{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:LGNRQLIJJXXWEMZ6JNYC2DOOVB","short_pith_number":"pith:LGNRQLIJ","schema_version":"1.0","canonical_sha256":"599b182d094def62333e4b702d0dcea84b65947da4a51e3029bcb1eb04df9088","source":{"kind":"arxiv","id":"2311.09027","version":1},"attestation_state":"computed","paper":{"title":"Assessing the Robustness of Intelligence-Driven Reinforcement Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CR"],"primary_cat":"cs.LG","authors_text":"Federico Cerutti, Lorenzo Nodari","submitted_at":"2023-11-15T15:15:57Z","abstract_excerpt":"Robustness to noise is of utmost importance in reinforcement learning systems, particularly in military contexts where high stakes and uncertain environments prevail. Noise and uncertainty are inherent features of military operations, arising from factors such as incomplete information, adversarial actions, or unpredictable battlefield conditions. In RL, noise can critically impact decision-making, mission success, and the safety of personnel. Reward machines offer a powerful tool to express complex reward structures in RL tasks, enabling the design of tailored reinforcement signals that align"},"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":"2311.09027","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-11-15T15:15:57Z","cross_cats_sorted":["cs.AI","cs.CR"],"title_canon_sha256":"d0fed47cb836844c5e19dd68eb57c920bf4f96a08af17db94b623405178ad46a","abstract_canon_sha256":"2c04eeb1b1512ad32deddbde6c9d1bf73fe695ae5e9452e24188d3dd33ca26f7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:13:07.796574Z","signature_b64":"cLQtN5YQbMYhAph0uDBAaElnzfrmAWjarU9K2kJag5EWRLK00oCu0UaqdBYV1ok60BzgmWMk0jdg8L0Gd8+fBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"599b182d094def62333e4b702d0dcea84b65947da4a51e3029bcb1eb04df9088","last_reissued_at":"2026-07-05T07:13:07.796105Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:13:07.796105Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Assessing the Robustness of Intelligence-Driven Reinforcement Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CR"],"primary_cat":"cs.LG","authors_text":"Federico Cerutti, Lorenzo Nodari","submitted_at":"2023-11-15T15:15:57Z","abstract_excerpt":"Robustness to noise is of utmost importance in reinforcement learning systems, particularly in military contexts where high stakes and uncertain environments prevail. Noise and uncertainty are inherent features of military operations, arising from factors such as incomplete information, adversarial actions, or unpredictable battlefield conditions. In RL, noise can critically impact decision-making, mission success, and the safety of personnel. Reward machines offer a powerful tool to express complex reward structures in RL tasks, enabling the design of tailored reinforcement signals that align"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.09027","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/2311.09027/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":"2311.09027","created_at":"2026-07-05T07:13:07.796160+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.09027v1","created_at":"2026-07-05T07:13:07.796160+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.09027","created_at":"2026-07-05T07:13:07.796160+00:00"},{"alias_kind":"pith_short_12","alias_value":"LGNRQLIJJXXW","created_at":"2026-07-05T07:13:07.796160+00:00"},{"alias_kind":"pith_short_16","alias_value":"LGNRQLIJJXXWEMZ6","created_at":"2026-07-05T07:13:07.796160+00:00"},{"alias_kind":"pith_short_8","alias_value":"LGNRQLIJ","created_at":"2026-07-05T07:13:07.796160+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/LGNRQLIJJXXWEMZ6JNYC2DOOVB","json":"https://pith.science/pith/LGNRQLIJJXXWEMZ6JNYC2DOOVB.json","graph_json":"https://pith.science/api/pith-number/LGNRQLIJJXXWEMZ6JNYC2DOOVB/graph.json","events_json":"https://pith.science/api/pith-number/LGNRQLIJJXXWEMZ6JNYC2DOOVB/events.json","paper":"https://pith.science/paper/LGNRQLIJ"},"agent_actions":{"view_html":"https://pith.science/pith/LGNRQLIJJXXWEMZ6JNYC2DOOVB","download_json":"https://pith.science/pith/LGNRQLIJJXXWEMZ6JNYC2DOOVB.json","view_paper":"https://pith.science/paper/LGNRQLIJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.09027&json=true","fetch_graph":"https://pith.science/api/pith-number/LGNRQLIJJXXWEMZ6JNYC2DOOVB/graph.json","fetch_events":"https://pith.science/api/pith-number/LGNRQLIJJXXWEMZ6JNYC2DOOVB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LGNRQLIJJXXWEMZ6JNYC2DOOVB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LGNRQLIJJXXWEMZ6JNYC2DOOVB/action/storage_attestation","attest_author":"https://pith.science/pith/LGNRQLIJJXXWEMZ6JNYC2DOOVB/action/author_attestation","sign_citation":"https://pith.science/pith/LGNRQLIJJXXWEMZ6JNYC2DOOVB/action/citation_signature","submit_replication":"https://pith.science/pith/LGNRQLIJJXXWEMZ6JNYC2DOOVB/action/replication_record"}},"created_at":"2026-07-05T07:13:07.796160+00:00","updated_at":"2026-07-05T07:13:07.796160+00:00"}