{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:VGMSKL2GDHOUTFZEXIGG4V2EWV","short_pith_number":"pith:VGMSKL2G","canonical_record":{"source":{"id":"2506.14045","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-06-16T22:36:32Z","cross_cats_sorted":[],"title_canon_sha256":"44a2ce45207933fe1206470cac0c9796d2a8e84608ac1ba5031b3a880cd55246","abstract_canon_sha256":"b71256e9c8c70da96c4cdcbf5b333881b25e1181fbb467b03903ddeef35a819b"},"schema_version":"1.0"},"canonical_sha256":"a999252f4619dd499724ba0c6e5744b5409ab9a07af0919ee1574becc7f213a9","source":{"kind":"arxiv","id":"2506.14045","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.14045","created_at":"2026-07-05T11:22:48Z"},{"alias_kind":"arxiv_version","alias_value":"2506.14045v1","created_at":"2026-07-05T11:22:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.14045","created_at":"2026-07-05T11:22:48Z"},{"alias_kind":"pith_short_12","alias_value":"VGMSKL2GDHOU","created_at":"2026-07-05T11:22:48Z"},{"alias_kind":"pith_short_16","alias_value":"VGMSKL2GDHOUTFZE","created_at":"2026-07-05T11:22:48Z"},{"alias_kind":"pith_short_8","alias_value":"VGMSKL2G","created_at":"2026-07-05T11:22:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:VGMSKL2GDHOUTFZEXIGG4V2EWV","target":"record","payload":{"canonical_record":{"source":{"id":"2506.14045","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-06-16T22:36:32Z","cross_cats_sorted":[],"title_canon_sha256":"44a2ce45207933fe1206470cac0c9796d2a8e84608ac1ba5031b3a880cd55246","abstract_canon_sha256":"b71256e9c8c70da96c4cdcbf5b333881b25e1181fbb467b03903ddeef35a819b"},"schema_version":"1.0"},"canonical_sha256":"a999252f4619dd499724ba0c6e5744b5409ab9a07af0919ee1574becc7f213a9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:22:48.056105Z","signature_b64":"Bl661KesDYmObPXRRX1JSIQroTGY+x9MdxxBMlL23n4gv+vBk4jj7JXbqILVIyfVosdyXO59yLBItDif+297CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a999252f4619dd499724ba0c6e5744b5409ab9a07af0919ee1574becc7f213a9","last_reissued_at":"2026-07-05T11:22:48.055606Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:22:48.055606Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.14045","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:22:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"t4VKAmZdMhAmLOdq1Xw45uuXYWXTNaTQcQDnjVI+VyDKHNiBc13aqLD/SAc/joi1FPXqd8ZRhf1XLTJpNhQqBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T15:11:50.922853Z"},"content_sha256":"4fdfb3b25b2bc003cb96efb4e7d5c215462e45061861706a2278631ee5335c6d","schema_version":"1.0","event_id":"sha256:4fdfb3b25b2bc003cb96efb4e7d5c215462e45061861706a2278631ee5335c6d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:VGMSKL2GDHOUTFZEXIGG4V2EWV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Discovering Temporal Structure: An Overview of Hierarchical Reinforcement Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Akhil Bagaria, Doina Precup, George Konidaris, Marlos C. Machado, Martin Klissarov, Ziyan Luo","submitted_at":"2025-06-16T22:36:32Z","abstract_excerpt":"Developing agents capable of exploring, planning and learning in complex open-ended environments is a grand challenge in artificial intelligence (AI). Hierarchical reinforcement learning (HRL) offers a promising solution to this challenge by discovering and exploiting the temporal structure within a stream of experience. The strong appeal of the HRL framework has led to a rich and diverse body of literature attempting to discover a useful structure. However, it is still not clear how one might define what constitutes good structure in the first place, or the kind of problems in which identifyi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.14045","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/2506.14045/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:22:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MXOiyNAazdNmMN/X3hY93ATQ68+Q6gq7W95K0ZsvVi6SKj+JnIh8IVJhN+0frPCGVcAKG/w5AnUeCE00pwAFBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T15:11:50.923345Z"},"content_sha256":"84c2888a2131f4211ba85574dd1a28f9bc312d209933c233b4eb882501334c5d","schema_version":"1.0","event_id":"sha256:84c2888a2131f4211ba85574dd1a28f9bc312d209933c233b4eb882501334c5d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VGMSKL2GDHOUTFZEXIGG4V2EWV/bundle.json","state_url":"https://pith.science/pith/VGMSKL2GDHOUTFZEXIGG4V2EWV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VGMSKL2GDHOUTFZEXIGG4V2EWV/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-05T15:11:50Z","links":{"resolver":"https://pith.science/pith/VGMSKL2GDHOUTFZEXIGG4V2EWV","bundle":"https://pith.science/pith/VGMSKL2GDHOUTFZEXIGG4V2EWV/bundle.json","state":"https://pith.science/pith/VGMSKL2GDHOUTFZEXIGG4V2EWV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VGMSKL2GDHOUTFZEXIGG4V2EWV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:VGMSKL2GDHOUTFZEXIGG4V2EWV","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"b71256e9c8c70da96c4cdcbf5b333881b25e1181fbb467b03903ddeef35a819b","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-06-16T22:36:32Z","title_canon_sha256":"44a2ce45207933fe1206470cac0c9796d2a8e84608ac1ba5031b3a880cd55246"},"schema_version":"1.0","source":{"id":"2506.14045","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.14045","created_at":"2026-07-05T11:22:48Z"},{"alias_kind":"arxiv_version","alias_value":"2506.14045v1","created_at":"2026-07-05T11:22:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.14045","created_at":"2026-07-05T11:22:48Z"},{"alias_kind":"pith_short_12","alias_value":"VGMSKL2GDHOU","created_at":"2026-07-05T11:22:48Z"},{"alias_kind":"pith_short_16","alias_value":"VGMSKL2GDHOUTFZE","created_at":"2026-07-05T11:22:48Z"},{"alias_kind":"pith_short_8","alias_value":"VGMSKL2G","created_at":"2026-07-05T11:22:48Z"}],"graph_snapshots":[{"event_id":"sha256:84c2888a2131f4211ba85574dd1a28f9bc312d209933c233b4eb882501334c5d","target":"graph","created_at":"2026-07-05T11:22:48Z","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/2506.14045/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Developing agents capable of exploring, planning and learning in complex open-ended environments is a grand challenge in artificial intelligence (AI). Hierarchical reinforcement learning (HRL) offers a promising solution to this challenge by discovering and exploiting the temporal structure within a stream of experience. The strong appeal of the HRL framework has led to a rich and diverse body of literature attempting to discover a useful structure. However, it is still not clear how one might define what constitutes good structure in the first place, or the kind of problems in which identifyi","authors_text":"Akhil Bagaria, Doina Precup, George Konidaris, Marlos C. Machado, Martin Klissarov, Ziyan Luo","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-06-16T22:36:32Z","title":"Discovering Temporal Structure: An Overview of Hierarchical Reinforcement Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.14045","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":"sha256:4fdfb3b25b2bc003cb96efb4e7d5c215462e45061861706a2278631ee5335c6d","target":"record","created_at":"2026-07-05T11:22:48Z","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":"b71256e9c8c70da96c4cdcbf5b333881b25e1181fbb467b03903ddeef35a819b","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-06-16T22:36:32Z","title_canon_sha256":"44a2ce45207933fe1206470cac0c9796d2a8e84608ac1ba5031b3a880cd55246"},"schema_version":"1.0","source":{"id":"2506.14045","kind":"arxiv","version":1}},"canonical_sha256":"a999252f4619dd499724ba0c6e5744b5409ab9a07af0919ee1574becc7f213a9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a999252f4619dd499724ba0c6e5744b5409ab9a07af0919ee1574becc7f213a9","first_computed_at":"2026-07-05T11:22:48.055606Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:22:48.055606Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Bl661KesDYmObPXRRX1JSIQroTGY+x9MdxxBMlL23n4gv+vBk4jj7JXbqILVIyfVosdyXO59yLBItDif+297CA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:22:48.056105Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.14045","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4fdfb3b25b2bc003cb96efb4e7d5c215462e45061861706a2278631ee5335c6d","sha256:84c2888a2131f4211ba85574dd1a28f9bc312d209933c233b4eb882501334c5d"],"state_sha256":"b20d6f1c46a59a01417d2e4a398a4adff95e228a9a97533f37dcb56da27a94e3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9LJEAcR4Bdd8NzHHwpUNBaqH3IcKwM1+ojV1PUnETXh+tOlmxFcWiVZCN1pYsOVALY3pZu3CZlWsUM+/UNlbBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T15:11:50.937482Z","bundle_sha256":"e9923f85b48b3660e12f5c3bd3c8be81c52b123339b1526caecc4ea8d1979979"}}