{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:GYSFKA6KADM4HTMRDO3FXO6R7J","short_pith_number":"pith:GYSFKA6K","schema_version":"1.0","canonical_sha256":"36245503ca00d9c3cd911bb65bbbd1fa57eb19f9a958a2c568e70e286d4dc7f7","source":{"kind":"arxiv","id":"2506.05422","version":1},"attestation_state":"computed","paper":{"title":"Constructive Symbolic Reinforcement Learning via Intuitionistic Logic and Goal-Chaining Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Andrei T. Patrascu","submitted_at":"2025-06-05T04:49:31Z","abstract_excerpt":"We introduce a novel learning and planning framework that replaces traditional reward-based optimisation with constructive logical inference. In our model, actions, transitions, and goals are represented as logical propositions, and decision-making proceeds by building constructive proofs under intuitionistic logic. This method ensures that state transitions and policies are accepted only when supported by verifiable preconditions -- eschewing probabilistic trial-and-error in favour of guaranteed logical validity. We implement a symbolic agent operating in a structured gridworld, where reachin"},"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":"2506.05422","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-06-05T04:49:31Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"779d564fe0d80cb165b1644f6d074547b71ae2bc4341db8ec0c0bad9b2135a34","abstract_canon_sha256":"6d142b41258730e32a44c9a604a11257e120f9daaa6024544276f600754e2b42"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:17:02.361796Z","signature_b64":"HHckHoTDJFheYiaTVssP0ZrTs9ukGSsg4zVGAeHq3dj4UggrB9Uhmrk0NPFCq+pb9IhU96ON5kqLn6oYKGCRAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"36245503ca00d9c3cd911bb65bbbd1fa57eb19f9a958a2c568e70e286d4dc7f7","last_reissued_at":"2026-07-05T11:17:02.361293Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:17:02.361293Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Constructive Symbolic Reinforcement Learning via Intuitionistic Logic and Goal-Chaining Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Andrei T. Patrascu","submitted_at":"2025-06-05T04:49:31Z","abstract_excerpt":"We introduce a novel learning and planning framework that replaces traditional reward-based optimisation with constructive logical inference. In our model, actions, transitions, and goals are represented as logical propositions, and decision-making proceeds by building constructive proofs under intuitionistic logic. This method ensures that state transitions and policies are accepted only when supported by verifiable preconditions -- eschewing probabilistic trial-and-error in favour of guaranteed logical validity. We implement a symbolic agent operating in a structured gridworld, where reachin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.05422","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/2506.05422/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":"2506.05422","created_at":"2026-07-05T11:17:02.361361+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.05422v1","created_at":"2026-07-05T11:17:02.361361+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.05422","created_at":"2026-07-05T11:17:02.361361+00:00"},{"alias_kind":"pith_short_12","alias_value":"GYSFKA6KADM4","created_at":"2026-07-05T11:17:02.361361+00:00"},{"alias_kind":"pith_short_16","alias_value":"GYSFKA6KADM4HTMR","created_at":"2026-07-05T11:17:02.361361+00:00"},{"alias_kind":"pith_short_8","alias_value":"GYSFKA6K","created_at":"2026-07-05T11:17:02.361361+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2603.03971","citing_title":"No Certificate, No Categorical Speech Act: A Brouwerian Assertibility Constraint for Public Reason","ref_index":61,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GYSFKA6KADM4HTMRDO3FXO6R7J","json":"https://pith.science/pith/GYSFKA6KADM4HTMRDO3FXO6R7J.json","graph_json":"https://pith.science/api/pith-number/GYSFKA6KADM4HTMRDO3FXO6R7J/graph.json","events_json":"https://pith.science/api/pith-number/GYSFKA6KADM4HTMRDO3FXO6R7J/events.json","paper":"https://pith.science/paper/GYSFKA6K"},"agent_actions":{"view_html":"https://pith.science/pith/GYSFKA6KADM4HTMRDO3FXO6R7J","download_json":"https://pith.science/pith/GYSFKA6KADM4HTMRDO3FXO6R7J.json","view_paper":"https://pith.science/paper/GYSFKA6K","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.05422&json=true","fetch_graph":"https://pith.science/api/pith-number/GYSFKA6KADM4HTMRDO3FXO6R7J/graph.json","fetch_events":"https://pith.science/api/pith-number/GYSFKA6KADM4HTMRDO3FXO6R7J/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GYSFKA6KADM4HTMRDO3FXO6R7J/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GYSFKA6KADM4HTMRDO3FXO6R7J/action/storage_attestation","attest_author":"https://pith.science/pith/GYSFKA6KADM4HTMRDO3FXO6R7J/action/author_attestation","sign_citation":"https://pith.science/pith/GYSFKA6KADM4HTMRDO3FXO6R7J/action/citation_signature","submit_replication":"https://pith.science/pith/GYSFKA6KADM4HTMRDO3FXO6R7J/action/replication_record"}},"created_at":"2026-07-05T11:17:02.361361+00:00","updated_at":"2026-07-05T11:17:02.361361+00:00"}