{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:OO73Y57B5IW24EZPTKUWMDKWYO","short_pith_number":"pith:OO73Y57B","canonical_record":{"source":{"id":"1506.04834","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2015-06-16T05:12:52Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"1c1fa167bbe3978fbb8f6f378a74c324df3a9c82f3ea891b0b0909d893f81b67","abstract_canon_sha256":"d945f0fc347d74cf07797ae441b7265cbf79e1f6003403186f1ef79b3ffc9e36"},"schema_version":"1.0"},"canonical_sha256":"73bfbc77e1ea2dae132f9aa9660d56c3aeb83882ff7ce6f452d431da84067d8b","source":{"kind":"arxiv","id":"1506.04834","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1506.04834","created_at":"2026-05-18T01:27:31Z"},{"alias_kind":"arxiv_version","alias_value":"1506.04834v3","created_at":"2026-05-18T01:27:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1506.04834","created_at":"2026-05-18T01:27:31Z"},{"alias_kind":"pith_short_12","alias_value":"OO73Y57B5IW2","created_at":"2026-05-18T12:29:34Z"},{"alias_kind":"pith_short_16","alias_value":"OO73Y57B5IW24EZP","created_at":"2026-05-18T12:29:34Z"},{"alias_kind":"pith_short_8","alias_value":"OO73Y57B","created_at":"2026-05-18T12:29:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:OO73Y57B5IW24EZPTKUWMDKWYO","target":"record","payload":{"canonical_record":{"source":{"id":"1506.04834","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2015-06-16T05:12:52Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"1c1fa167bbe3978fbb8f6f378a74c324df3a9c82f3ea891b0b0909d893f81b67","abstract_canon_sha256":"d945f0fc347d74cf07797ae441b7265cbf79e1f6003403186f1ef79b3ffc9e36"},"schema_version":"1.0"},"canonical_sha256":"73bfbc77e1ea2dae132f9aa9660d56c3aeb83882ff7ce6f452d431da84067d8b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:27:31.523768Z","signature_b64":"eA2LMaXUVtoHWXPbNRI9gUJh7Q7hqDB9tctu6htU1ougkyIKOBo3NZ3QlOrhfr7Am67wsr8U0WFbkYZ/v7sPCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"73bfbc77e1ea2dae132f9aa9660d56c3aeb83882ff7ce6f452d431da84067d8b","last_reissued_at":"2026-05-18T01:27:31.523111Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:27:31.523111Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1506.04834","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":"2026-05-18T01:27:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"svZTB28UbwwY7OjEOX2Eo+9KfLeXItfrK5uZvaLdsVLN89rm/EQ7QZKhM9t/HA7gkRNevx48iuujSfjwD5hFDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T19:49:23.376893Z"},"content_sha256":"691e2e9f2a56c30cd24bd2433f90e97fab55268aec49c8100e2625963e2df416","schema_version":"1.0","event_id":"sha256:691e2e9f2a56c30cd24bd2433f90e97fab55268aec49c8100e2625963e2df416"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:OO73Y57B5IW24EZPTKUWMDKWYO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Tree-structured composition in neural networks without tree-structured architectures","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Christopher D. Manning, Christopher Potts, Samuel R. Bowman","submitted_at":"2015-06-16T05:12:52Z","abstract_excerpt":"Tree-structured neural networks encode a particular tree geometry for a sentence in the network design. However, these models have at best only slightly outperformed simpler sequence-based models. We hypothesize that neural sequence models like LSTMs are in fact able to discover and implicitly use recursive compositional structure, at least for tasks with clear cues to that structure in the data. We demonstrate this possibility using an artificial data task for which recursive compositional structure is crucial, and find an LSTM-based sequence model can indeed learn to exploit the underlying t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1506.04834","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":"2026-05-18T01:27:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8sZUcuXASC+skUd6HJlklDCBrcNswRHBEt9pVCV2gn4xlEEGKakQ3ba+qN70pDl+vHxTzp8mvGdDMhw0k3n/CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T19:49:23.377246Z"},"content_sha256":"bfe2bab046b6110bdac9f42a53d3c97d5984a5dd438f062ae65544e442456742","schema_version":"1.0","event_id":"sha256:bfe2bab046b6110bdac9f42a53d3c97d5984a5dd438f062ae65544e442456742"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OO73Y57B5IW24EZPTKUWMDKWYO/bundle.json","state_url":"https://pith.science/pith/OO73Y57B5IW24EZPTKUWMDKWYO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OO73Y57B5IW24EZPTKUWMDKWYO/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":"2026-06-03T19:49:23Z","links":{"resolver":"https://pith.science/pith/OO73Y57B5IW24EZPTKUWMDKWYO","bundle":"https://pith.science/pith/OO73Y57B5IW24EZPTKUWMDKWYO/bundle.json","state":"https://pith.science/pith/OO73Y57B5IW24EZPTKUWMDKWYO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OO73Y57B5IW24EZPTKUWMDKWYO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:OO73Y57B5IW24EZPTKUWMDKWYO","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":"d945f0fc347d74cf07797ae441b7265cbf79e1f6003403186f1ef79b3ffc9e36","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2015-06-16T05:12:52Z","title_canon_sha256":"1c1fa167bbe3978fbb8f6f378a74c324df3a9c82f3ea891b0b0909d893f81b67"},"schema_version":"1.0","source":{"id":"1506.04834","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1506.04834","created_at":"2026-05-18T01:27:31Z"},{"alias_kind":"arxiv_version","alias_value":"1506.04834v3","created_at":"2026-05-18T01:27:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1506.04834","created_at":"2026-05-18T01:27:31Z"},{"alias_kind":"pith_short_12","alias_value":"OO73Y57B5IW2","created_at":"2026-05-18T12:29:34Z"},{"alias_kind":"pith_short_16","alias_value":"OO73Y57B5IW24EZP","created_at":"2026-05-18T12:29:34Z"},{"alias_kind":"pith_short_8","alias_value":"OO73Y57B","created_at":"2026-05-18T12:29:34Z"}],"graph_snapshots":[{"event_id":"sha256:bfe2bab046b6110bdac9f42a53d3c97d5984a5dd438f062ae65544e442456742","target":"graph","created_at":"2026-05-18T01:27:31Z","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":"Tree-structured neural networks encode a particular tree geometry for a sentence in the network design. However, these models have at best only slightly outperformed simpler sequence-based models. We hypothesize that neural sequence models like LSTMs are in fact able to discover and implicitly use recursive compositional structure, at least for tasks with clear cues to that structure in the data. We demonstrate this possibility using an artificial data task for which recursive compositional structure is crucial, and find an LSTM-based sequence model can indeed learn to exploit the underlying t","authors_text":"Christopher D. Manning, Christopher Potts, Samuel R. Bowman","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2015-06-16T05:12:52Z","title":"Tree-structured composition in neural networks without tree-structured architectures"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1506.04834","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":"sha256:691e2e9f2a56c30cd24bd2433f90e97fab55268aec49c8100e2625963e2df416","target":"record","created_at":"2026-05-18T01:27:31Z","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":"d945f0fc347d74cf07797ae441b7265cbf79e1f6003403186f1ef79b3ffc9e36","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2015-06-16T05:12:52Z","title_canon_sha256":"1c1fa167bbe3978fbb8f6f378a74c324df3a9c82f3ea891b0b0909d893f81b67"},"schema_version":"1.0","source":{"id":"1506.04834","kind":"arxiv","version":3}},"canonical_sha256":"73bfbc77e1ea2dae132f9aa9660d56c3aeb83882ff7ce6f452d431da84067d8b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"73bfbc77e1ea2dae132f9aa9660d56c3aeb83882ff7ce6f452d431da84067d8b","first_computed_at":"2026-05-18T01:27:31.523111Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:27:31.523111Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"eA2LMaXUVtoHWXPbNRI9gUJh7Q7hqDB9tctu6htU1ougkyIKOBo3NZ3QlOrhfr7Am67wsr8U0WFbkYZ/v7sPCA==","signature_status":"signed_v1","signed_at":"2026-05-18T01:27:31.523768Z","signed_message":"canonical_sha256_bytes"},"source_id":"1506.04834","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:691e2e9f2a56c30cd24bd2433f90e97fab55268aec49c8100e2625963e2df416","sha256:bfe2bab046b6110bdac9f42a53d3c97d5984a5dd438f062ae65544e442456742"],"state_sha256":"6e6bffed436de84ed04e8cd03994db109b558ca9869593c38103a26d44fe1d09"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8xVh3omza6i5FEGnurt/gJZYc8t7EkcjRFfqU0bacjt2i0sbPpuOASvfKa4SaxSM58ftQWMSKKzqhUMKuAJHBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T19:49:23.379475Z","bundle_sha256":"4fb07c3587f13aab53c8d9737be7ab819d92b8ad0d6a55f1b7f1db0913bc99cf"}}