{"paper":{"title":"Multi-Property Temporal Logic Monitoring","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"A shared directed acyclic graph reuses intermediate results when monitoring multiple past-time LTL and MTL properties at once.","cross_cats":[],"primary_cat":"cs.LO","authors_text":"Ar{\\i}n\\c{c} Demir, Dogan Ulus","submitted_at":"2026-05-13T15:29:38Z","abstract_excerpt":"Runtime verification enables checking temporal logic specifications over individual execution traces and offers a scalable alternative to exhaustive formal verification. In practice, systems must satisfy dozens to hundreds of temporal properties simultaneously; however, existing approaches monitor each property in isolation, resulting in redundant computation and limited scalability. In this work, we present an online multi-property monitoring framework that compiles past-time LTL and MTL specifications into a shared directed acyclic graph of subformulas with one output per property. Unlike pr"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"our method extends compositional sequential network-based temporal logic monitor construction to a shared setting, enabling reuse of intermediate results across properties while preserving their individual structure. Experimental results demonstrate per-property throughput improvements of 2x to 4.5x and 6x to 12x in multi-property configurations compared to conventional single-property monitoring.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That specifications in practice share enough subformulas for the shared DAG to deliver meaningful reuse, and that the arena-allocated double-buffered memory layout will maintain spatial locality and low overhead across diverse hardware and property sets without hidden costs.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A shared DAG-based online monitor for multiple past-time LTL and MTL properties reuses subformula results via arena-allocated double-buffered memory to achieve 2x-12x per-property throughput gains over isolated monitors.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A shared directed acyclic graph reuses intermediate results when monitoring multiple past-time LTL and MTL properties at once.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"55ecf098daf80ffd34af4af9b3b6b660961c7a03355f8cfa2a18ed836c605cc6"},"source":{"id":"2605.13668","kind":"arxiv","version":1},"verdict":{"id":"69a8a8e9-c1ca-464a-a8fa-b721a62ef4e1","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-14T17:49:10.603610Z","strongest_claim":"our method extends compositional sequential network-based temporal logic monitor construction to a shared setting, enabling reuse of intermediate results across properties while preserving their individual structure. Experimental results demonstrate per-property throughput improvements of 2x to 4.5x and 6x to 12x in multi-property configurations compared to conventional single-property monitoring.","one_line_summary":"A shared DAG-based online monitor for multiple past-time LTL and MTL properties reuses subformula results via arena-allocated double-buffered memory to achieve 2x-12x per-property throughput gains over isolated monitors.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That specifications in practice share enough subformulas for the shared DAG to deliver meaningful reuse, and that the arena-allocated double-buffered memory layout will maintain spatial locality and low overhead across diverse hardware and property sets without hidden costs.","pith_extraction_headline":"A shared directed acyclic graph reuses intermediate results when monitoring multiple past-time LTL and MTL properties at once."},"references":{"count":26,"sample":[{"doi":"","year":1977,"title":"The temporal logic of programs,","work_id":"da85a58b-dc14-4658-83aa-e6a0423ea269","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1990,"title":"Specifying real-time properties with metric temporal logic,","work_id":"2a114d8b-823a-4b23-b5d3-53dcecaaa6cc","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2026,"title":"Online monitoring of metric temporal logic using sequential networks,","work_id":"6473fb53-a7b6-4d02-ad92-c05f9195bc0d","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2026,"title":"Reelay: Online Temporal Logic Monitoring Framework","work_id":"f66956cf-b3bd-445d-881e-3ee3c9a48e49","ref_index":4,"cited_arxiv_id":"2604.22384","is_internal_anchor":true},{"doi":"","year":2019,"title":"Parsing gigabytes of JSON per second,","work_id":"f62ed5d2-136b-49e4-905d-381259b9bd05","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":26,"snapshot_sha256":"eafa988af1738a9c954c2dce675dc9ff2c7de0ff3c8d9b49cbfd4b2efc23ef86","internal_anchors":1},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}