Ilana Shapiro | Searcharxiv

arXiv2026

Parallel SMT Solving via Dynamic Partitioning, Core-Guided Pruning, and Backbone Detection

Exploiting parallelism in modern CPU architectures remains a longstanding challenge in optimizing SMT solvers. We introduce a novel framework for parallel SMT solving that uses feedback from active search to steer solving. We dynamically build a binary partition tree of the search space by sampling from workers' VSIDS statistics during solving. We introduce a novel search-space pruning mechanism that harnesses the full power of core-based CDCL-style pruning to continuously shrink the partition tree. We further optimize our architecture by incorporating online backbone detection into worker threads, as well as a terminate-on-demand mechanism to eagerly eliminate work on pruned subproblems. The resulting algorithm is highly generalizable and scales effectively with available resources. We implement our approach in the Z3 SMT solver and demonstrate that it outperforms both sequential Z3 and existing state-of-the-art parallel frameworks on challenging benchmarks from six logics in the SMT-COMP 2025 Parallel Track.

cs.LO↗

arXiv2025

Synthesizing Composite Hierarchical Structure from Symbolic Music Corpora

Western music is an innately hierarchical system of interacting levels of structure, from fine-grained melody to high-level form. In order to analyze music compositions holistically and at multiple granularities, we propose a unified, hierarchical meta-representation of musical structure called the structural temporal graph (STG). For a single piece, the STG is a data structure that defines a hierarchy of progressively finer structural musical features and the temporal relationships between them. We use the STG to enable a novel approach for deriving a representative structural summary of a music corpus, which we formalize as a nested NP-hard combinatorial optimization problem extending the Generalized Median Graph problem. Our approach first applies simulated annealing to develop a measure of structural distance between two music pieces rooted in graph isomorphism. Our approach then combines the formal guarantees of SMT solvers with nested simulated annealing over structural distances to produce a structurally sound, representative centroid STG for an entire corpus of STGs from individual pieces. To evaluate our approach, we conduct experiments verifying that structural distance accurately differentiates between music pieces, and that derived centroids accurately structurally characterize their corpora.

cs.AI↗