AGWM improves world model accuracy in compositional environments by learning an explicit DAG of action affordance prerequisites to handle dynamic executability.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.AI 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Scientific knowledge is a local optimum shaped by cognitive, formal, and institutional lock-in, potentially missing superior frameworks.
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AGWM: Affordance-Grounded World Models for Environments with Compositional Prerequisites
AGWM improves world model accuracy in compositional environments by learning an explicit DAG of action affordance prerequisites to handle dynamic executability.
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The Non-Optimality of Scientific Knowledge: Path Dependence, Lock-In, and The Local Minimum Trap
Scientific knowledge is a local optimum shaped by cognitive, formal, and institutional lock-in, potentially missing superior frameworks.