{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:OABOTDEFI7EGASIMOQUU7TPDDR","short_pith_number":"pith:OABOTDEF","canonical_record":{"source":{"id":"2303.02506","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-03-04T21:22:47Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"d870775460b4e94243b67f7f9c0a102aa6c4ec6d49d8fda999d8549374a0d6be","abstract_canon_sha256":"ce4742aa7a8c96dd16d77b8ebf666433e8860aa470669621338fca42e4e07ef6"},"schema_version":"1.0"},"canonical_sha256":"7002e98c8547c860490c74294fcde31c67ea0550fefbd33159ec68a0fd78b68f","source":{"kind":"arxiv","id":"2303.02506","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.02506","created_at":"2026-07-05T07:35:18Z"},{"alias_kind":"arxiv_version","alias_value":"2303.02506v3","created_at":"2026-07-05T07:35:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.02506","created_at":"2026-07-05T07:35:18Z"},{"alias_kind":"pith_short_12","alias_value":"OABOTDEFI7EG","created_at":"2026-07-05T07:35:18Z"},{"alias_kind":"pith_short_16","alias_value":"OABOTDEFI7EGASIM","created_at":"2026-07-05T07:35:18Z"},{"alias_kind":"pith_short_8","alias_value":"OABOTDEF","created_at":"2026-07-05T07:35:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:OABOTDEFI7EGASIMOQUU7TPDDR","target":"record","payload":{"canonical_record":{"source":{"id":"2303.02506","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-03-04T21:22:47Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"d870775460b4e94243b67f7f9c0a102aa6c4ec6d49d8fda999d8549374a0d6be","abstract_canon_sha256":"ce4742aa7a8c96dd16d77b8ebf666433e8860aa470669621338fca42e4e07ef6"},"schema_version":"1.0"},"canonical_sha256":"7002e98c8547c860490c74294fcde31c67ea0550fefbd33159ec68a0fd78b68f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:35:18.190552Z","signature_b64":"uWAIqEkAgTEAF9msazI5wagntCaHZugzYmFVKmfSOeXZeqFNSGNYK7dI9ww2mVteloInKfGB4fxx67hsZdmxCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7002e98c8547c860490c74294fcde31c67ea0550fefbd33159ec68a0fd78b68f","last_reissued_at":"2026-07-05T07:35:18.190075Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:35:18.190075Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2303.02506","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:35:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8xY14s03cQl04+Z1Ad8ZamDNqRg5xrUK39PjlA4mfdAjH4Z+vyTacPDp75BSep200XpQQdnr5GbTGFGvioFkCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T00:17:37.710074Z"},"content_sha256":"0fed4898c9aad256de15b2e9ea26b5289d25946f5055cb2534485b53ef135ae8","schema_version":"1.0","event_id":"sha256:0fed4898c9aad256de15b2e9ea26b5289d25946f5055cb2534485b53ef135ae8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:OABOTDEFI7EGASIMOQUU7TPDDR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Prismer: A Vision-Language Model with Multi-Task Experts","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Anima Anandkumar, Chaowei Xiao, Edward Johns, Linxi Fan, Shikun Liu, Zhiding Yu","submitted_at":"2023-03-04T21:22:47Z","abstract_excerpt":"Recent vision-language models have shown impressive multi-modal generation capabilities. However, typically they require training huge models on massive datasets. As a more scalable alternative, we introduce Prismer, a data- and parameter-efficient vision-language model that leverages an ensemble of task-specific experts. Prismer only requires training of a small number of components, with the majority of network weights inherited from multiple readily-available, pre-trained experts, and kept frozen during training. By leveraging experts from a wide range of domains, we show Prismer can effici"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.02506","kind":"arxiv","version":3},"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/2303.02506/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":"2026-07-05T07:35:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"w7PDxQg4My6WS2wCVCqpA0DBDOhwN0/1IE1SA54FRFy5rYCXmDsHIHxgdDuJu/qlfLN7nrErxgMsTsT5V/5wAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T00:17:37.710590Z"},"content_sha256":"7ff8af3f298e5f38ff008aa5a1e57424a328fae629537c88f8256cba7726e7f1","schema_version":"1.0","event_id":"sha256:7ff8af3f298e5f38ff008aa5a1e57424a328fae629537c88f8256cba7726e7f1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OABOTDEFI7EGASIMOQUU7TPDDR/bundle.json","state_url":"https://pith.science/pith/OABOTDEFI7EGASIMOQUU7TPDDR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OABOTDEFI7EGASIMOQUU7TPDDR/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":"2026-08-06T00:17:37Z","links":{"resolver":"https://pith.science/pith/OABOTDEFI7EGASIMOQUU7TPDDR","bundle":"https://pith.science/pith/OABOTDEFI7EGASIMOQUU7TPDDR/bundle.json","state":"https://pith.science/pith/OABOTDEFI7EGASIMOQUU7TPDDR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OABOTDEFI7EGASIMOQUU7TPDDR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:OABOTDEFI7EGASIMOQUU7TPDDR","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":"ce4742aa7a8c96dd16d77b8ebf666433e8860aa470669621338fca42e4e07ef6","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-03-04T21:22:47Z","title_canon_sha256":"d870775460b4e94243b67f7f9c0a102aa6c4ec6d49d8fda999d8549374a0d6be"},"schema_version":"1.0","source":{"id":"2303.02506","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.02506","created_at":"2026-07-05T07:35:18Z"},{"alias_kind":"arxiv_version","alias_value":"2303.02506v3","created_at":"2026-07-05T07:35:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.02506","created_at":"2026-07-05T07:35:18Z"},{"alias_kind":"pith_short_12","alias_value":"OABOTDEFI7EG","created_at":"2026-07-05T07:35:18Z"},{"alias_kind":"pith_short_16","alias_value":"OABOTDEFI7EGASIM","created_at":"2026-07-05T07:35:18Z"},{"alias_kind":"pith_short_8","alias_value":"OABOTDEF","created_at":"2026-07-05T07:35:18Z"}],"graph_snapshots":[{"event_id":"sha256:7ff8af3f298e5f38ff008aa5a1e57424a328fae629537c88f8256cba7726e7f1","target":"graph","created_at":"2026-07-05T07:35:18Z","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/2303.02506/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent vision-language models have shown impressive multi-modal generation capabilities. However, typically they require training huge models on massive datasets. As a more scalable alternative, we introduce Prismer, a data- and parameter-efficient vision-language model that leverages an ensemble of task-specific experts. Prismer only requires training of a small number of components, with the majority of network weights inherited from multiple readily-available, pre-trained experts, and kept frozen during training. By leveraging experts from a wide range of domains, we show Prismer can effici","authors_text":"Anima Anandkumar, Chaowei Xiao, Edward Johns, Linxi Fan, Shikun Liu, Zhiding Yu","cross_cats":["cs.AI","cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-03-04T21:22:47Z","title":"Prismer: A Vision-Language Model with Multi-Task Experts"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.02506","kind":"arxiv","version":3},"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":"sha256:0fed4898c9aad256de15b2e9ea26b5289d25946f5055cb2534485b53ef135ae8","target":"record","created_at":"2026-07-05T07:35:18Z","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":"ce4742aa7a8c96dd16d77b8ebf666433e8860aa470669621338fca42e4e07ef6","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-03-04T21:22:47Z","title_canon_sha256":"d870775460b4e94243b67f7f9c0a102aa6c4ec6d49d8fda999d8549374a0d6be"},"schema_version":"1.0","source":{"id":"2303.02506","kind":"arxiv","version":3}},"canonical_sha256":"7002e98c8547c860490c74294fcde31c67ea0550fefbd33159ec68a0fd78b68f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7002e98c8547c860490c74294fcde31c67ea0550fefbd33159ec68a0fd78b68f","first_computed_at":"2026-07-05T07:35:18.190075Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:35:18.190075Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uWAIqEkAgTEAF9msazI5wagntCaHZugzYmFVKmfSOeXZeqFNSGNYK7dI9ww2mVteloInKfGB4fxx67hsZdmxCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:35:18.190552Z","signed_message":"canonical_sha256_bytes"},"source_id":"2303.02506","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0fed4898c9aad256de15b2e9ea26b5289d25946f5055cb2534485b53ef135ae8","sha256:7ff8af3f298e5f38ff008aa5a1e57424a328fae629537c88f8256cba7726e7f1"],"state_sha256":"4a952976803e087c2c8be581e5059dd3760090c3a13333f0b3c10372fc2bb34b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WKL0kyr76SC3wRCrsqUJAA/9dK1vWwCHbK9w71DI2VBHsrWYfVSwnRYxj5vquwsHEB+GZC/Wswn+f36sdMvYAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T00:17:37.715329Z","bundle_sha256":"d9854c160dd4486ee74d41028ef9767a1fd4304adc6ed147508e866e9768eca4"}}