{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:EQ5TETSEG6MIKKVS4NE35VV4LD","short_pith_number":"pith:EQ5TETSE","canonical_record":{"source":{"id":"2311.07996","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-11-14T08:51:00Z","cross_cats_sorted":[],"title_canon_sha256":"23361fca58d1f25d0b3e51ce4e752ea76cbacde58d5cc3077fc74886ca9261bb","abstract_canon_sha256":"7bde3d81d934ff79e786ac9a34a0d31c0d49eee01d3a4e430207bfebb157786d"},"schema_version":"1.0"},"canonical_sha256":"243b324e443798852ab2e349bed6bc58f965b01313fc06db7d5d367370140dfd","source":{"kind":"arxiv","id":"2311.07996","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.07996","created_at":"2026-07-05T07:12:26Z"},{"alias_kind":"arxiv_version","alias_value":"2311.07996v1","created_at":"2026-07-05T07:12:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.07996","created_at":"2026-07-05T07:12:26Z"},{"alias_kind":"pith_short_12","alias_value":"EQ5TETSEG6MI","created_at":"2026-07-05T07:12:26Z"},{"alias_kind":"pith_short_16","alias_value":"EQ5TETSEG6MIKKVS","created_at":"2026-07-05T07:12:26Z"},{"alias_kind":"pith_short_8","alias_value":"EQ5TETSE","created_at":"2026-07-05T07:12:26Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:EQ5TETSEG6MIKKVS4NE35VV4LD","target":"record","payload":{"canonical_record":{"source":{"id":"2311.07996","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-11-14T08:51:00Z","cross_cats_sorted":[],"title_canon_sha256":"23361fca58d1f25d0b3e51ce4e752ea76cbacde58d5cc3077fc74886ca9261bb","abstract_canon_sha256":"7bde3d81d934ff79e786ac9a34a0d31c0d49eee01d3a4e430207bfebb157786d"},"schema_version":"1.0"},"canonical_sha256":"243b324e443798852ab2e349bed6bc58f965b01313fc06db7d5d367370140dfd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:12:26.916549Z","signature_b64":"SAkAvHRGvOoUA6tTeEo2fnYwNMfg1n6R/66aDqj33jGK++yp0eKjIQS1kTEPtqCqgdIvGAKfN7iq5pT7hUuFCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"243b324e443798852ab2e349bed6bc58f965b01313fc06db7d5d367370140dfd","last_reissued_at":"2026-07-05T07:12:26.916152Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:12:26.916152Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2311.07996","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":"2026-07-05T07:12:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JcjtAoMT573w94pfrODO4hFmdN2yaRcQnnJVajOt5d5FEc0KDT4clzNpFL1uLd4Nkh6BMakBAeKn3dhq01JpDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T12:06:36.730303Z"},"content_sha256":"273a4614648e10e9dd5951a8cad32b1eabcec84b3f85c89998c89fc74b088ac4","schema_version":"1.0","event_id":"sha256:273a4614648e10e9dd5951a8cad32b1eabcec84b3f85c89998c89fc74b088ac4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:EQ5TETSEG6MIKKVS4NE35VV4LD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"How Well Do Text Embedding Models Understand Syntax?","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Haizhou Li, Yan Zhang, Zhaopeng Feng, Zhiyang Teng, Zuozhu Liu","submitted_at":"2023-11-14T08:51:00Z","abstract_excerpt":"Text embedding models have significantly contributed to advancements in natural language processing by adeptly capturing semantic properties of textual data. However, the ability of these models to generalize across a wide range of syntactic contexts remains under-explored. In this paper, we first develop an evaluation set, named \\textbf{SR}, to scrutinize the capability for syntax understanding of text embedding models from two crucial syntactic aspects: Structural heuristics, and Relational understanding among concepts, as revealed by the performance gaps in previous studies. Our findings re"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.07996","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/2311.07996/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":"2026-07-05T07:12:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eOMCEBAsnyxJZOOmNIHgG/AC9wT34KVNckt+jPTcLax8+Dt6LhIx1q/u+EUqjDtAlPHsAirutbhtItfgo/cQDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T12:06:36.730852Z"},"content_sha256":"312db576a853a2d69152a5eaca931977e695514f14430f4fbd4bc1c15ce52089","schema_version":"1.0","event_id":"sha256:312db576a853a2d69152a5eaca931977e695514f14430f4fbd4bc1c15ce52089"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EQ5TETSEG6MIKKVS4NE35VV4LD/bundle.json","state_url":"https://pith.science/pith/EQ5TETSEG6MIKKVS4NE35VV4LD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EQ5TETSEG6MIKKVS4NE35VV4LD/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":"2026-08-14T12:06:36Z","links":{"resolver":"https://pith.science/pith/EQ5TETSEG6MIKKVS4NE35VV4LD","bundle":"https://pith.science/pith/EQ5TETSEG6MIKKVS4NE35VV4LD/bundle.json","state":"https://pith.science/pith/EQ5TETSEG6MIKKVS4NE35VV4LD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EQ5TETSEG6MIKKVS4NE35VV4LD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:EQ5TETSEG6MIKKVS4NE35VV4LD","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":"7bde3d81d934ff79e786ac9a34a0d31c0d49eee01d3a4e430207bfebb157786d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-11-14T08:51:00Z","title_canon_sha256":"23361fca58d1f25d0b3e51ce4e752ea76cbacde58d5cc3077fc74886ca9261bb"},"schema_version":"1.0","source":{"id":"2311.07996","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.07996","created_at":"2026-07-05T07:12:26Z"},{"alias_kind":"arxiv_version","alias_value":"2311.07996v1","created_at":"2026-07-05T07:12:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.07996","created_at":"2026-07-05T07:12:26Z"},{"alias_kind":"pith_short_12","alias_value":"EQ5TETSEG6MI","created_at":"2026-07-05T07:12:26Z"},{"alias_kind":"pith_short_16","alias_value":"EQ5TETSEG6MIKKVS","created_at":"2026-07-05T07:12:26Z"},{"alias_kind":"pith_short_8","alias_value":"EQ5TETSE","created_at":"2026-07-05T07:12:26Z"}],"graph_snapshots":[{"event_id":"sha256:312db576a853a2d69152a5eaca931977e695514f14430f4fbd4bc1c15ce52089","target":"graph","created_at":"2026-07-05T07:12:26Z","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/2311.07996/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Text embedding models have significantly contributed to advancements in natural language processing by adeptly capturing semantic properties of textual data. However, the ability of these models to generalize across a wide range of syntactic contexts remains under-explored. In this paper, we first develop an evaluation set, named \\textbf{SR}, to scrutinize the capability for syntax understanding of text embedding models from two crucial syntactic aspects: Structural heuristics, and Relational understanding among concepts, as revealed by the performance gaps in previous studies. Our findings re","authors_text":"Haizhou Li, Yan Zhang, Zhaopeng Feng, Zhiyang Teng, Zuozhu Liu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-11-14T08:51:00Z","title":"How Well Do Text Embedding Models Understand Syntax?"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.07996","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":"sha256:273a4614648e10e9dd5951a8cad32b1eabcec84b3f85c89998c89fc74b088ac4","target":"record","created_at":"2026-07-05T07:12:26Z","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":"7bde3d81d934ff79e786ac9a34a0d31c0d49eee01d3a4e430207bfebb157786d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-11-14T08:51:00Z","title_canon_sha256":"23361fca58d1f25d0b3e51ce4e752ea76cbacde58d5cc3077fc74886ca9261bb"},"schema_version":"1.0","source":{"id":"2311.07996","kind":"arxiv","version":1}},"canonical_sha256":"243b324e443798852ab2e349bed6bc58f965b01313fc06db7d5d367370140dfd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"243b324e443798852ab2e349bed6bc58f965b01313fc06db7d5d367370140dfd","first_computed_at":"2026-07-05T07:12:26.916152Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:12:26.916152Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"SAkAvHRGvOoUA6tTeEo2fnYwNMfg1n6R/66aDqj33jGK++yp0eKjIQS1kTEPtqCqgdIvGAKfN7iq5pT7hUuFCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:12:26.916549Z","signed_message":"canonical_sha256_bytes"},"source_id":"2311.07996","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:273a4614648e10e9dd5951a8cad32b1eabcec84b3f85c89998c89fc74b088ac4","sha256:312db576a853a2d69152a5eaca931977e695514f14430f4fbd4bc1c15ce52089"],"state_sha256":"06bbdf08bb9e35686b5e7c9037d5d933409adc433f22be72372be98219390478"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"l7bhA/WCae6nuSbetbCBH5FrzJaEtuk0pJXqAGzVNbWfNF6JcioCH7TwcUlZ6YjhtJ43w5wMzVfPCHQzNzgMCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T12:06:36.734608Z","bundle_sha256":"93afd65a66024de09a43c8f4fd5c83f231d3111ba487d6341f4a00efb49fb149"}}