{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:QAQWGKROG4UMUXIHQ6WE7ZLEC7","short_pith_number":"pith:QAQWGKRO","canonical_record":{"source":{"id":"1804.00823","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2018-04-03T04:47:22Z","cross_cats_sorted":["cs.CL","cs.LG","stat.ML"],"title_canon_sha256":"f7cd8ab4ff11c335df9726f68cb63c29af67ea19a4e2c3330f81278a3b0815f4","abstract_canon_sha256":"734095fc1b266355ac97c8579a5f1b7adc3cf1f91ca9e8c3c197abe5d4d60ada"},"schema_version":"1.0"},"canonical_sha256":"8021632a2e3728ca5d0787ac4fe56417f1c2bf6d9d4d9d4ff52535848d652396","source":{"kind":"arxiv","id":"1804.00823","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1804.00823","created_at":"2026-05-17T23:59:22Z"},{"alias_kind":"arxiv_version","alias_value":"1804.00823v4","created_at":"2026-05-17T23:59:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.00823","created_at":"2026-05-17T23:59:22Z"},{"alias_kind":"pith_short_12","alias_value":"QAQWGKROG4UM","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_16","alias_value":"QAQWGKROG4UMUXIH","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_8","alias_value":"QAQWGKRO","created_at":"2026-05-18T12:32:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:QAQWGKROG4UMUXIHQ6WE7ZLEC7","target":"record","payload":{"canonical_record":{"source":{"id":"1804.00823","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2018-04-03T04:47:22Z","cross_cats_sorted":["cs.CL","cs.LG","stat.ML"],"title_canon_sha256":"f7cd8ab4ff11c335df9726f68cb63c29af67ea19a4e2c3330f81278a3b0815f4","abstract_canon_sha256":"734095fc1b266355ac97c8579a5f1b7adc3cf1f91ca9e8c3c197abe5d4d60ada"},"schema_version":"1.0"},"canonical_sha256":"8021632a2e3728ca5d0787ac4fe56417f1c2bf6d9d4d9d4ff52535848d652396","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:59:22.837845Z","signature_b64":"6/BfMBt4OudLh0mc3RKcRgu1voFxsuQVJ4/KYSJpIhwwu4cgZvbSSVnD5nOk8QCXMAciSqhuJwtvXcFO5OC1CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8021632a2e3728ca5d0787ac4fe56417f1c2bf6d9d4d9d4ff52535848d652396","last_reissued_at":"2026-05-17T23:59:22.837479Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:59:22.837479Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1804.00823","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:59:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KOYdsAWsyzPzcfTAM2RPKFbVF/odUPkcDmRpc1kDHOs2V/EoD+04OHSdyU5EHrjrWxsVyCNwNr8ChE06YX9XCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T16:06:25.410503Z"},"content_sha256":"49143247ac1976e6bc4fd05b74803785f88ef011cb8ce2b79114fb20c3e3ff54","schema_version":"1.0","event_id":"sha256:49143247ac1976e6bc4fd05b74803785f88ef011cb8ce2b79114fb20c3e3ff54"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:QAQWGKROG4UMUXIHQ6WE7ZLEC7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG","stat.ML"],"primary_cat":"cs.AI","authors_text":"Kun Xu, Lingfei Wu, Michael Witbrock, Vadim Sheinin, Yansong Feng, Zhiguo Wang","submitted_at":"2018-04-03T04:47:22Z","abstract_excerpt":"The celebrated Sequence to Sequence learning (Seq2Seq) technique and its numerous variants achieve excellent performance on many tasks. However, many machine learning tasks have inputs naturally represented as graphs; existing Seq2Seq models face a significant challenge in achieving accurate conversion from graph form to the appropriate sequence. To address this challenge, we introduce a novel general end-to-end graph-to-sequence neural encoder-decoder model that maps an input graph to a sequence of vectors and uses an attention-based LSTM method to decode the target sequence from these vector"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.00823","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"2026-05-17T23:59:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VRL1F7sVJ/k8JWrd9xqWmN0EqBtAYIac6zVxKH7ZcF9dAzvfuBrfCFGlhPdf3JP/PNgASYzp3Yo4JGO15YhaAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T16:06:25.411163Z"},"content_sha256":"1fea1fe7452b62a0d2d7aeddac9f4cb8dc2839dbf8a01cb4a9d85b5d56fde024","schema_version":"1.0","event_id":"sha256:1fea1fe7452b62a0d2d7aeddac9f4cb8dc2839dbf8a01cb4a9d85b5d56fde024"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QAQWGKROG4UMUXIHQ6WE7ZLEC7/bundle.json","state_url":"https://pith.science/pith/QAQWGKROG4UMUXIHQ6WE7ZLEC7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QAQWGKROG4UMUXIHQ6WE7ZLEC7/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":"2026-05-28T16:06:25Z","links":{"resolver":"https://pith.science/pith/QAQWGKROG4UMUXIHQ6WE7ZLEC7","bundle":"https://pith.science/pith/QAQWGKROG4UMUXIHQ6WE7ZLEC7/bundle.json","state":"https://pith.science/pith/QAQWGKROG4UMUXIHQ6WE7ZLEC7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QAQWGKROG4UMUXIHQ6WE7ZLEC7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:QAQWGKROG4UMUXIHQ6WE7ZLEC7","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":"734095fc1b266355ac97c8579a5f1b7adc3cf1f91ca9e8c3c197abe5d4d60ada","cross_cats_sorted":["cs.CL","cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2018-04-03T04:47:22Z","title_canon_sha256":"f7cd8ab4ff11c335df9726f68cb63c29af67ea19a4e2c3330f81278a3b0815f4"},"schema_version":"1.0","source":{"id":"1804.00823","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1804.00823","created_at":"2026-05-17T23:59:22Z"},{"alias_kind":"arxiv_version","alias_value":"1804.00823v4","created_at":"2026-05-17T23:59:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.00823","created_at":"2026-05-17T23:59:22Z"},{"alias_kind":"pith_short_12","alias_value":"QAQWGKROG4UM","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_16","alias_value":"QAQWGKROG4UMUXIH","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_8","alias_value":"QAQWGKRO","created_at":"2026-05-18T12:32:46Z"}],"graph_snapshots":[{"event_id":"sha256:1fea1fe7452b62a0d2d7aeddac9f4cb8dc2839dbf8a01cb4a9d85b5d56fde024","target":"graph","created_at":"2026-05-17T23:59:22Z","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"},"paper":{"abstract_excerpt":"The celebrated Sequence to Sequence learning (Seq2Seq) technique and its numerous variants achieve excellent performance on many tasks. However, many machine learning tasks have inputs naturally represented as graphs; existing Seq2Seq models face a significant challenge in achieving accurate conversion from graph form to the appropriate sequence. To address this challenge, we introduce a novel general end-to-end graph-to-sequence neural encoder-decoder model that maps an input graph to a sequence of vectors and uses an attention-based LSTM method to decode the target sequence from these vector","authors_text":"Kun Xu, Lingfei Wu, Michael Witbrock, Vadim Sheinin, Yansong Feng, Zhiguo Wang","cross_cats":["cs.CL","cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2018-04-03T04:47:22Z","title":"Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.00823","kind":"arxiv","version":4},"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":"sha256:49143247ac1976e6bc4fd05b74803785f88ef011cb8ce2b79114fb20c3e3ff54","target":"record","created_at":"2026-05-17T23:59:22Z","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":"734095fc1b266355ac97c8579a5f1b7adc3cf1f91ca9e8c3c197abe5d4d60ada","cross_cats_sorted":["cs.CL","cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2018-04-03T04:47:22Z","title_canon_sha256":"f7cd8ab4ff11c335df9726f68cb63c29af67ea19a4e2c3330f81278a3b0815f4"},"schema_version":"1.0","source":{"id":"1804.00823","kind":"arxiv","version":4}},"canonical_sha256":"8021632a2e3728ca5d0787ac4fe56417f1c2bf6d9d4d9d4ff52535848d652396","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8021632a2e3728ca5d0787ac4fe56417f1c2bf6d9d4d9d4ff52535848d652396","first_computed_at":"2026-05-17T23:59:22.837479Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:59:22.837479Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6/BfMBt4OudLh0mc3RKcRgu1voFxsuQVJ4/KYSJpIhwwu4cgZvbSSVnD5nOk8QCXMAciSqhuJwtvXcFO5OC1CA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:59:22.837845Z","signed_message":"canonical_sha256_bytes"},"source_id":"1804.00823","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:49143247ac1976e6bc4fd05b74803785f88ef011cb8ce2b79114fb20c3e3ff54","sha256:1fea1fe7452b62a0d2d7aeddac9f4cb8dc2839dbf8a01cb4a9d85b5d56fde024"],"state_sha256":"9be59d4e8699aa0c42ea51a662a134bd44b23d6e85533e3c4ab1d4f481f90035"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tyrm8gE9HIDTkmkOaDjXT06tPaxX/H2YpQATMsgrlWhFdo/LGyfC2l6580BVN1r5EZoib0B3UDIq5y3AzXLPDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T16:06:25.414292Z","bundle_sha256":"bdd732bf7f6e374d1c66068583f816c8ea08e6823c57cb14bb07bbefb9c5d541"}}