{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:O7UXBD4NQYFGYZ5ZOS3UOBBHZY","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":"500dc674caa688c3cdaf1f8de9a9ee8d8d925e7541f8d389676bef44247c5089","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2019-10-15T09:19:55Z","title_canon_sha256":"9fc5b92882ef927e2d83fa65b0931fa3e803ad6ec2ae696a46ee88c56b6ea742"},"schema_version":"1.0","source":{"id":"1910.06611","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1910.06611","created_at":"2026-07-05T01:49:00Z"},{"alias_kind":"arxiv_version","alias_value":"1910.06611v2","created_at":"2026-07-05T01:49:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.06611","created_at":"2026-07-05T01:49:00Z"},{"alias_kind":"pith_short_12","alias_value":"O7UXBD4NQYFG","created_at":"2026-07-05T01:49:00Z"},{"alias_kind":"pith_short_16","alias_value":"O7UXBD4NQYFGYZ5Z","created_at":"2026-07-05T01:49:00Z"},{"alias_kind":"pith_short_8","alias_value":"O7UXBD4N","created_at":"2026-07-05T01:49:00Z"}],"graph_snapshots":[{"event_id":"sha256:8f0327247405fa31b156ab567c2ce482b7040dfc2c9596361372d412c2a96c05","target":"graph","created_at":"2026-07-05T01:49:00Z","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/1910.06611/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We incorporate Tensor-Product Representations within the Transformer in order to better support the explicit representation of relation structure. Our Tensor-Product Transformer (TP-Transformer) sets a new state of the art on the recently-introduced Mathematics Dataset containing 56 categories of free-form math word-problems. The essential component of the model is a novel attention mechanism, called TP-Attention, which explicitly encodes the relations between each Transformer cell and the other cells from which values have been retrieved by attention. TP-Attention goes beyond linear combinati","authors_text":"Imanol Schlag, Jianfeng Gao, J\\\"urgen Schmidhuber, Nebojsa Jojic, Paul Smolensky, Roland Fernandez","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2019-10-15T09:19:55Z","title":"Enhancing the Transformer with Explicit Relational Encoding for Math Problem Solving"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.06611","kind":"arxiv","version":2},"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":"sha256:6122f7f9c9df7b06ea0dba09e2b115dc460880774032d0de0fbf66873caac735","target":"record","created_at":"2026-07-05T01:49:00Z","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":"500dc674caa688c3cdaf1f8de9a9ee8d8d925e7541f8d389676bef44247c5089","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2019-10-15T09:19:55Z","title_canon_sha256":"9fc5b92882ef927e2d83fa65b0931fa3e803ad6ec2ae696a46ee88c56b6ea742"},"schema_version":"1.0","source":{"id":"1910.06611","kind":"arxiv","version":2}},"canonical_sha256":"77e9708f8d860a6c67b974b7470427ce183d6418f058a7ead361cc2f74b85bda","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"77e9708f8d860a6c67b974b7470427ce183d6418f058a7ead361cc2f74b85bda","first_computed_at":"2026-07-05T01:49:00.853696Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:49:00.853696Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9pDe+2t+tQJJu+7Ha/l3lxa7xJ7YEsDwcwyqVoRMRcahN+JkTurvdAmpaiFYBXelySxW5PzMI26H3OZ6oJrCAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T01:49:00.854142Z","signed_message":"canonical_sha256_bytes"},"source_id":"1910.06611","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6122f7f9c9df7b06ea0dba09e2b115dc460880774032d0de0fbf66873caac735","sha256:8f0327247405fa31b156ab567c2ce482b7040dfc2c9596361372d412c2a96c05"],"state_sha256":"6545bf2dbf0b64cc08b81166de99046d029b5c0306bb1c9d3ba65111b8d029bb"}