{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:L67T5D5SJSVH24JRKMYTCIJQBK","short_pith_number":"pith:L67T5D5S","canonical_record":{"source":{"id":"1707.07847","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2017-07-25T08:21:30Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"51d4a5bd3a99f8fe291e7c877743d3cad06c66f8d462496fad25fcddf7dd6eae","abstract_canon_sha256":"7abb0e66e878f1941711c57c328cc1708d479ffe576ec8afbdddf1d349931458"},"schema_version":"1.0"},"canonical_sha256":"5fbf3e8fb24caa7d713153313121300a988ce090e4adf467732a6d9d341aedef","source":{"kind":"arxiv","id":"1707.07847","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1707.07847","created_at":"2026-05-18T00:29:45Z"},{"alias_kind":"arxiv_version","alias_value":"1707.07847v3","created_at":"2026-05-18T00:29:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.07847","created_at":"2026-05-18T00:29:45Z"},{"alias_kind":"pith_short_12","alias_value":"L67T5D5SJSVH","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_16","alias_value":"L67T5D5SJSVH24JR","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_8","alias_value":"L67T5D5S","created_at":"2026-05-18T12:31:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:L67T5D5SJSVH24JRKMYTCIJQBK","target":"record","payload":{"canonical_record":{"source":{"id":"1707.07847","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2017-07-25T08:21:30Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"51d4a5bd3a99f8fe291e7c877743d3cad06c66f8d462496fad25fcddf7dd6eae","abstract_canon_sha256":"7abb0e66e878f1941711c57c328cc1708d479ffe576ec8afbdddf1d349931458"},"schema_version":"1.0"},"canonical_sha256":"5fbf3e8fb24caa7d713153313121300a988ce090e4adf467732a6d9d341aedef","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:29:45.698613Z","signature_b64":"orI1s1mfXUIlvmbOwqscSj8C5gXdKSXh9Q05wRrvtrfqg+5rHDLJUNsFLopttxRWR49exJdEUY7aqS7meLy8DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5fbf3e8fb24caa7d713153313121300a988ce090e4adf467732a6d9d341aedef","last_reissued_at":"2026-05-18T00:29:45.697973Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:29:45.697973Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1707.07847","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":"2026-05-18T00:29:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dFNnIEOyyUurzk3XmHzV5+OvfyLmswFBry+PjGDD4YO/Nu3XPfOpeVe6cHo1J7cTman3sX9GT/AcHV+PHtUEAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T17:38:55.940021Z"},"content_sha256":"329f27af38c7c285e2fe20de4e1eedcb096d9d2bbfea5fdd6bc14739e1fad052","schema_version":"1.0","event_id":"sha256:329f27af38c7c285e2fe20de4e1eedcb096d9d2bbfea5fdd6bc14739e1fad052"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:L67T5D5SJSVH24JRKMYTCIJQBK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Hyperbolic Representation Learning for Fast and Efficient Neural Question Answering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.IR","authors_text":"Luu Anh Tuan, Siu Cheung Hui, Yi Tay","submitted_at":"2017-07-25T08:21:30Z","abstract_excerpt":"The dominant neural architectures in question answer retrieval are based on recurrent or convolutional encoders configured with complex word matching layers. Given that recent architectural innovations are mostly new word interaction layers or attention-based matching mechanisms, it seems to be a well-established fact that these components are mandatory for good performance. Unfortunately, the memory and computation cost incurred by these complex mechanisms are undesirable for practical applications. As such, this paper tackles the question of whether it is possible to achieve competitive perf"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.07847","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":""},"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":"2026-05-18T00:29:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cujDFWqDDnYlZu3W4lHp5qJoZ7ZETZm4qhTuwwQhRj/f6FoQIitZJKmW7MayFIE2Q/lyiYcJXBo467o7YaRsDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T17:38:55.940776Z"},"content_sha256":"07a19074970710ad26c559425112b359a30a3c16e93ee54fa392b8acaa8fbda6","schema_version":"1.0","event_id":"sha256:07a19074970710ad26c559425112b359a30a3c16e93ee54fa392b8acaa8fbda6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/L67T5D5SJSVH24JRKMYTCIJQBK/bundle.json","state_url":"https://pith.science/pith/L67T5D5SJSVH24JRKMYTCIJQBK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/L67T5D5SJSVH24JRKMYTCIJQBK/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":"2026-05-25T17:38:55Z","links":{"resolver":"https://pith.science/pith/L67T5D5SJSVH24JRKMYTCIJQBK","bundle":"https://pith.science/pith/L67T5D5SJSVH24JRKMYTCIJQBK/bundle.json","state":"https://pith.science/pith/L67T5D5SJSVH24JRKMYTCIJQBK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/L67T5D5SJSVH24JRKMYTCIJQBK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:L67T5D5SJSVH24JRKMYTCIJQBK","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":"7abb0e66e878f1941711c57c328cc1708d479ffe576ec8afbdddf1d349931458","cross_cats_sorted":["cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2017-07-25T08:21:30Z","title_canon_sha256":"51d4a5bd3a99f8fe291e7c877743d3cad06c66f8d462496fad25fcddf7dd6eae"},"schema_version":"1.0","source":{"id":"1707.07847","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1707.07847","created_at":"2026-05-18T00:29:45Z"},{"alias_kind":"arxiv_version","alias_value":"1707.07847v3","created_at":"2026-05-18T00:29:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.07847","created_at":"2026-05-18T00:29:45Z"},{"alias_kind":"pith_short_12","alias_value":"L67T5D5SJSVH","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_16","alias_value":"L67T5D5SJSVH24JR","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_8","alias_value":"L67T5D5S","created_at":"2026-05-18T12:31:28Z"}],"graph_snapshots":[{"event_id":"sha256:07a19074970710ad26c559425112b359a30a3c16e93ee54fa392b8acaa8fbda6","target":"graph","created_at":"2026-05-18T00:29:45Z","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 dominant neural architectures in question answer retrieval are based on recurrent or convolutional encoders configured with complex word matching layers. Given that recent architectural innovations are mostly new word interaction layers or attention-based matching mechanisms, it seems to be a well-established fact that these components are mandatory for good performance. Unfortunately, the memory and computation cost incurred by these complex mechanisms are undesirable for practical applications. As such, this paper tackles the question of whether it is possible to achieve competitive perf","authors_text":"Luu Anh Tuan, Siu Cheung Hui, Yi Tay","cross_cats":["cs.CL"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2017-07-25T08:21:30Z","title":"Hyperbolic Representation Learning for Fast and Efficient Neural Question Answering"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.07847","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":"sha256:329f27af38c7c285e2fe20de4e1eedcb096d9d2bbfea5fdd6bc14739e1fad052","target":"record","created_at":"2026-05-18T00:29:45Z","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":"7abb0e66e878f1941711c57c328cc1708d479ffe576ec8afbdddf1d349931458","cross_cats_sorted":["cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2017-07-25T08:21:30Z","title_canon_sha256":"51d4a5bd3a99f8fe291e7c877743d3cad06c66f8d462496fad25fcddf7dd6eae"},"schema_version":"1.0","source":{"id":"1707.07847","kind":"arxiv","version":3}},"canonical_sha256":"5fbf3e8fb24caa7d713153313121300a988ce090e4adf467732a6d9d341aedef","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5fbf3e8fb24caa7d713153313121300a988ce090e4adf467732a6d9d341aedef","first_computed_at":"2026-05-18T00:29:45.697973Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:29:45.697973Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"orI1s1mfXUIlvmbOwqscSj8C5gXdKSXh9Q05wRrvtrfqg+5rHDLJUNsFLopttxRWR49exJdEUY7aqS7meLy8DQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:29:45.698613Z","signed_message":"canonical_sha256_bytes"},"source_id":"1707.07847","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:329f27af38c7c285e2fe20de4e1eedcb096d9d2bbfea5fdd6bc14739e1fad052","sha256:07a19074970710ad26c559425112b359a30a3c16e93ee54fa392b8acaa8fbda6"],"state_sha256":"723b2a71d348919d52c4a759e131578bdbb6bb0ceb3923fc11d2d8875eb82990"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TL9NupFooiaCYzQ/M/L8sYLUElcuJP7eP29Dv0VL5XVIQ5sdj5yWGZL0iWAwQtcF/eUQ8SYxNMffcMau8VANDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T17:38:55.944818Z","bundle_sha256":"c8c7cde326371cd42e480701b978fd474f97859e9ad5ea36efd15a89a0fdd352"}}