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MemPlan is proposed, a memory-conditioned PDDL planning framework for partially observable interactive text environments, which improves task completion and step efficiency over language-only and PDDL-aware baselines, while substantially reducing online token cost compared with LLM-planning baselines such as ReAct.
Evaluating knowledge graph and logic-geometric embedding models on $\mathcal{EL}$ ontologies, with entailed, contradictory, and undetermined test sets generated by a reasoner, it is found that ranking accuracy does not imply logical faithfulness and that none of the evaluated models is faithful across the hierarchy.
Moose improves over propositional-NeSy, fuzzy-logic, and ontology embedding baselines, and presents the first reasoning-shortcut analysis in an OWL EL setting.
Baobab is presented, which is to the authors' knowledge the first to characterize and mitigate reasoning shortcuts in a non-Horn description logic and machine-checked in Lean 4.
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