Efficiently navigating complex environments requires agents to internalize the underlying logic of their world, yet standard world modelling methods often struggle with sample inefficiency, lack of transparency, and poor scalability. We propose a framework for constructing symbolic causal world models entirely online by integrating continuous model learning and repair into the agent's decision loop, by leveraging the power of Meta-Interpretive Learning and predicate invention to find semantically meaningful and reusable abstractions, allowing an agent to construct a hierarchy of disentangled, high-quality concepts from its observations. We demonstrate that our lifted inference approach scales to domains with complex relational dynamics, where propositional methods suffer from combinatorial explosion, while achieving sample-efficiency orders of magnitude higher than the established PPO neural-network-based baseline.
Figures & tables
Figure 1: Visualization of a selected set of rules from the learnt symbolic causal model on the MiniHack ‘Lava Crossing’ task. The environment is modelled as a hierarchy of interpretable concepts, transition rules, and physical constraints. (Left) State Interpretation: Colored overlays illustrate how the Learnt Abstractions (Right) ground to specific regions of the state space. The agent ( p4 , purple) senses its neighborhood ( p3 , cyan). The concept of ”moving” ( p2 , orange) is reused in two rules: the one modelling movement ( p1 ) and the one modelling death ( p5 ). (Center) Dynamics & Constraints: The Learnt Dynamics use these high-level abstractions to predict state evolution. For example, the dying rule is triggered only when the abstract condition p5 (moving into lava) is met. Learnt Constraints enforce physical consistency, such as mutual exclusion ( ⊗ ), ensuring the agent cannot be simultaneously alive and dead or occupy multiple coordinates.
Figure 2: Our system vs PPO on the 10 × 10 grid version of the MiniHack ‘Lava Crossing’ task. (a) Our system converges to 43 clauses (28 abstractions, 13 dynamics, 2 constraints) by step 23. (b) Our system reaches the goal at episode 2 since then it is able to consistently navigate the environment to it relying on it learn model; PPO requires 129 episodes for first success and it is not until episode 300 that it starts to converge.
Appendix figures & tables1 asset
Supplementary material from the paper’s appendix.
Appendix
Figure 3: Lattice traversal dynamics. Prediction errors trigger Generalization (moving to ⊤ ) or Specialization (pruning to H ).
In model-based reinforcement learning, world models exist as internal simulators, but their training often conflates statistical correlations with causal mechanisms. This problem is exacerbated in multi-agent systems where physical transitions are intertwined with strategic agent intents, causing world models to fail under distribution shift. We introduce Implicit Causal World Models to recover environmental dynamics from offline demonstrations without requiring pre-defined causal graphs. By incorporating policy variance, we render world models discoverable via the sequential backdoor condition. Evaluations across coordination tasks (Two-Door, Navigation, and Giveway) demonstrate that these models provide interpretable causal representations under both full and partial observability, with model accuracy scaling directly with interventional strength.
Jasorsi Ghosh
Department of Computer Science Purdue University West Lafayette, Indiana, USA
The central challenge of world modeling is to learn representations that capture how the world evolves. However, existing world models predominantly represent future states without explicitly capturing the latent causes underlying their evolution, limiting their ability to reason about why and how the world changes. To address this limitation, we propose Abductive World Modeling (AWM), a framework that learns structured causal representations by abductively inferring latent causes from predicted futures. Specifically, we realize AWM through the Hierarchical Abductive State Pyramid (HASP), which organizes the inferred world state into three complementary components - Entity, Dynamic, and Relation - capturing what exists, how it changes, and how entities interact, respectively. By jointly reasoning over the current observation and its predicted future, HASP abductively infers these latent factors and integrates them into a structured state representation for downstream reasoning. To the best of our knowledge, AWM is the first framework to introduce abductive state inference into latent-space world modeling for learning structured representations of world dynamics. Experiments across physical prediction, causal reasoning, and action understanding demonstrate the effectiveness of our approach. Compared with V-JEPA, a state-of-the-art latent-space world model, AWM improves physical prediction AUROC by 10.7%, causal reasoning accuracy by 16.8%, and action Top-1 accuracy by 68.0%.
World models aim to learn compact representations of an environment from observations, enabling agents to predict future states and plan their behaviour. However, most existing approaches learn black-box representations, with limited ability to incorporate prior knowledge about the world. We introduce Concept-Grounded World Models (CGWMs), a neurosymbolic world model design that instead learns interpretable concept representations. We learn these concepts from observations using explicit and complete knowledge, expressed in temporal logic, that specifies the valid world states and the temporal relations governing their dynamics. Furthermore, we directly use this knowledge by formulating planning as differentiable reasoning, optimising the agent's actions by gradient ascent to satisfy a goal. We experimentally compare CGWM with a purely neural baseline and a neurosymbolic baseline on two game environments. We find that CGWMs ground concepts much more accurately, even over long horizons, resulting in significantly more robust planning both in and out of distribution.