report describes the benchmark motivation, dataset taxonomy, evaluation protocol, and baseline results across several domain-specific […]
Software Vulnerability Detection Using a Lightweight Graph Neural Network
arXiv:2603.29216v1 Announce Type: cross Abstract: Large Language Models (LLMs) have emerged as a popular choice in vulnerability detection studies given their foundational capabilities, open source availability, and variety of models, but have limited scalability due to extensive compute requirements. Using the natural graph relational structure of code, we show that our proposed graph neural network […]