{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:V62HDCA74ZR5FYVBECO3XYQMOY","short_pith_number":"pith:V62HDCA7","canonical_record":{"source":{"id":"2506.07744","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-09T13:26:23Z","cross_cats_sorted":["cs.AI","cs.RO"],"title_canon_sha256":"ae0b9ee26b47fc3da2d11d13c5f3cdf54671657b5d40fd4908868d42b4d671d0","abstract_canon_sha256":"9f606fff486c98071612c2a48a4a9d45d52b84d50bb0859f822e6b2af39492c7"},"schema_version":"1.0"},"canonical_sha256":"afb471881fe663d2e2a1209dbbe20c7615ea92aa83fe103308d0643345f1c7a8","source":{"kind":"arxiv","id":"2506.07744","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.07744","created_at":"2026-07-05T11:33:05Z"},{"alias_kind":"arxiv_version","alias_value":"2506.07744v3","created_at":"2026-07-05T11:33:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.07744","created_at":"2026-07-05T11:33:05Z"},{"alias_kind":"pith_short_12","alias_value":"V62HDCA74ZR5","created_at":"2026-07-05T11:33:05Z"},{"alias_kind":"pith_short_16","alias_value":"V62HDCA74ZR5FYVB","created_at":"2026-07-05T11:33:05Z"},{"alias_kind":"pith_short_8","alias_value":"V62HDCA7","created_at":"2026-07-05T11:33:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:V62HDCA74ZR5FYVBECO3XYQMOY","target":"record","payload":{"canonical_record":{"source":{"id":"2506.07744","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-09T13:26:23Z","cross_cats_sorted":["cs.AI","cs.RO"],"title_canon_sha256":"ae0b9ee26b47fc3da2d11d13c5f3cdf54671657b5d40fd4908868d42b4d671d0","abstract_canon_sha256":"9f606fff486c98071612c2a48a4a9d45d52b84d50bb0859f822e6b2af39492c7"},"schema_version":"1.0"},"canonical_sha256":"afb471881fe663d2e2a1209dbbe20c7615ea92aa83fe103308d0643345f1c7a8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:33:05.430414Z","signature_b64":"i/vFipgbDhy3R/T994etV5jPkTK3CRO62cAeuCk3dPsWEiUZsl+0IAdUSHG7JTheP4jXToeQx31iHAC7Wxq6CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"afb471881fe663d2e2a1209dbbe20c7615ea92aa83fe103308d0643345f1c7a8","last_reissued_at":"2026-07-05T11:33:05.429904Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:33:05.429904Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.07744","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:33:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UYVJaU6DsMNEFyqVAi7jVhng8eY2UGGNSMvnQFjlPFa+eCGTrJGWvdY3X2eyO67a1qs5cLCwCsHZAvo/ds6JDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T14:53:38.599596Z"},"content_sha256":"edb1d9d11db1d935751b4e74ee6688af2e328d4b163105f77d7946915f201a85","schema_version":"1.0","event_id":"sha256:edb1d9d11db1d935751b4e74ee6688af2e328d4b163105f77d7946915f201a85"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:V62HDCA74ZR5FYVBECO3XYQMOY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Graph-Assisted Stitching for Offline Hierarchical Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.RO"],"primary_cat":"cs.LG","authors_text":"Jongchan Park, Seungho Baek, Seungjun Oh, Taegeon Park, Yusung Kim","submitted_at":"2025-06-09T13:26:23Z","abstract_excerpt":"Existing offline hierarchical reinforcement learning methods rely on high-level policy learning to generate subgoal sequences. However, their efficiency degrades as task horizons increase, and they lack effective strategies for stitching useful state transitions across different trajectories. We propose Graph-Assisted Stitching (GAS), a novel framework that formulates subgoal selection as a graph search problem rather than learning an explicit high-level policy. By embedding states into a Temporal Distance Representation (TDR) space, GAS clusters semantically similar states from different traj"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.07744","kind":"arxiv","version":3},"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/2506.07744/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":"2026-07-05T11:33:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2Wp1rQRZWIE5Txm4MFylji6dp0+Zch9umknFlS1CeTeLDe7qdFCEiRmHSPiktXo/uTUzODYAOiQTKUu06B+HAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T14:53:38.599961Z"},"content_sha256":"1162cefb98aeaa873a4f7a7a320b27dd612f3b08eafcaff9fa162d3dac26e33b","schema_version":"1.0","event_id":"sha256:1162cefb98aeaa873a4f7a7a320b27dd612f3b08eafcaff9fa162d3dac26e33b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/V62HDCA74ZR5FYVBECO3XYQMOY/bundle.json","state_url":"https://pith.science/pith/V62HDCA74ZR5FYVBECO3XYQMOY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/V62HDCA74ZR5FYVBECO3XYQMOY/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":"2026-07-07T14:53:38Z","links":{"resolver":"https://pith.science/pith/V62HDCA74ZR5FYVBECO3XYQMOY","bundle":"https://pith.science/pith/V62HDCA74ZR5FYVBECO3XYQMOY/bundle.json","state":"https://pith.science/pith/V62HDCA74ZR5FYVBECO3XYQMOY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/V62HDCA74ZR5FYVBECO3XYQMOY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:V62HDCA74ZR5FYVBECO3XYQMOY","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":"9f606fff486c98071612c2a48a4a9d45d52b84d50bb0859f822e6b2af39492c7","cross_cats_sorted":["cs.AI","cs.RO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-09T13:26:23Z","title_canon_sha256":"ae0b9ee26b47fc3da2d11d13c5f3cdf54671657b5d40fd4908868d42b4d671d0"},"schema_version":"1.0","source":{"id":"2506.07744","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.07744","created_at":"2026-07-05T11:33:05Z"},{"alias_kind":"arxiv_version","alias_value":"2506.07744v3","created_at":"2026-07-05T11:33:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.07744","created_at":"2026-07-05T11:33:05Z"},{"alias_kind":"pith_short_12","alias_value":"V62HDCA74ZR5","created_at":"2026-07-05T11:33:05Z"},{"alias_kind":"pith_short_16","alias_value":"V62HDCA74ZR5FYVB","created_at":"2026-07-05T11:33:05Z"},{"alias_kind":"pith_short_8","alias_value":"V62HDCA7","created_at":"2026-07-05T11:33:05Z"}],"graph_snapshots":[{"event_id":"sha256:1162cefb98aeaa873a4f7a7a320b27dd612f3b08eafcaff9fa162d3dac26e33b","target":"graph","created_at":"2026-07-05T11:33:05Z","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/2506.07744/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Existing offline hierarchical reinforcement learning methods rely on high-level policy learning to generate subgoal sequences. However, their efficiency degrades as task horizons increase, and they lack effective strategies for stitching useful state transitions across different trajectories. We propose Graph-Assisted Stitching (GAS), a novel framework that formulates subgoal selection as a graph search problem rather than learning an explicit high-level policy. By embedding states into a Temporal Distance Representation (TDR) space, GAS clusters semantically similar states from different traj","authors_text":"Jongchan Park, Seungho Baek, Seungjun Oh, Taegeon Park, Yusung Kim","cross_cats":["cs.AI","cs.RO"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-09T13:26:23Z","title":"Graph-Assisted Stitching for Offline Hierarchical Reinforcement Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.07744","kind":"arxiv","version":3},"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:edb1d9d11db1d935751b4e74ee6688af2e328d4b163105f77d7946915f201a85","target":"record","created_at":"2026-07-05T11:33:05Z","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":"9f606fff486c98071612c2a48a4a9d45d52b84d50bb0859f822e6b2af39492c7","cross_cats_sorted":["cs.AI","cs.RO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-09T13:26:23Z","title_canon_sha256":"ae0b9ee26b47fc3da2d11d13c5f3cdf54671657b5d40fd4908868d42b4d671d0"},"schema_version":"1.0","source":{"id":"2506.07744","kind":"arxiv","version":3}},"canonical_sha256":"afb471881fe663d2e2a1209dbbe20c7615ea92aa83fe103308d0643345f1c7a8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"afb471881fe663d2e2a1209dbbe20c7615ea92aa83fe103308d0643345f1c7a8","first_computed_at":"2026-07-05T11:33:05.429904Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:33:05.429904Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"i/vFipgbDhy3R/T994etV5jPkTK3CRO62cAeuCk3dPsWEiUZsl+0IAdUSHG7JTheP4jXToeQx31iHAC7Wxq6CQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:33:05.430414Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.07744","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:edb1d9d11db1d935751b4e74ee6688af2e328d4b163105f77d7946915f201a85","sha256:1162cefb98aeaa873a4f7a7a320b27dd612f3b08eafcaff9fa162d3dac26e33b"],"state_sha256":"7ad6e6865e8f9f8463521db2f550dd403e2e630ac9c741b93820716c2b275605"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bqrTROW5F9Ms3AQ0R5Ulfhnm6klwEx0zBa3TKI1A7jwM8Njy9FMGe34gxxv7kSAvIxsxk7Gts0RZHxKfUB6QCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T14:53:38.601835Z","bundle_sha256":"40b891f2bc6878937185ad32d9c719d5406b9868508ba8a3bfc716657b502f32"}}