01
World Model
We learn structured, predictive models of an environment — given a state and an action, what happens next. Neural perception is grounded in a symbolic state you can inspect and reason over.
State · Transition · Prediction
An applied AI research lab
World models
that imagine.
Trygentic Labs builds neurosymbolic world models — systems that learn the structure of an environment and use an imagination engine to simulate the consequences of their actions before they act.
A learned world model, a symbolic reasoning core, and an imagination engine — operating as one.
§ 01 — Beyond Prediction
State-of-the-art models predict the next token; they do not hold a model of the world they act in, nor imagine the consequences of an action before committing to it. To reason and plan over long horizons, prediction is not enough — a system must be able to imagine. We believe the most important open problem in applied AI is combining neural perception with symbolic structure inside a learned world model. Several fundamental advances are required to achieve this goal.
§ 02 — Our Approach
A neurosymbolic imagination loop.
Three processes — a world model, an imagination engine, and a symbolic reasoning core — sit between perception and action. Each is independently auditable and individually replaceable.
02
Imagination Engine
Before acting, the system forks its own state and rolls forward counterfactual futures — mentally simulating outcomes without touching the real world. Plans are chosen from what it imagines, not only from what it has seen.
Counterfactual · Rollout · Foresight
03
Symbolic Reasoning
Hierarchical goals, behavior trees, and a logical constraint engine hold imagined plans to what is valid and achievable. Structure makes the model's reasoning auditable — and its decisions explainable.
Goals · Constraints · Behavior trees
A.1 — Architecture overview available in our technical brief.Read the technical brief →
Product
Introducing AgentLoop
AgentLoop equips agents with the ability to iteratively decompose, investigate, and dynamically solve problems — grounding both language models and real-time neural networks, such as vision models, in a neurosymbolic runtime that can imagine consequences before it acts.
Built on Trygentic's research stack, AgentLoop brings learned world models, symbolic control, and verifiable execution to multi-agent systems that deliver state-of-the-art reliability on top of any model.