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A structural correspondence between signal-flow circuits for dynamical systems and sequential programs — where finding a closed-form solution corresponds to eliminating loops. We show trace elimination is undecidable in general, formalising the intuition that most ODEs don't have closed-form solutions.

An agent-based approach that extends the categorical proof system with simulation-guided reasoning. The agent interleaves strict symbolic rewrites with simulation-informed approximate rewrites to discover approximate closed-form solutions for systems where exact solutions do not exist.

A visual overview of the Gimle platform — why explicit mathematical structure matters for learning dynamical systems, how Asgard compiles equations into typed circuits, and the path from formal methods to practical simulation.

Most agent frameworks treat LLMs as agents. Hugin treats them as oracles — one component in a larger reasoning system. Built around an immutable stack architecture that makes branching, debugging, and multi-agent coordination natural.

Why does next-token prediction learn language so well, yet fail for dynamical systems? We propose an explanation rooted in the smoothness of the syntax-to-semantics map — and what it means for building foundation models in structure-sensitive domains.

A framework that compiles mathematical equations into typed compositional circuits and executes them via JAX. Unlike implicit approaches that learn black-box dynamics, Asgard maintains explicit mathematical structure throughout — enabling formal composition, algebraic rewriting, and interchangeable semantics.

This is part four in a series on how to use a state machine framework to model agentic flows and how this approach enables some interesting features. In this part we explore advanced techniques for improving multi-agent reasoning.

In the last two parts we described the state machine framework, how it works and how we can steer agents within it. In this third part, we will describe several other desirable extensions of any agent framework and how we can implement them with our state machine setup.

In the first part we described the state machine framework and how it works. In this part we will dig into some of the techniques for steering agents and how we implement them in the state machine framework.

Since I last wrote a few thoughts down on agents the buzz has only increased. With our release of Bigwig I thought it would also be interesting to dig into some of the details of how we have gone about implementing our system of multi-agents.

Recently Yoav Goldberg asked a series of interesting question on Bluesky(1, 2, 3).

I think this is one of the more interesting and peculiar operators in matrix algebra, so I wanted to write a little bit about it.

I have recently been thinking about modelling of dynamical systems again - so wanted to revisit some of my old ideas.

At the end of October, I gave a quick demo at AI Tinkerers Paris about one of our early, early version of BigWig. This is a short writeup based on the slides, called "Agents for building ML models".