{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:UZX5VRBD6NVFWLSQGXQ3P3SAH6","short_pith_number":"pith:UZX5VRBD","schema_version":"1.0","canonical_sha256":"a66fdac423f36a5b2e5035e1b7ee403f83f65470b5996bd43670e932ea5a3bc7","source":{"kind":"arxiv","id":"2502.04530","version":1},"attestation_state":"computed","paper":{"title":"Robust Probabilistic Model Checking with Continuous Reward Domains","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.FL","cs.LG"],"primary_cat":"cs.AI","authors_text":"Antonio Filieri, Hanchun Wang, Ilenia Epifani, Xiaotong Ji","submitted_at":"2025-02-06T22:03:18Z","abstract_excerpt":"Probabilistic model checking traditionally verifies properties on the expected value of a measure of interest. This restriction may fail to capture the quality of service of a significant proportion of a system's runs, especially when the probability distribution of the measure of interest is poorly represented by its expected value due to heavy-tail behaviors or multiple modalities. Recent works inspired by distributional reinforcement learning use discrete histograms to approximate integer reward distribution, but they struggle with continuous reward space and present challenges in balancing"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2502.04530","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-02-06T22:03:18Z","cross_cats_sorted":["cs.FL","cs.LG"],"title_canon_sha256":"f11c59b43512ccb4eef487d1ac40d8caf8bec167106eccc7f47d96c669786c6d","abstract_canon_sha256":"ab87e67fcf1db898947300d79238469ecbdb8905d532924443e2652cb9ae12a1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:10:52.990659Z","signature_b64":"lDdEyD+LKLUfM3TAU/AXhsNtLdz+drHoRCGOHo5taUY6H6RPx8tHriuqCLxsGEHLiMadyojt/ESgQ8ulntSYCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a66fdac423f36a5b2e5035e1b7ee403f83f65470b5996bd43670e932ea5a3bc7","last_reissued_at":"2026-07-05T10:10:52.990232Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:10:52.990232Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Robust Probabilistic Model Checking with Continuous Reward Domains","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.FL","cs.LG"],"primary_cat":"cs.AI","authors_text":"Antonio Filieri, Hanchun Wang, Ilenia Epifani, Xiaotong Ji","submitted_at":"2025-02-06T22:03:18Z","abstract_excerpt":"Probabilistic model checking traditionally verifies properties on the expected value of a measure of interest. This restriction may fail to capture the quality of service of a significant proportion of a system's runs, especially when the probability distribution of the measure of interest is poorly represented by its expected value due to heavy-tail behaviors or multiple modalities. Recent works inspired by distributional reinforcement learning use discrete histograms to approximate integer reward distribution, but they struggle with continuous reward space and present challenges in balancing"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.04530","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2502.04530/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2502.04530","created_at":"2026-07-05T10:10:52.990290+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.04530v1","created_at":"2026-07-05T10:10:52.990290+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.04530","created_at":"2026-07-05T10:10:52.990290+00:00"},{"alias_kind":"pith_short_12","alias_value":"UZX5VRBD6NVF","created_at":"2026-07-05T10:10:52.990290+00:00"},{"alias_kind":"pith_short_16","alias_value":"UZX5VRBD6NVFWLSQ","created_at":"2026-07-05T10:10:52.990290+00:00"},{"alias_kind":"pith_short_8","alias_value":"UZX5VRBD","created_at":"2026-07-05T10:10:52.990290+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UZX5VRBD6NVFWLSQGXQ3P3SAH6","json":"https://pith.science/pith/UZX5VRBD6NVFWLSQGXQ3P3SAH6.json","graph_json":"https://pith.science/api/pith-number/UZX5VRBD6NVFWLSQGXQ3P3SAH6/graph.json","events_json":"https://pith.science/api/pith-number/UZX5VRBD6NVFWLSQGXQ3P3SAH6/events.json","paper":"https://pith.science/paper/UZX5VRBD"},"agent_actions":{"view_html":"https://pith.science/pith/UZX5VRBD6NVFWLSQGXQ3P3SAH6","download_json":"https://pith.science/pith/UZX5VRBD6NVFWLSQGXQ3P3SAH6.json","view_paper":"https://pith.science/paper/UZX5VRBD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.04530&json=true","fetch_graph":"https://pith.science/api/pith-number/UZX5VRBD6NVFWLSQGXQ3P3SAH6/graph.json","fetch_events":"https://pith.science/api/pith-number/UZX5VRBD6NVFWLSQGXQ3P3SAH6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UZX5VRBD6NVFWLSQGXQ3P3SAH6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UZX5VRBD6NVFWLSQGXQ3P3SAH6/action/storage_attestation","attest_author":"https://pith.science/pith/UZX5VRBD6NVFWLSQGXQ3P3SAH6/action/author_attestation","sign_citation":"https://pith.science/pith/UZX5VRBD6NVFWLSQGXQ3P3SAH6/action/citation_signature","submit_replication":"https://pith.science/pith/UZX5VRBD6NVFWLSQGXQ3P3SAH6/action/replication_record"}},"created_at":"2026-07-05T10:10:52.990290+00:00","updated_at":"2026-07-05T10:10:52.990290+00:00"}