arXiv:2509.24655v2 Announce Type: replace-cross Abstract: Language models are increasingly applied to biological sequences like proteins and mRNA, yet their default Euclidean geometry may mismatch the hierarchical structures inherent to biological data. While hyperbolic geometry provides a better alternative for accommodating hierarchical data, it has yet to find a way into language modeling for mRNA sequences. […]
Automata-Conditioned Cooperative Multi-Agent Reinforcement Learning
arXiv:2511.02304v1 Announce Type: cross Abstract: We study the problem of learning multi-task, multi-agent policies for cooperative, temporal objectives, under centralized training, decentralized execution. In this setting, using automata to represent tasks enables the decomposition of complex tasks into simpler sub-tasks that can be assigned to agents. However, existing approaches remain sample-inefficient and are limited to […]
Human-AI Co-Embodied Intelligence for Scientific Experimentation and Manufacturing
arXiv:2511.02071v1 Announce Type: new Abstract: Scientific experiment and manufacture rely on complex, multi-step procedures that demand continuous human expertise for precise execution and decision-making. Despite advances in machine learning and automation, conventional models remain confined to virtual domains, while real-world experiment and manufacture still rely on human supervision and expertise. This gap between machine intelligence […]
EvoDev: An Iterative Feature-Driven Framework for End-to-End Software Development with LLM-based Agents
arXiv:2511.02399v1 Announce Type: cross Abstract: Recent advances in large language model agents offer the promise of automating end-to-end software development from natural language requirements. However, existing approaches largely adopt linear, waterfall-style pipelines, which oversimplify the iterative nature of real-world development and struggle with complex, large-scale projects. To address these limitations, we propose EvoDev, an iterative […]
Charting the European LLM Benchmarking Landscape: A New Taxonomy and a Set of Best Practices
arXiv:2510.24450v2 Announce Type: replace-cross Abstract: While new benchmarks for large language models (LLMs) are being developed continuously to catch up with the growing capabilities of new models and AI in general, using and evaluating LLMs in non-English languages remains a little-charted landscape. We give a concise overview of recent developments in LLM benchmarking, and then […]
An End-to-End Learning Approach for Solving Capacitated Location-Routing Problems
arXiv:2511.02525v1 Announce Type: cross Abstract: The capacitated location-routing problems (CLRPs) are classical problems in combinatorial optimization, which require simultaneously making location and routing decisions. In CLRPs, the complex constraints and the intricate relationships between various decisions make the problem challenging to solve. With the emergence of deep reinforcement learning (DRL), it has been extensively applied […]
In silico trials of acute ischemic stroke: predicting the total potential for improvement to patient functional outcomes
arXiv:2511.02088v1 Announce Type: new Abstract: This study uses in silico trials (ISTs) to quantify the potential for benefit due to improved recanalisation outcomes and shorter time to treatment for acute ischaemic stroke (AIS) patients. We use an IST framework to run trials on cohorts of virtual patients with early and late treatment after stroke onset, […]
On The Dangers of Poisoned LLMs In Security Automation
arXiv:2511.02600v1 Announce Type: cross Abstract: This paper investigates some of the risks introduced by “LLM poisoning,” the intentional or unintentional introduction of malicious or biased data during model training. We demonstrate how a seemingly improved LLM, fine-tuned on a limited dataset, can introduce significant bias, to the extent that a simple LLM-based alert investigator is […]
STAR-VAE: Latent Variable Transformers for Scalable and Controllable Molecular Generation
arXiv:2511.02769v1 Announce Type: cross Abstract: The chemical space of drug-like molecules is vast, motivating the development of generative models that must learn broad chemical distributions, enable conditional generation by capturing structure-property representations, and provide fast molecular generation. Meeting the objectives depends on modeling choices, including the probabilistic modeling approach, the conditional generative formulation, the architecture, […]
Interpretable Heart Disease Prediction via a Weighted Ensemble Model: A Large-Scale Study with SHAP and Surrogate Decision Trees
arXiv:2511.01947v1 Announce Type: cross Abstract: Cardiovascular disease (CVD) remains a critical global health concern, demanding reliable and interpretable predictive models for early risk assessment. This study presents a large-scale analysis using the Heart Disease Health Indicators Dataset, developing a strategically weighted ensemble model that combines tree-based methods (LightGBM, XGBoost) with a Convolutional Neural Network (CNN) […]