The Archive

While federated learning enables collaborative modelling on decentralised data, standard methods merely fit historical observations. This purely observational approach is fundamentally insufficient for interventional inference and policy evaluation, as sequential actions dynamically alter future states. We propose \textbf{Fed-CausalDiff}, a federated causal diffusion framework for do-simulation. The architecture decomposes the evolution of the latent state into a global causal score function and a local confounding score function. This design enables \emph{decoupled synchronisation} (DSS), wh...

Soil microorganisms control organic matter cycling and largely determine how soil systems can cope with and mitigate climate change and environmental threats. Representing microbial dynamics in process-based soil models is therefore critical to predict carbon cycling in soils, albeit highly challenging to inform from data. One promising approach to improve their parametrisation is the integration of genomic data, yet modelling the complex and unknown relationship between genomes and the processes the microbes are driving is an unsolved problem. In this work, we present the first hybrid modeli...

Current world models face a fundamental tension: faithful long-horizon simulation demands deep computation, but deeper models are expensive to deploy and prone to compounding errors. We resolve this by introducing Looped World Models (LoopWM), which are the first looped architectures for world modelling. Our method iteratively refines latent environment states through a parameter-shared transformer block. This yield up to 100x parameter efficiency over conventional approaches with adaptive computation that automatically scales depth to match the complexity of each prediction step. Orthogonal ...

Alzheimer's disease (AD) progression is highly heterogeneous and is typically observed through sparse and irregular longitudinal data, posing challenges for prediction and personalised monitoring. Existing machine learning approaches have improved AD prediction using multimodal data, yet often focus on static classification or cohort-level risk estimation, providing limited support for subject-specific modelling and uncertainty-aware reasoning. To address these limitations, we present a personalised digital twin framework for AD prediction and scenario-based analysis using multimodal longitud...

Data-driven causal relationship identification is pertinent to advancing understanding of complex systems both within and beyond science. Bayesian networks offer a probabilistic method for modelling generic causal relationships via directed acyclic graphs (DAGs). However, typical techniques for constructing Bayesian networks rely on optimization, which can be ill-suited for learning causal relationships because the underlying data may admit multiple chains of causation. More data-faithful representations of causal relationships would provide frameworks for constructing multiple causal maps th...

Modelling individual decision-making during infectious disease outbreaks is crucial for understanding behavioural dynamics and informing effective public health interventions. Prior work has shown that large language models can simulate realistic human behaviour by generating agent decisions based on demographic prompts and situational context. We build on this foundation with a spatially grounded, agent-based simulation framework that integrates LLM-generated decisions about self-reported influenza-like illness into a census-based synthetic population of agents. Location is treated as a cent...

An interview with Microsoft CEO Satya Nadella about figuring out Microsoft's role in AI, the relationship with OpenAI, Capex, Software, and a potential new agentic platform.

We introduce the Graph Set Transformer (GST), a neural network architecture for learning on sets of graphs, designed for tasks in which per-element predictions depend on set-wide context as well as local structure. Existing architectures, including DeepSets and SetTransformer, require pre-encoded graph embeddings from a separate GNN, creating a bottleneck between feature extraction and set-level contextualisation. In contrast, GST interleaves node-level feature propagation and cross-graph contextual modelling at every layer, fusing the two levels of information through a gating mechanism. We ...

The legendary Microsoft CEO makes his first Latent Space appearance!

Microsoft just kicked off Build 2026 with a keynote from CEO Satya Nadella and other company leaders. As expected, it was filled with announcements, ranging from new Surface hardware to an always-on personal assistant and updates across Microsoft's in-house AI models. If you didn't watch the event live, you can catch up on all the latest news in the roundup below. A mini Surface PC designed for AI development The Surface RTX Spark Dev Box is geared toward developers who want to run local AI models on their device, serving as a substitute for Qualcomm's canceled dev kit. It comes equipped with...

We introduce an explainable machine-learning approach that forecasts the structural precursors of scientific breakthroughs -- the emergence and intensification of links between research concepts -- by modelling how OpenAlex concept networks evolve over time. Using 59 semantic and topological features, a two-stage LightGBM model jointly predicts the formation and the future weight of concept pairs, adding a regression stage that quantifies expected intensity to prior link-existence forecasts. Relative to the state of the art, the approach improves accuracy and explainability at once: comparati...

In-silico trials of medical devices require the generation of virtual populations of anatomies. In cardiovascular applications, virtual anatomy is typically represented as a 3D+t mesh sampled from a generative model. However, most existing mesh generators focus on static anatomy, while sequence models often lack explicit periodicity. To this end, we propose 4D F-MeshLDM, a conditional generative framework comprising a convolutional mesh VAE to encode meshes, a structural latent space that parameterises motion using a truncated Fourier series, and a diffusion prior that learns the latent distr...

If Nvidia has cracked a way to bring AI agents easily, safely and usefully to the masses, it could — and should — be big.

Dynamic graph learning (DGL) is essential for modelling evolving graph data, but existing methods suffer from significant computational overhead due to repeated full-snapshot retraining and are not well-suited for collaborative settings with partitioned data. In realistic graph systems, cross-partition edges are unavoidable, but direct sharing of graph structure between clients may violate privacy constraints. We propose DG-CoLearn, a client-oblivious collaborative dynamic graph learning framework built on incremental graph snapshot processing, which focuses computation on graph regions affec...

OpenAI and Dell partner to deploy Codex in hybrid/on-premise environments for enterprise coding automation with security controls.

Python package for reduced-order modelling of multi-physics nuclear engineering problems using data-driven techniques.

Empirical evaluation compares domain-adapted and general-purpose LLMs/SLMs for structured threat modeling in cybersecurity.

I want to share an interview experience anonymously in case it helps others on the job market. I was approached about a Vancouver ML role that was presented to me as research-oriented. The recruiter told me the team had looked at my research and that I should be ready to discuss my projects, so I expected a conversation about modelling, research ideas, and fit. That is not how the interview felt. It was much more focused on trivia-style and coding-style questioning, with very little real engagement with my research or how I think about problems. The overall process felt much narrower and mo...

OpenAI CEO Sam Altman and Microsoft CTO Kevin Scott. | Image: Getty Images When OpenAI was busy experimenting with AI-powered gaming bots, Microsoft CEO Satya Nadella and OpenAI CEO Sam Altman were in the early days of forming an AI partnership. Court documents from the ongoing Musk v. Altman trial have provided a rare look at the communications between Microsoft's top executives about investing in OpenAI and fears the AI startup could "storm off to Amazon" and "shit-talk" Microsoft. Just days after OpenAI showed a bot beating a Dota 2 professional in the summer of 2017, Altman responded to N...

Fully convolutional neural network for chemical-mechanical polishing modeling in IC manufacturing using white light interferometry.

Microsoft gets to offer OpenAI's tech to its cloud customers and doesn't have to pay for it. "We fully plan to exploit it," Nadella said.

Chris Liddell appointed to Anthropic's board of directors.