{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:G3DWQORIMNAAVXRDWRN2HXBC5Z","short_pith_number":"pith:G3DWQORI","schema_version":"1.0","canonical_sha256":"36c7683a2863400ade23b45ba3dc22ee76577a6f6414a56b8c8e6d42b3d59bd6","source":{"kind":"arxiv","id":"2508.18164","version":1},"attestation_state":"computed","paper":{"title":"S2Sent: Nested Selectivity Aware Sentence Representation Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Hui Liu, Jianxiang Zang, Meiling Ning, Nijia Mo, Yonda Wei","submitted_at":"2025-08-25T16:13:42Z","abstract_excerpt":"The combination of Transformer-based encoders with contrastive learning represents the current mainstream paradigm for sentence representation learning. This paradigm is typically based on the hidden states of the last Transformer block of the encoder. However, within Transformer-based encoders, different blocks exhibit varying degrees of semantic perception ability. From the perspective of interpretability, the semantic perception potential of knowledge neurons is modulated by stimuli, thus rational cross-block representation fusion is a direction worth optimizing. To balance the semantic red"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2508.18164","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-08-25T16:13:42Z","cross_cats_sorted":[],"title_canon_sha256":"4c008a7398a89c4c9c6f1ed9a5194a5680c05c178db3ad25c77165e1b7df00a7","abstract_canon_sha256":"38d2a214876149a3ad855960e009ff94842ced8847c6fc5c85aa84635696cc60"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:58:54.815356Z","signature_b64":"0+mcn/jueN1prMN9isgOSGqIioKFCqJQo6levEwvvLqbErGvHn3Kq6YmkE1NwO5zOx5BIA1Kkb79CFtyvjSsBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"36c7683a2863400ade23b45ba3dc22ee76577a6f6414a56b8c8e6d42b3d59bd6","last_reissued_at":"2026-07-05T11:58:54.814905Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:58:54.814905Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"S2Sent: Nested Selectivity Aware Sentence Representation Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Hui Liu, Jianxiang Zang, Meiling Ning, Nijia Mo, Yonda Wei","submitted_at":"2025-08-25T16:13:42Z","abstract_excerpt":"The combination of Transformer-based encoders with contrastive learning represents the current mainstream paradigm for sentence representation learning. This paradigm is typically based on the hidden states of the last Transformer block of the encoder. However, within Transformer-based encoders, different blocks exhibit varying degrees of semantic perception ability. From the perspective of interpretability, the semantic perception potential of knowledge neurons is modulated by stimuli, thus rational cross-block representation fusion is a direction worth optimizing. To balance the semantic red"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.18164","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/2508.18164/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2508.18164","created_at":"2026-07-05T11:58:54.814959+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.18164v1","created_at":"2026-07-05T11:58:54.814959+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.18164","created_at":"2026-07-05T11:58:54.814959+00:00"},{"alias_kind":"pith_short_12","alias_value":"G3DWQORIMNAA","created_at":"2026-07-05T11:58:54.814959+00:00"},{"alias_kind":"pith_short_16","alias_value":"G3DWQORIMNAAVXRD","created_at":"2026-07-05T11:58:54.814959+00:00"},{"alias_kind":"pith_short_8","alias_value":"G3DWQORI","created_at":"2026-07-05T11:58:54.814959+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/G3DWQORIMNAAVXRDWRN2HXBC5Z","json":"https://pith.science/pith/G3DWQORIMNAAVXRDWRN2HXBC5Z.json","graph_json":"https://pith.science/api/pith-number/G3DWQORIMNAAVXRDWRN2HXBC5Z/graph.json","events_json":"https://pith.science/api/pith-number/G3DWQORIMNAAVXRDWRN2HXBC5Z/events.json","paper":"https://pith.science/paper/G3DWQORI"},"agent_actions":{"view_html":"https://pith.science/pith/G3DWQORIMNAAVXRDWRN2HXBC5Z","download_json":"https://pith.science/pith/G3DWQORIMNAAVXRDWRN2HXBC5Z.json","view_paper":"https://pith.science/paper/G3DWQORI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.18164&json=true","fetch_graph":"https://pith.science/api/pith-number/G3DWQORIMNAAVXRDWRN2HXBC5Z/graph.json","fetch_events":"https://pith.science/api/pith-number/G3DWQORIMNAAVXRDWRN2HXBC5Z/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/G3DWQORIMNAAVXRDWRN2HXBC5Z/action/timestamp_anchor","attest_storage":"https://pith.science/pith/G3DWQORIMNAAVXRDWRN2HXBC5Z/action/storage_attestation","attest_author":"https://pith.science/pith/G3DWQORIMNAAVXRDWRN2HXBC5Z/action/author_attestation","sign_citation":"https://pith.science/pith/G3DWQORIMNAAVXRDWRN2HXBC5Z/action/citation_signature","submit_replication":"https://pith.science/pith/G3DWQORIMNAAVXRDWRN2HXBC5Z/action/replication_record"}},"created_at":"2026-07-05T11:58:54.814959+00:00","updated_at":"2026-07-05T11:58:54.814959+00:00"}