In Automated Planning, learning and exploiting structural patterns of plans, domain models and/or problem models, in order to improve plan generation speed-up and increase the scope of problems solved, has attracted much research. Reformulation techniques such as those based on macro-operators or entanglements are very promising, mainly because they are planner-independent. This paper aims to extend and revisit the recent work on inner entanglements, relations between pairs of planning operators and predicates encapsulating exclusivity of predicate ‘achievements’ or ‘requirements’, in order to bring new theoretical results (PSPACE-completeness of deciding inner entanglements), present a new way of encoding of inner entanglements and empirical comparison between different kinds of inner entanglements.