aiqbench analysis
This arXiv preprint proposes Concept-Grounded World Models, which learn interpretable concept representations guided by temporal logic and plan by gradient ascent through differentiable reasoning. The evaluation is limited to two game environments against one neural and one neurosymbolic baseline, so the reported gains in concept grounding and out-of-distribution robustness are preliminary and not yet shown to transfer to real-world settings. For readers, the main takeaway is the attempt to make world models inspectable and steerable with explicit domain knowledge rather than purely black-box learning.
Neurosymbolic Learning of Interpretable World Models
arXiv:2610.19955v1 Announce Type: new Abstract: 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.
Abstract: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.
Artificial Intelligence (cs.AI)
arXiv:2610.19955 [cs.AI]
(or arXiv:2610.19955v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.19955 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 243 words
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