{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:KLHQIL2IKA6FQ5ZMV5CZSR3OWQ","short_pith_number":"pith:KLHQIL2I","canonical_record":{"source":{"id":"2305.03210","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2023-05-04T23:46:49Z","cross_cats_sorted":["cs.CL","cs.CV","cs.LG"],"title_canon_sha256":"adbb77390e7ec99974b43eca3d05867c9a61704bd5413f052f3976d43d2435af","abstract_canon_sha256":"1672e3153e7a87783dac219a2efefd6ce89bf16821fda627af32d6ec28981873"},"schema_version":"1.0"},"canonical_sha256":"52cf042f48503c58772caf4599476eb409394c1c1a05b75636b6ef176d5941c8","source":{"kind":"arxiv","id":"2305.03210","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.03210","created_at":"2026-07-05T06:39:35Z"},{"alias_kind":"arxiv_version","alias_value":"2305.03210v2","created_at":"2026-07-05T06:39:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.03210","created_at":"2026-07-05T06:39:35Z"},{"alias_kind":"pith_short_12","alias_value":"KLHQIL2IKA6F","created_at":"2026-07-05T06:39:35Z"},{"alias_kind":"pith_short_16","alias_value":"KLHQIL2IKA6FQ5ZM","created_at":"2026-07-05T06:39:35Z"},{"alias_kind":"pith_short_8","alias_value":"KLHQIL2I","created_at":"2026-07-05T06:39:35Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:KLHQIL2IKA6FQ5ZMV5CZSR3OWQ","target":"record","payload":{"canonical_record":{"source":{"id":"2305.03210","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2023-05-04T23:46:49Z","cross_cats_sorted":["cs.CL","cs.CV","cs.LG"],"title_canon_sha256":"adbb77390e7ec99974b43eca3d05867c9a61704bd5413f052f3976d43d2435af","abstract_canon_sha256":"1672e3153e7a87783dac219a2efefd6ce89bf16821fda627af32d6ec28981873"},"schema_version":"1.0"},"canonical_sha256":"52cf042f48503c58772caf4599476eb409394c1c1a05b75636b6ef176d5941c8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:39:35.046448Z","signature_b64":"2y5JfVGKlCg93lGHrEsI2cm5wT8sXg/lrKQeXJulOS/M07D3ZT0CB9fT6urYf2d4ruJ3mCszuVsqQjEYOxjuDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"52cf042f48503c58772caf4599476eb409394c1c1a05b75636b6ef176d5941c8","last_reissued_at":"2026-07-05T06:39:35.045948Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:39:35.045948Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2305.03210","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:39:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PzPVnh7qYlUrn4giupZxAy78KHYvCRHqlNnjjYpzSEj7v/UYHIDMQkypv/r8u2pAjp7qJ3JgEh4+ZyP1idjSCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T08:20:01.005082Z"},"content_sha256":"c8be7e00779c53a17d26d6f9dc389514b91493384d4c68040ff84c446075bab4","schema_version":"1.0","event_id":"sha256:c8be7e00779c53a17d26d6f9dc389514b91493384d4c68040ff84c446075bab4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:KLHQIL2IKA6FQ5ZMV5CZSR3OWQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"AttentionViz: A Global View of Transformer Attention","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL","cs.CV","cs.LG"],"primary_cat":"cs.HC","authors_text":"Aoyu Wu, Catherine Yeh, Cynthia Chen, Fernanda Vi\\'egas, Martin Wattenberg, Yida Chen","submitted_at":"2023-05-04T23:46:49Z","abstract_excerpt":"Transformer models are revolutionizing machine learning, but their inner workings remain mysterious. In this work, we present a new visualization technique designed to help researchers understand the self-attention mechanism in transformers that allows these models to learn rich, contextual relationships between elements of a sequence. The main idea behind our method is to visualize a joint embedding of the query and key vectors used by transformer models to compute attention. Unlike previous attention visualization techniques, our approach enables the analysis of global patterns across multip"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.03210","kind":"arxiv","version":2},"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/2305.03210/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":"2026-07-05T06:39:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vacsxQk7HDWFXXBSkHLIlw8ZJVLZ8SSY3PMUpo6//qVDE+Z++De/nfBdbMt705Kc5q8LqE+2wG3t2+cIVkq7DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T08:20:01.005461Z"},"content_sha256":"7b362044b95ac668647f914e4799d58a70f895d9857c5e7f0f2d670943fef2b7","schema_version":"1.0","event_id":"sha256:7b362044b95ac668647f914e4799d58a70f895d9857c5e7f0f2d670943fef2b7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KLHQIL2IKA6FQ5ZMV5CZSR3OWQ/bundle.json","state_url":"https://pith.science/pith/KLHQIL2IKA6FQ5ZMV5CZSR3OWQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KLHQIL2IKA6FQ5ZMV5CZSR3OWQ/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":"2026-08-20T08:20:01Z","links":{"resolver":"https://pith.science/pith/KLHQIL2IKA6FQ5ZMV5CZSR3OWQ","bundle":"https://pith.science/pith/KLHQIL2IKA6FQ5ZMV5CZSR3OWQ/bundle.json","state":"https://pith.science/pith/KLHQIL2IKA6FQ5ZMV5CZSR3OWQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KLHQIL2IKA6FQ5ZMV5CZSR3OWQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:KLHQIL2IKA6FQ5ZMV5CZSR3OWQ","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":"1672e3153e7a87783dac219a2efefd6ce89bf16821fda627af32d6ec28981873","cross_cats_sorted":["cs.CL","cs.CV","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2023-05-04T23:46:49Z","title_canon_sha256":"adbb77390e7ec99974b43eca3d05867c9a61704bd5413f052f3976d43d2435af"},"schema_version":"1.0","source":{"id":"2305.03210","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.03210","created_at":"2026-07-05T06:39:35Z"},{"alias_kind":"arxiv_version","alias_value":"2305.03210v2","created_at":"2026-07-05T06:39:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.03210","created_at":"2026-07-05T06:39:35Z"},{"alias_kind":"pith_short_12","alias_value":"KLHQIL2IKA6F","created_at":"2026-07-05T06:39:35Z"},{"alias_kind":"pith_short_16","alias_value":"KLHQIL2IKA6FQ5ZM","created_at":"2026-07-05T06:39:35Z"},{"alias_kind":"pith_short_8","alias_value":"KLHQIL2I","created_at":"2026-07-05T06:39:35Z"}],"graph_snapshots":[{"event_id":"sha256:7b362044b95ac668647f914e4799d58a70f895d9857c5e7f0f2d670943fef2b7","target":"graph","created_at":"2026-07-05T06:39:35Z","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/2305.03210/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Transformer models are revolutionizing machine learning, but their inner workings remain mysterious. In this work, we present a new visualization technique designed to help researchers understand the self-attention mechanism in transformers that allows these models to learn rich, contextual relationships between elements of a sequence. The main idea behind our method is to visualize a joint embedding of the query and key vectors used by transformer models to compute attention. Unlike previous attention visualization techniques, our approach enables the analysis of global patterns across multip","authors_text":"Aoyu Wu, Catherine Yeh, Cynthia Chen, Fernanda Vi\\'egas, Martin Wattenberg, Yida Chen","cross_cats":["cs.CL","cs.CV","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2023-05-04T23:46:49Z","title":"AttentionViz: A Global View of Transformer Attention"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.03210","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:c8be7e00779c53a17d26d6f9dc389514b91493384d4c68040ff84c446075bab4","target":"record","created_at":"2026-07-05T06:39:35Z","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":"1672e3153e7a87783dac219a2efefd6ce89bf16821fda627af32d6ec28981873","cross_cats_sorted":["cs.CL","cs.CV","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2023-05-04T23:46:49Z","title_canon_sha256":"adbb77390e7ec99974b43eca3d05867c9a61704bd5413f052f3976d43d2435af"},"schema_version":"1.0","source":{"id":"2305.03210","kind":"arxiv","version":2}},"canonical_sha256":"52cf042f48503c58772caf4599476eb409394c1c1a05b75636b6ef176d5941c8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"52cf042f48503c58772caf4599476eb409394c1c1a05b75636b6ef176d5941c8","first_computed_at":"2026-07-05T06:39:35.045948Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:39:35.045948Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2y5JfVGKlCg93lGHrEsI2cm5wT8sXg/lrKQeXJulOS/M07D3ZT0CB9fT6urYf2d4ruJ3mCszuVsqQjEYOxjuDw==","signature_status":"signed_v1","signed_at":"2026-07-05T06:39:35.046448Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.03210","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c8be7e00779c53a17d26d6f9dc389514b91493384d4c68040ff84c446075bab4","sha256:7b362044b95ac668647f914e4799d58a70f895d9857c5e7f0f2d670943fef2b7"],"state_sha256":"6d76c461406665eedb28699014536ba301e6da6db79ac0f649f03252aab69d25"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ehPvN/TP/qkEzctKcOjbj7se197V1DPCmykDTXxnNBC127/kekUdXPly1qLn/jgAkqqRxKXS3Ee7zaSmieR5Cw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-20T08:20:01.008799Z","bundle_sha256":"1aad34b38eda9ab81a1ee4e9c9157d2688757b1c31acefd7d2cfb4179faa2c29"}}