As systems evolve, analysis results based on models of the system must be updated, in many cases as fast as possible. Since usually only small parts of the model change, large parts of the analysis' intermediate results could be reused in an incremental fashion. Manually invalidating these intermediate results at the right places in the analysis is a non-trivial and error-prone task that conceals the codes intention. A possible solution for this problem is implicit incrementality, i.e., an

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... mental algorithm is derived from the batch specification, aiming for an increased performance without the cost of degraded maintainability. Current approaches are either specialized to a subset of analyses or require explicit state management. In this paper, we propose an approach to implicit incremental model analysis capable of integrating custom dynamic algorithms. For this, we formalize incremental derivation using category theory, gaining type-safety and correctness properties. We implement an extensible implicit incremental computation system and validate its applicability by integrating incremental queries. We evaluate the performance using a micro-benchmark and a community benchmark where the integration of explicit query incrementalization was multiple orders of magnitude faster than rerunning the analysis after every change.