{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:BEBDPBDOLN5VPXEJSA7IJKAK7S","merge_version":"pith-open-graph-merge-v1","event_count":3,"valid_event_count":3,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"969a4677d953c66af017c11e264974b7c66ca2051fe59262d587f77f36120675","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-07-15T10:02:55Z","title_canon_sha256":"c056ce4adaccf0ec50ca9f8b3e6a8d9bd3532cfabf7201fa28c2de6ef25192c4"},"schema_version":"1.0","source":{"id":"2607.13655","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.13655","created_at":"2026-07-16T01:22:59Z"},{"alias_kind":"arxiv_version","alias_value":"2607.13655v1","created_at":"2026-07-16T01:22:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.13655","created_at":"2026-07-16T01:22:59Z"},{"alias_kind":"pith_short_12","alias_value":"BEBDPBDOLN5V","created_at":"2026-07-16T01:22:59Z"},{"alias_kind":"pith_short_16","alias_value":"BEBDPBDOLN5VPXEJ","created_at":"2026-07-16T01:22:59Z"},{"alias_kind":"pith_short_8","alias_value":"BEBDPBDO","created_at":"2026-07-16T01:22:59Z"}],"graph_snapshots":[{"event_id":"sha256:886ec0f989e7e35f7a87932a5c7a94605d9f72396e605413998edd7dd1b3331a","target":"graph","created_at":"2026-07-16T01:22:59Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2607.13655/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Explainable Reinforcement Learning (XRL) seeks to make Reinforcement Learning (RL) policies more transparent and interpretable, a key requirement in safety-critical and human-centric scenarios. However, it is mostly based on user studies, thus targeting the needs of a specific audience and lacking shared evaluation metrics. On the other hand, logic-based approaches within eXplainable Artificial Intelligence (XAI) provide compact, human-readable abstractions of decision-making. However, the systematic quantification of the explainability degree of logical representations remains an open problem","authors_text":"Alessandro Farinelli, Celeste Veronese, Daniele Meli, Edoardo Zorzi","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-07-15T10:02:55Z","title":"Explaining Reinforcement Learning Agents via Inductive Logic Programming"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.13655","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:55f297fa6e909fc47c08d695011aa4413b97a64d2cc440df853d6927d4c793fb","target":"record","created_at":"2026-07-16T01:22:59Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"969a4677d953c66af017c11e264974b7c66ca2051fe59262d587f77f36120675","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-07-15T10:02:55Z","title_canon_sha256":"c056ce4adaccf0ec50ca9f8b3e6a8d9bd3532cfabf7201fa28c2de6ef25192c4"},"schema_version":"1.0","source":{"id":"2607.13655","kind":"arxiv","version":1}},"canonical_sha256":"090237846e5b7b57dc89903e84a80afcb50c69f4eec15d1697dbbd24fa16871d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"090237846e5b7b57dc89903e84a80afcb50c69f4eec15d1697dbbd24fa16871d","first_computed_at":"2026-07-16T01:22:59.208308Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-16T01:22:59.208308Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"wtmKyj96zlyGbn0aVtoS0FcHNBtP4wQTgRtnJWEI0bI2DYfPsFxBQPK0msgbRVxjYgks9pD2gJFcSn95fMVtBA==","signature_status":"signed_v1","signed_at":"2026-07-16T01:22:59.209151Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.13655","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:55f297fa6e909fc47c08d695011aa4413b97a64d2cc440df853d6927d4c793fb","sha256:886ec0f989e7e35f7a87932a5c7a94605d9f72396e605413998edd7dd1b3331a","sha256:99e5bd98e612c436fc71046b12008b30a06925af0d89b5b2d391ed42f6b0cb34"],"state_sha256":"863884de73e479b44364c575ac2ab6ecaafc5035b23712b9f15caaa6e563add7"}