{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:GFNXS6XM7XNQFIJEY5BXXZ54GR","short_pith_number":"pith:GFNXS6XM","canonical_record":{"source":{"id":"2402.08183","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-02-13T02:46:45Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"528bcb3411e16c10a5a083283ec47d35eec2c05623e7f62e0133ce35910acff7","abstract_canon_sha256":"166c36d8496bce52ce8ecb53ffb296e60c9738bf4bd2031fee93346ac2ad941e"},"schema_version":"1.0"},"canonical_sha256":"315b797aecfddb02a124c7437be7bc34732fdff44ecf022f3dc1d8508dab70f3","source":{"kind":"arxiv","id":"2402.08183","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.08183","created_at":"2026-07-05T07:44:28Z"},{"alias_kind":"arxiv_version","alias_value":"2402.08183v1","created_at":"2026-07-05T07:44:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.08183","created_at":"2026-07-05T07:44:28Z"},{"alias_kind":"pith_short_12","alias_value":"GFNXS6XM7XNQ","created_at":"2026-07-05T07:44:28Z"},{"alias_kind":"pith_short_16","alias_value":"GFNXS6XM7XNQFIJE","created_at":"2026-07-05T07:44:28Z"},{"alias_kind":"pith_short_8","alias_value":"GFNXS6XM","created_at":"2026-07-05T07:44:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:GFNXS6XM7XNQFIJEY5BXXZ54GR","target":"record","payload":{"canonical_record":{"source":{"id":"2402.08183","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-02-13T02:46:45Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"528bcb3411e16c10a5a083283ec47d35eec2c05623e7f62e0133ce35910acff7","abstract_canon_sha256":"166c36d8496bce52ce8ecb53ffb296e60c9738bf4bd2031fee93346ac2ad941e"},"schema_version":"1.0"},"canonical_sha256":"315b797aecfddb02a124c7437be7bc34732fdff44ecf022f3dc1d8508dab70f3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:44:28.324375Z","signature_b64":"79a7SeswcBUV92kC4ctb1PiZPNhM3X4BnNT5/GeDczZ1hFIgJ61++ZrQNPWd5qLcNwap5TSm9pI/Nv7pev7ZCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"315b797aecfddb02a124c7437be7bc34732fdff44ecf022f3dc1d8508dab70f3","last_reissued_at":"2026-07-05T07:44:28.323835Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:44:28.323835Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2402.08183","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":"2026-07-05T07:44:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dmxcoylYGaqZlibtJKQDHn40qT1aK/BsMIn9/ac4Hwj6CHlqrlMTXRP/iOXtqbDXuO0TwcoXz2TNSs/Fhh8dBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T22:33:30.526084Z"},"content_sha256":"4bd6e48c47bb1e760456746fc0673cd15190fb7851f1904ae9e3d0fbe811f54b","schema_version":"1.0","event_id":"sha256:4bd6e48c47bb1e760456746fc0673cd15190fb7851f1904ae9e3d0fbe811f54b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:GFNXS6XM7XNQFIJEY5BXXZ54GR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Pixel Sentence Representation Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.CL","authors_text":"Chenghao Xiao, Chenghua Lin, Danlu Chen, G Thomas Hudson, Haoran Duan, Jie Fu, Jungong Han, Noura Al Moubayed, Yizhi Li, Zhuoxu Huang","submitted_at":"2024-02-13T02:46:45Z","abstract_excerpt":"Pretrained language models are long known to be subpar in capturing sentence and document-level semantics. Though heavily investigated, transferring perturbation-based methods from unsupervised visual representation learning to NLP remains an unsolved problem. This is largely due to the discreteness of subword units brought by tokenization of language models, limiting small perturbations of inputs to form semantics-preserved positive pairs. In this work, we conceptualize the learning of sentence-level textual semantics as a visual representation learning process. Drawing from cognitive and lin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.08183","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/2402.08183/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":"2026-07-05T07:44:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KL6cVX9w8+3mkQb4Pq+A3uNIZFt2Tv1J3n2wG+9GlnNvKfS4mQdnpCs5mIjIcYw6pDuF6S73wpbgwDmSArHnCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T22:33:30.527017Z"},"content_sha256":"fa951202a73209abd640a6d16c0ecfcbfab859559be521b42a23abe07799aada","schema_version":"1.0","event_id":"sha256:fa951202a73209abd640a6d16c0ecfcbfab859559be521b42a23abe07799aada"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GFNXS6XM7XNQFIJEY5BXXZ54GR/bundle.json","state_url":"https://pith.science/pith/GFNXS6XM7XNQFIJEY5BXXZ54GR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GFNXS6XM7XNQFIJEY5BXXZ54GR/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":"2026-08-17T22:33:30Z","links":{"resolver":"https://pith.science/pith/GFNXS6XM7XNQFIJEY5BXXZ54GR","bundle":"https://pith.science/pith/GFNXS6XM7XNQFIJEY5BXXZ54GR/bundle.json","state":"https://pith.science/pith/GFNXS6XM7XNQFIJEY5BXXZ54GR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GFNXS6XM7XNQFIJEY5BXXZ54GR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:GFNXS6XM7XNQFIJEY5BXXZ54GR","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":"166c36d8496bce52ce8ecb53ffb296e60c9738bf4bd2031fee93346ac2ad941e","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-02-13T02:46:45Z","title_canon_sha256":"528bcb3411e16c10a5a083283ec47d35eec2c05623e7f62e0133ce35910acff7"},"schema_version":"1.0","source":{"id":"2402.08183","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.08183","created_at":"2026-07-05T07:44:28Z"},{"alias_kind":"arxiv_version","alias_value":"2402.08183v1","created_at":"2026-07-05T07:44:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.08183","created_at":"2026-07-05T07:44:28Z"},{"alias_kind":"pith_short_12","alias_value":"GFNXS6XM7XNQ","created_at":"2026-07-05T07:44:28Z"},{"alias_kind":"pith_short_16","alias_value":"GFNXS6XM7XNQFIJE","created_at":"2026-07-05T07:44:28Z"},{"alias_kind":"pith_short_8","alias_value":"GFNXS6XM","created_at":"2026-07-05T07:44:28Z"}],"graph_snapshots":[{"event_id":"sha256:fa951202a73209abd640a6d16c0ecfcbfab859559be521b42a23abe07799aada","target":"graph","created_at":"2026-07-05T07:44:28Z","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/2402.08183/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Pretrained language models are long known to be subpar in capturing sentence and document-level semantics. Though heavily investigated, transferring perturbation-based methods from unsupervised visual representation learning to NLP remains an unsolved problem. This is largely due to the discreteness of subword units brought by tokenization of language models, limiting small perturbations of inputs to form semantics-preserved positive pairs. In this work, we conceptualize the learning of sentence-level textual semantics as a visual representation learning process. Drawing from cognitive and lin","authors_text":"Chenghao Xiao, Chenghua Lin, Danlu Chen, G Thomas Hudson, Haoran Duan, Jie Fu, Jungong Han, Noura Al Moubayed, Yizhi Li, Zhuoxu Huang","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-02-13T02:46:45Z","title":"Pixel Sentence Representation Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.08183","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":"sha256:4bd6e48c47bb1e760456746fc0673cd15190fb7851f1904ae9e3d0fbe811f54b","target":"record","created_at":"2026-07-05T07:44:28Z","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":"166c36d8496bce52ce8ecb53ffb296e60c9738bf4bd2031fee93346ac2ad941e","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-02-13T02:46:45Z","title_canon_sha256":"528bcb3411e16c10a5a083283ec47d35eec2c05623e7f62e0133ce35910acff7"},"schema_version":"1.0","source":{"id":"2402.08183","kind":"arxiv","version":1}},"canonical_sha256":"315b797aecfddb02a124c7437be7bc34732fdff44ecf022f3dc1d8508dab70f3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"315b797aecfddb02a124c7437be7bc34732fdff44ecf022f3dc1d8508dab70f3","first_computed_at":"2026-07-05T07:44:28.323835Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:44:28.323835Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"79a7SeswcBUV92kC4ctb1PiZPNhM3X4BnNT5/GeDczZ1hFIgJ61++ZrQNPWd5qLcNwap5TSm9pI/Nv7pev7ZCA==","signature_status":"signed_v1","signed_at":"2026-07-05T07:44:28.324375Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.08183","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4bd6e48c47bb1e760456746fc0673cd15190fb7851f1904ae9e3d0fbe811f54b","sha256:fa951202a73209abd640a6d16c0ecfcbfab859559be521b42a23abe07799aada"],"state_sha256":"f7af0c0ab3a843204843ed5c22175e61430a655c7cbddcc6b3adc0e5e3097502"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"x2QbBjHI+vjb58QzfRenjNgo8WCj3vpthK/zt0j3oMEFZ0LvAEvP/CjnRhmeX82mgeRxkRDZQl/buZhLBxJVDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T22:33:30.583734Z","bundle_sha256":"7d383a7bd56b04c6530dc75f461e81c0b3605dc1bceeceab623cd9d72097ecac"}}