{"paper":{"title":"String Solving with Stabilization and Transducers (Technical Report)","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"Generalizing stabilization to transducers handles relational string constraints efficiently by reducing costly length operations.","cross_cats":["cs.LO"],"primary_cat":"cs.FL","authors_text":"David Chocholat\\'y, Juraj S\\'i\\v{c}, Luk\\'a\\v{s} Hol\\'ik, Michal \\v{S}ed\\'y, Ond\\v{r}ej Leng\\'al, Vojt\\v{e}ch Havlena","submitted_at":"2026-05-14T14:18:40Z","abstract_excerpt":"We generalize an efficient automata-based approach to string constraint solving, the stabilization-based method behind the solver Z3-Noodler, to support relational constraints represented by finite-state transducers (useful, for example, for modeling replaceAll constraints). We focus on an efficient treatment of length constraints by reducing the need for expensive concatenation elimination, which is a major bottleneck in automata-based string solving. We also propose powerful heuristics that significantly improve performance in practice. Implemented on top of Z3-Noodler, our method vastly out"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Implemented on top of Z3-Noodler, our method vastly outperforms existing solvers on benchmarks with relational constraints. It solves more instances and runs orders of magnitude faster.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the transducer encoding and length-constraint reductions preserve soundness and completeness while the new heuristics do not miss solutions on realistic inputs.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Generalization of Z3-Noodler's stabilization method to transducers enables efficient solving of relational string constraints and outperforms prior solvers by solving more instances orders of magnitude faster.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Generalizing stabilization to transducers handles relational string constraints efficiently by reducing costly length operations.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"575495160beb33de71a8efd223dad3f6ccd24b416dd78991951e1362ac46bc58"},"source":{"id":"2605.14872","kind":"arxiv","version":1},"verdict":{"id":"545a2762-8b0c-47a1-a0b9-be9abb37e418","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T03:01:36.414749Z","strongest_claim":"Implemented on top of Z3-Noodler, our method vastly outperforms existing solvers on benchmarks with relational constraints. It solves more instances and runs orders of magnitude faster.","one_line_summary":"Generalization of Z3-Noodler's stabilization method to transducers enables efficient solving of relational string constraints and outperforms prior solvers by solving more instances orders of magnitude faster.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the transducer encoding and length-constraint reductions preserve soundness and completeness while the new heuristics do not miss solutions on realistic inputs.","pith_extraction_headline":"Generalizing stabilization to transducers handles relational string constraints efficiently by reducing costly length operations."},"references":{"count":50,"sample":[{"doi":"","year":2024,"title":"SMT-COMP QF Strings, 2024 (2024),https://smt-comp.github.io/2024/ results/qf_strings-single-query/,https://smt-comp.github.io/2024/ results/qf_strings-single-query/","work_id":"35f9dba7-a74e-4406-9fe4-55a2560e1638","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"SMT-COMP QF Strings, 2025 (2025),https://smt-comp.github.io/2025/ results/qf_slia-single-query/,https://smt-comp.github.io/2025/ results/qf_slia-single-query/","work_id":"b76c2cf5-9038-4d37-a3ca-3914ca9a2757","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1145/3062341.3062384","year":2017,"title":"In: Co- hen, A., Vechev, M.T","work_id":"f75713d0-c271-4139-8262-42be71bc2aef","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2014,"title":"In: Biere, A., Bloem, R","work_id":"67565f65-daf4-418b-ac29-194fae316862","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1007/978-3-319-08867-9_10","year":2014,"title":"Springer (2014).https://doi.org/10.1007/978-3-319-08867-9_10,https: //doi.org/10.1007/978-3-319-08867-9_10","work_id":"9e6c4e88-d73c-486a-9036-23b96b2a530e","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":50,"snapshot_sha256":"ccebb0b1a3adc8bb1cef458f8ca7df1e4956f0c8cc9a53890289ed152fe9d42f","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"dd4360bf0bdba04d1f88c533742f17b66f472e3f2efd9fac3db334aba49059ea"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}