A Semantic Proof of Generalised Cut Elimination for Deep Inference
<|reference_start|>A Semantic Proof of Generalised Cut Elimination for Deep Inference: Multiplicative-Additive System Virtual (MAV) is a logic that extends Multiplicative-Additive Linear Logic with a self-dual non-commutative operator expressing the concept of "before" or "sequencing". MAV is also an extenson of the the logic Basic System Virtual (BV) with additives. Formulas in BV have an appealing reading as processes with parallel and sequential composition. MAV adds internal and external choice operators. BV and MAV are also closely related to Concurrent sKleene Algebras.   Proof systems for MAV and BV are Deep Inference systems, which allow inference rules to be applied anywhere inside a structure. As with any proof system, a key question is whether proofs in MAV can be reduced to a normal form, removing detours and the introduction of structures not present in the original goal. In Sequent Calcluli systems, this property is referred to as Cut Elimination. Deep Inference systems have an analogous Cut rule and other rules that are not present in normalised proofs. Cut Elimination for Deep Inference systems has the same metatheoretic benefits as for Sequent Calculi systems, including consistency and decidability.   Proofs of Cut Elimination for BV, MAV, and other Deep Inference systems present in the literature have relied on intrincate syntactic reasoning and complex termination measures.   We present a concise semantic proof that all MAV proofs can be reduced to a normal form avoiding the Cut rule and other "non analytic" rules. We also develop soundness and completeness proofs of MAV (and BV) with respect to a class of models. We have mechanised all our proofs in the Agda proof assistant, which provides both assurance of their correctness as well as yielding an executable normalisation procedure.<|reference_end|>
@article{atkey2024a,
    title={A Semantic Proof of Generalised Cut Elimination for Deep Inference},
    author={Robert Atkey, Wen Kokke},
    journal={arXiv preprint arXiv:2404.06233},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.06233},
    primaryClass={cs.LO}
}