{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:GHO5UWCSVLPC556ND3UJOFEBOE","short_pith_number":"pith:GHO5UWCS","schema_version":"1.0","canonical_sha256":"31ddda5852aade2ef7cd1ee897148171176d8f1cee9cb23571a2420886787ab2","source":{"kind":"arxiv","id":"2608.03502","version":1},"attestation_state":"computed","paper":{"title":"Hybrid LLM-Augmented Reinforcement Learning Agents for Complex Sequential Decision Tasks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.MA"],"primary_cat":"cs.AI","authors_text":"Christophe D. Hounwanou, John Emeka Eze, Ya\\'e Ulrich Gaba","submitted_at":"2026-08-04T11:44:07Z","abstract_excerpt":"Large Language Models (LLMs) have recently shown strong capabilities in reasoning, planning, and tool-use, enabling new forms of autonomous agents. However, LLM-based agents struggle with long-horizon sequential decision tasks that require precise action optimization and environment interaction. Reinforcement Learning (RL), while effective for sequential control, often lacks the high-level abstraction and task decomposition abilities needed for complex scenarios. This paper introduces an LLM-Augmented Reinforcement Learning Agent that integrates LLM-driven planning with RL-based action optimiz"},"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":"2608.03502","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-08-04T11:44:07Z","cross_cats_sorted":["cs.LG","cs.MA"],"title_canon_sha256":"98262045dab1d8241f564e2c3f326c06b392528627bd02c293ae96a92900fd7a","abstract_canon_sha256":"13750035f4dcfe187fa860ec88882c8aff76f7f0e302a322d6a4c60e8f3de1c0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-05T01:35:58.575184Z","signature_b64":"hWDf6t8mJRh/+lPwzr/tmXWRriV2v+0jNxljqL8xCeMlGc8eTP4mJbbDut0TUzpVqEZ/AyAX5Tw4uAH+P/YsBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"31ddda5852aade2ef7cd1ee897148171176d8f1cee9cb23571a2420886787ab2","last_reissued_at":"2026-08-05T01:35:58.573593Z","signature_status":"signed_v1","first_computed_at":"2026-08-05T01:35:58.573593Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hybrid LLM-Augmented Reinforcement Learning Agents for Complex Sequential Decision Tasks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.MA"],"primary_cat":"cs.AI","authors_text":"Christophe D. Hounwanou, John Emeka Eze, Ya\\'e Ulrich Gaba","submitted_at":"2026-08-04T11:44:07Z","abstract_excerpt":"Large Language Models (LLMs) have recently shown strong capabilities in reasoning, planning, and tool-use, enabling new forms of autonomous agents. However, LLM-based agents struggle with long-horizon sequential decision tasks that require precise action optimization and environment interaction. Reinforcement Learning (RL), while effective for sequential control, often lacks the high-level abstraction and task decomposition abilities needed for complex scenarios. This paper introduces an LLM-Augmented Reinforcement Learning Agent that integrates LLM-driven planning with RL-based action optimiz"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.03502","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/2608.03502/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":"2608.03502","created_at":"2026-08-05T01:35:58.574029+00:00"},{"alias_kind":"arxiv_version","alias_value":"2608.03502v1","created_at":"2026-08-05T01:35:58.574029+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.03502","created_at":"2026-08-05T01:35:58.574029+00:00"},{"alias_kind":"pith_short_12","alias_value":"GHO5UWCSVLPC","created_at":"2026-08-05T01:35:58.574029+00:00"},{"alias_kind":"pith_short_16","alias_value":"GHO5UWCSVLPC556N","created_at":"2026-08-05T01:35:58.574029+00:00"},{"alias_kind":"pith_short_8","alias_value":"GHO5UWCS","created_at":"2026-08-05T01:35:58.574029+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/GHO5UWCSVLPC556ND3UJOFEBOE","json":"https://pith.science/pith/GHO5UWCSVLPC556ND3UJOFEBOE.json","graph_json":"https://pith.science/api/pith-number/GHO5UWCSVLPC556ND3UJOFEBOE/graph.json","events_json":"https://pith.science/api/pith-number/GHO5UWCSVLPC556ND3UJOFEBOE/events.json","paper":"https://pith.science/paper/GHO5UWCS"},"agent_actions":{"view_html":"https://pith.science/pith/GHO5UWCSVLPC556ND3UJOFEBOE","download_json":"https://pith.science/pith/GHO5UWCSVLPC556ND3UJOFEBOE.json","view_paper":"https://pith.science/paper/GHO5UWCS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2608.03502&json=true","fetch_graph":"https://pith.science/api/pith-number/GHO5UWCSVLPC556ND3UJOFEBOE/graph.json","fetch_events":"https://pith.science/api/pith-number/GHO5UWCSVLPC556ND3UJOFEBOE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GHO5UWCSVLPC556ND3UJOFEBOE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GHO5UWCSVLPC556ND3UJOFEBOE/action/storage_attestation","attest_author":"https://pith.science/pith/GHO5UWCSVLPC556ND3UJOFEBOE/action/author_attestation","sign_citation":"https://pith.science/pith/GHO5UWCSVLPC556ND3UJOFEBOE/action/citation_signature","submit_replication":"https://pith.science/pith/GHO5UWCSVLPC556ND3UJOFEBOE/action/replication_record"}},"created_at":"2026-08-05T01:35:58.574029+00:00","updated_at":"2026-08-05T01:35:58.574029+00:00"}