Out now in TMLR! 🚀 We formalize how epistemic priors transform Expected Free Energy minimization into a standard variational objective. This allows us to frame planning as a continuous variational optimization problem, moving away from combinatorial tree search. 👇

This is what we ask ourselves in our latest paper (arxiv.org/abs/2508.02197), where we solve Minigrid environments with Active Inference. (Spoiler, it works!) Code available at github.com/biaslab/EFEasVFE Done together with @mlmykola , @ThijsvdLaar and @bertdv0

What's even nicer: because our method injects priors locally, everything still works within @ReactiveBayes ' RxInfer.jl using message passing. Special thanks to my colleagues at @LazyDynamics for making this possible!

In Active Inference, a lot of time is spent on computing Expected Free Energy. What if we could tweak the generative model such that EFE can be minimised with traditional variational inference methods?

Backed Trojan Robotics (Team 24090) at the FIRST® Tech Challenge European Premier Event in Eindhoven (July 1–5)  . They hustled—coding, building, troubleshooting—and came away with 3rd in the Think Award. Proud to support their next steps. 🚀 #FTC #Robotics #STEM

That is 🤯