{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:FAMSNAMZVX4AYB66FTUBWA33H7","short_pith_number":"pith:FAMSNAMZ","canonical_record":{"source":{"id":"2211.16022","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-11-29T08:44:09Z","cross_cats_sorted":[],"title_canon_sha256":"0bed74d8e12366e326df76e93d8e1ee83fdc6b0c3feda1534f00b6d70777dbb4","abstract_canon_sha256":"b931b44c86e03a2539a94a564d083c83aafde42e2aa30f532474c3589219b83b"},"schema_version":"1.0"},"canonical_sha256":"2819268199adf80c07de2ce81b037b3fdd7febb632e586069bd69242b455ceb8","source":{"kind":"arxiv","id":"2211.16022","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.16022","created_at":"2026-07-05T05:20:22Z"},{"alias_kind":"arxiv_version","alias_value":"2211.16022v1","created_at":"2026-07-05T05:20:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.16022","created_at":"2026-07-05T05:20:22Z"},{"alias_kind":"pith_short_12","alias_value":"FAMSNAMZVX4A","created_at":"2026-07-05T05:20:22Z"},{"alias_kind":"pith_short_16","alias_value":"FAMSNAMZVX4AYB66","created_at":"2026-07-05T05:20:22Z"},{"alias_kind":"pith_short_8","alias_value":"FAMSNAMZ","created_at":"2026-07-05T05:20:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:FAMSNAMZVX4AYB66FTUBWA33H7","target":"record","payload":{"canonical_record":{"source":{"id":"2211.16022","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-11-29T08:44:09Z","cross_cats_sorted":[],"title_canon_sha256":"0bed74d8e12366e326df76e93d8e1ee83fdc6b0c3feda1534f00b6d70777dbb4","abstract_canon_sha256":"b931b44c86e03a2539a94a564d083c83aafde42e2aa30f532474c3589219b83b"},"schema_version":"1.0"},"canonical_sha256":"2819268199adf80c07de2ce81b037b3fdd7febb632e586069bd69242b455ceb8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:20:22.553934Z","signature_b64":"+WXenJIocJvxpiSaSlT2gND+5abSwvYtFAdSKF+bNcOnpbw/O1jWubRyU8k7sCgZVbUN0iBTdExB382FCW5MAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2819268199adf80c07de2ce81b037b3fdd7febb632e586069bd69242b455ceb8","last_reissued_at":"2026-07-05T05:20:22.553513Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:20:22.553513Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2211.16022","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:20:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rePeXhzAehR9qflxUQuzXHLK7TEaGgwHddV3Q84jXAEwPsnroH+KFw6zMAwOv8GQ5Hof6CKTh2Ps0nTPcEA9Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T03:11:46.672252Z"},"content_sha256":"9a9c4b2301d866c98d709ae973af70b26d16b8c2282dfee6fc3c922dbbb3f3f8","schema_version":"1.0","event_id":"sha256:9a9c4b2301d866c98d709ae973af70b26d16b8c2282dfee6fc3c922dbbb3f3f8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:FAMSNAMZVX4AYB66FTUBWA33H7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Textual Enhanced Contrastive Learning for Solving Math Word Problems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Fei Cheng, Qianying Liu, Sadao Kurohashi, Yibin Shen, Zhuoyuan Mao","submitted_at":"2022-11-29T08:44:09Z","abstract_excerpt":"Solving math word problems is the task that analyses the relation of quantities and requires an accurate understanding of contextual natural language information. Recent studies show that current models rely on shallow heuristics to predict solutions and could be easily misled by small textual perturbations. To address this problem, we propose a Textual Enhanced Contrastive Learning framework, which enforces the models to distinguish semantically similar examples while holding different mathematical logic. We adopt a self-supervised manner strategy to enrich examples with subtle textual varian"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.16022","kind":"arxiv","version":1},"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/2211.16022/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":"2026-07-05T05:20:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FVK2zo6b+uL9D7QhpPKfRvYWWNJp1SfXYhsl0ZrishzryuDAo3+C1Bv2OYsXL+P/3Gtso+nfEs3gnCWrNjhKBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T03:11:46.672636Z"},"content_sha256":"b9d07d80b1f65f4bd8c1cabcca9cf673d95fe5750c63d007f4cec7ca99911753","schema_version":"1.0","event_id":"sha256:b9d07d80b1f65f4bd8c1cabcca9cf673d95fe5750c63d007f4cec7ca99911753"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FAMSNAMZVX4AYB66FTUBWA33H7/bundle.json","state_url":"https://pith.science/pith/FAMSNAMZVX4AYB66FTUBWA33H7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FAMSNAMZVX4AYB66FTUBWA33H7/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":"2026-07-09T03:11:46Z","links":{"resolver":"https://pith.science/pith/FAMSNAMZVX4AYB66FTUBWA33H7","bundle":"https://pith.science/pith/FAMSNAMZVX4AYB66FTUBWA33H7/bundle.json","state":"https://pith.science/pith/FAMSNAMZVX4AYB66FTUBWA33H7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FAMSNAMZVX4AYB66FTUBWA33H7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:FAMSNAMZVX4AYB66FTUBWA33H7","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":"b931b44c86e03a2539a94a564d083c83aafde42e2aa30f532474c3589219b83b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-11-29T08:44:09Z","title_canon_sha256":"0bed74d8e12366e326df76e93d8e1ee83fdc6b0c3feda1534f00b6d70777dbb4"},"schema_version":"1.0","source":{"id":"2211.16022","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.16022","created_at":"2026-07-05T05:20:22Z"},{"alias_kind":"arxiv_version","alias_value":"2211.16022v1","created_at":"2026-07-05T05:20:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.16022","created_at":"2026-07-05T05:20:22Z"},{"alias_kind":"pith_short_12","alias_value":"FAMSNAMZVX4A","created_at":"2026-07-05T05:20:22Z"},{"alias_kind":"pith_short_16","alias_value":"FAMSNAMZVX4AYB66","created_at":"2026-07-05T05:20:22Z"},{"alias_kind":"pith_short_8","alias_value":"FAMSNAMZ","created_at":"2026-07-05T05:20:22Z"}],"graph_snapshots":[{"event_id":"sha256:b9d07d80b1f65f4bd8c1cabcca9cf673d95fe5750c63d007f4cec7ca99911753","target":"graph","created_at":"2026-07-05T05:20:22Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2211.16022/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Solving math word problems is the task that analyses the relation of quantities and requires an accurate understanding of contextual natural language information. Recent studies show that current models rely on shallow heuristics to predict solutions and could be easily misled by small textual perturbations. To address this problem, we propose a Textual Enhanced Contrastive Learning framework, which enforces the models to distinguish semantically similar examples while holding different mathematical logic. We adopt a self-supervised manner strategy to enrich examples with subtle textual varian","authors_text":"Fei Cheng, Qianying Liu, Sadao Kurohashi, Yibin Shen, Zhuoyuan Mao","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-11-29T08:44:09Z","title":"Textual Enhanced Contrastive Learning for Solving Math Word Problems"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.16022","kind":"arxiv","version":1},"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":"sha256:9a9c4b2301d866c98d709ae973af70b26d16b8c2282dfee6fc3c922dbbb3f3f8","target":"record","created_at":"2026-07-05T05:20:22Z","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":"b931b44c86e03a2539a94a564d083c83aafde42e2aa30f532474c3589219b83b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-11-29T08:44:09Z","title_canon_sha256":"0bed74d8e12366e326df76e93d8e1ee83fdc6b0c3feda1534f00b6d70777dbb4"},"schema_version":"1.0","source":{"id":"2211.16022","kind":"arxiv","version":1}},"canonical_sha256":"2819268199adf80c07de2ce81b037b3fdd7febb632e586069bd69242b455ceb8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2819268199adf80c07de2ce81b037b3fdd7febb632e586069bd69242b455ceb8","first_computed_at":"2026-07-05T05:20:22.553513Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:20:22.553513Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+WXenJIocJvxpiSaSlT2gND+5abSwvYtFAdSKF+bNcOnpbw/O1jWubRyU8k7sCgZVbUN0iBTdExB382FCW5MAA==","signature_status":"signed_v1","signed_at":"2026-07-05T05:20:22.553934Z","signed_message":"canonical_sha256_bytes"},"source_id":"2211.16022","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9a9c4b2301d866c98d709ae973af70b26d16b8c2282dfee6fc3c922dbbb3f3f8","sha256:b9d07d80b1f65f4bd8c1cabcca9cf673d95fe5750c63d007f4cec7ca99911753"],"state_sha256":"ea3fd93f4dc3d4d6318ff80539e921b2be070bab07723544b7add018b34d36dd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Maf1GYoOI/7wdtDzCfyIWvLwKhXuXkvXc10CEQl92mdzYHEQwhXxKdYWkF6RB+e25yDtOAzE3fIfDbTdn9/lCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-09T03:11:46.674596Z","bundle_sha256":"1b5dedd5c768c4ca381a8c612c6780474d9a737c46fdfbcf20db20355821cb52"}}