Think In Cycles
AI-powered causal mapping for people trying to build, understand, and refine models of complex systems.
Neurosymbolic AI research
Machines that dream.
Dwnrt Absrd is building AI systems that learn from their interactions. We're experimenting at the intersection of generative AI and symbolic world models.
Why
From prediction to thinking.
Today's AI systems can write, code, perceive, retrieve, and act. But in complex environments, they often fail to build persistent models of cause and effect.
They can describe relationships without reliably representing them. They can use context without accumulating understanding. They can act without knowing how their theory of the world should change when predictions fail.
We believe the next generation of AI will need more than larger models and longer context windows. It will need systems that construct, test, refine, and use explicit models of the world.
What
Neural intuition. Symbolic structure.
Dwnrt Absrd is a neurosymbolic AI research lab.
Neural models provide perception, language, intuition, and flexible generalization. Symbolic models provide structure, memory, simulation, interpretability, and explicit reasoning.
We're exploring how they can work together to form systems that learn continuously from interaction and evidence.
PerceiveRecognise objects, states, patterns, and changes.
ModelRepresent mechanisms, feedback loops, actions, and consequences.
SimulateExplore possible futures before acting.
PredictMake expectations explicit and testable.
ReviseUpdate internal models when evidence contradicts them.
RememberCarry forward structured understanding across sessions.
Goal
World models that evolve.
Our long-term research goal is the development of causal agents: AI agents that learn, reason, and remember through evolving models of the world.
A causal agent observes an environment, forms hypotheses, predicts outcomes, notices failed predictions, repairs its theory, and uses that evolving understanding to plan better.
Learning is not the accumulation of text. It is the refinement of a world model.
Belief
Action generates information.
We're building.
Some of what we build will be useful. Some of it may just be for fun. The point is to keep learning from contact with reality.
Apps, prototypes, simulations, and playful experiments give us something real to observe: what people try, where systems fail, what surprises us, and what a model needs to learn next.
Apps
Apps along the way.
We are building practical applications that can be useful today while also informing the long term research direction.
SupaSim
AI-generated simulations that help people explore consequences before acting.
Each product creates value on its own. Together, they build the foundations for agents that learn, reason, and remember through models of the world.
ARC-AGI
The proving ground
We use ARC-AGI as a testbed for agents that must infer unfamiliar worlds from interaction.
Can the agent identify objects? Can it discover mechanisms? Can it recognise when its predictions are wrong? Can it repair its theory and improve through experience?
These are the capabilities required for AI systems that build models of the world rather than simply recalling information.
Contact
Build with us.
If you are interested in neurosymbolic AI, causal agents, simulation, systems thinking, or AI that learns how the world works, we'd love to hear from you.