{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:U3RHDBINZXVR6XYS4WLLFTAPTW","short_pith_number":"pith:U3RHDBIN","schema_version":"1.0","canonical_sha256":"a6e271850dcdeb1f5f12e596b2cc0f9d9917db606785f97b01dbe26f9def38f4","source":{"kind":"arxiv","id":"2507.08705","version":1},"attestation_state":"computed","paper":{"title":"elsciRL: Integrating Language Solutions into Reinforcement Learning Problem Settings","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Andr\\'e Freitas, Danilo S. Carvalho, Philip Osborne","submitted_at":"2025-07-11T16:02:24Z","abstract_excerpt":"We present elsciRL, an open-source Python library to facilitate the application of language solutions on reinforcement learning problems. We demonstrate the potential of our software by extending the Language Adapter with Self-Completing Instruction framework defined in (Osborne, 2024) with the use of LLMs. Our approach can be re-applied to new applications with minimal setup requirements. We provide a novel GUI that allows a user to provide text input for an LLM to generate instructions which it can then self-complete. Empirical results indicate that these instructions \\textit{can} improve a "},"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":"2507.08705","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-07-11T16:02:24Z","cross_cats_sorted":[],"title_canon_sha256":"c8cb8af003af08505034ab2307843a496d9bdb605a2efa6a09e208692a50b7d9","abstract_canon_sha256":"95ecf94f79606a391b3f56c63c8eb1da05e6179c894b3afc1c6c1220beb468fa"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:35:45.191574Z","signature_b64":"rYvry4JOW9lV0tcICyJgxAcFD3ZWfA05tZg8EQaglz1K6ZKPxbR4gZIiJ/7OH3lQtQKe5Ig4zYXZrpVHcuU4DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a6e271850dcdeb1f5f12e596b2cc0f9d9917db606785f97b01dbe26f9def38f4","last_reissued_at":"2026-07-05T11:35:45.191133Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:35:45.191133Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"elsciRL: Integrating Language Solutions into Reinforcement Learning Problem Settings","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Andr\\'e Freitas, Danilo S. Carvalho, Philip Osborne","submitted_at":"2025-07-11T16:02:24Z","abstract_excerpt":"We present elsciRL, an open-source Python library to facilitate the application of language solutions on reinforcement learning problems. We demonstrate the potential of our software by extending the Language Adapter with Self-Completing Instruction framework defined in (Osborne, 2024) with the use of LLMs. Our approach can be re-applied to new applications with minimal setup requirements. We provide a novel GUI that allows a user to provide text input for an LLM to generate instructions which it can then self-complete. Empirical results indicate that these instructions \\textit{can} improve a "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.08705","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/2507.08705/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":"2507.08705","created_at":"2026-07-05T11:35:45.191189+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.08705v1","created_at":"2026-07-05T11:35:45.191189+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.08705","created_at":"2026-07-05T11:35:45.191189+00:00"},{"alias_kind":"pith_short_12","alias_value":"U3RHDBINZXVR","created_at":"2026-07-05T11:35:45.191189+00:00"},{"alias_kind":"pith_short_16","alias_value":"U3RHDBINZXVR6XYS","created_at":"2026-07-05T11:35:45.191189+00:00"},{"alias_kind":"pith_short_8","alias_value":"U3RHDBIN","created_at":"2026-07-05T11:35:45.191189+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/U3RHDBINZXVR6XYS4WLLFTAPTW","json":"https://pith.science/pith/U3RHDBINZXVR6XYS4WLLFTAPTW.json","graph_json":"https://pith.science/api/pith-number/U3RHDBINZXVR6XYS4WLLFTAPTW/graph.json","events_json":"https://pith.science/api/pith-number/U3RHDBINZXVR6XYS4WLLFTAPTW/events.json","paper":"https://pith.science/paper/U3RHDBIN"},"agent_actions":{"view_html":"https://pith.science/pith/U3RHDBINZXVR6XYS4WLLFTAPTW","download_json":"https://pith.science/pith/U3RHDBINZXVR6XYS4WLLFTAPTW.json","view_paper":"https://pith.science/paper/U3RHDBIN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.08705&json=true","fetch_graph":"https://pith.science/api/pith-number/U3RHDBINZXVR6XYS4WLLFTAPTW/graph.json","fetch_events":"https://pith.science/api/pith-number/U3RHDBINZXVR6XYS4WLLFTAPTW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/U3RHDBINZXVR6XYS4WLLFTAPTW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/U3RHDBINZXVR6XYS4WLLFTAPTW/action/storage_attestation","attest_author":"https://pith.science/pith/U3RHDBINZXVR6XYS4WLLFTAPTW/action/author_attestation","sign_citation":"https://pith.science/pith/U3RHDBINZXVR6XYS4WLLFTAPTW/action/citation_signature","submit_replication":"https://pith.science/pith/U3RHDBINZXVR6XYS4WLLFTAPTW/action/replication_record"}},"created_at":"2026-07-05T11:35:45.191189+00:00","updated_at":"2026-07-05T11:35:45.191189+00:00"}