{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:JFRBLTJJORDPPNOZSXMYILW2WI","short_pith_number":"pith:JFRBLTJJ","canonical_record":{"source":{"id":"2403.02302","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-03-04T18:32:12Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"8b056cb8cf8f46cf477571cf1c981355472d6fcaa962ab4ac1d9632f66d3e09f","abstract_canon_sha256":"899546631e8bea46822f45856835128046aa44b00bd9f76e6a4efb39585bfeb0"},"schema_version":"1.0"},"canonical_sha256":"496215cd297446f7b5d995d9842edab20edbd77632c50a9b618e25876f01df54","source":{"kind":"arxiv","id":"2403.02302","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.02302","created_at":"2026-07-05T10:03:24Z"},{"alias_kind":"arxiv_version","alias_value":"2403.02302v4","created_at":"2026-07-05T10:03:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.02302","created_at":"2026-07-05T10:03:24Z"},{"alias_kind":"pith_short_12","alias_value":"JFRBLTJJORDP","created_at":"2026-07-05T10:03:24Z"},{"alias_kind":"pith_short_16","alias_value":"JFRBLTJJORDPPNOZ","created_at":"2026-07-05T10:03:24Z"},{"alias_kind":"pith_short_8","alias_value":"JFRBLTJJ","created_at":"2026-07-05T10:03:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:JFRBLTJJORDPPNOZSXMYILW2WI","target":"record","payload":{"canonical_record":{"source":{"id":"2403.02302","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-03-04T18:32:12Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"8b056cb8cf8f46cf477571cf1c981355472d6fcaa962ab4ac1d9632f66d3e09f","abstract_canon_sha256":"899546631e8bea46822f45856835128046aa44b00bd9f76e6a4efb39585bfeb0"},"schema_version":"1.0"},"canonical_sha256":"496215cd297446f7b5d995d9842edab20edbd77632c50a9b618e25876f01df54","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:03:24.195333Z","signature_b64":"yzO8GoO3yOU5o/NUZPDj3FDMeTdh/WDtQHGIU+1wO2oWs/e3c+6MVi6ZZgFeDzYAjB/+GPIRvscpJFK7B009Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"496215cd297446f7b5d995d9842edab20edbd77632c50a9b618e25876f01df54","last_reissued_at":"2026-07-05T10:03:24.194864Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:03:24.194864Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2403.02302","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:03:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+wjERawNpGCBqWvNJLjNIyEwRaqzPbiMefEg5tEbL9NRu3fHTUiBj0JQ6j5211FSxrWt5KvxsmYqWp4zC2/ZAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T12:51:45.338194Z"},"content_sha256":"ec0b430a05838413a78db9f80d133bec5b44355ed5d19eb888aa6d179d1f670e","schema_version":"1.0","event_id":"sha256:ec0b430a05838413a78db9f80d133bec5b44355ed5d19eb888aa6d179d1f670e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:JFRBLTJJORDPPNOZSXMYILW2WI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Beyond Specialization: Assessing the Capabilities of MLLMs in Age and Gender Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Grigorii Alekseenko, Irina Tolstykh, Maksim Kuprashevich","submitted_at":"2024-03-04T18:32:12Z","abstract_excerpt":"Multimodal Large Language Models (MLLMs) have recently gained immense popularity. Powerful commercial models like ChatGPT-4V and Gemini, as well as open-source ones such as LLaVA, are essentially general-purpose models and are applied to solve a wide variety of tasks, including those in computer vision. These neural networks possess such strong general knowledge and reasoning abilities that they have proven capable of working even on tasks for which they were not specifically trained. We compared the capabilities of the most powerful MLLMs to date: ShareGPT4V, ChatGPT, LLaVA-Next in a speciali"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.02302","kind":"arxiv","version":4},"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/2403.02302/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":"2026-07-05T10:03:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"a7cTEwFtEARlWQK6Vy9ydgYPSMAJ/fm0Uurk3ExpA/EOR8sD1NKhuDIq+63HWaj5IfX7SOZ4U0Xh44GYm1XXDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T12:51:45.339090Z"},"content_sha256":"1fc00a98d254b1286d641eb43de4d1bbda7a48f0310f91976a6971b7f0b10f01","schema_version":"1.0","event_id":"sha256:1fc00a98d254b1286d641eb43de4d1bbda7a48f0310f91976a6971b7f0b10f01"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JFRBLTJJORDPPNOZSXMYILW2WI/bundle.json","state_url":"https://pith.science/pith/JFRBLTJJORDPPNOZSXMYILW2WI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JFRBLTJJORDPPNOZSXMYILW2WI/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":"2026-08-08T12:51:45Z","links":{"resolver":"https://pith.science/pith/JFRBLTJJORDPPNOZSXMYILW2WI","bundle":"https://pith.science/pith/JFRBLTJJORDPPNOZSXMYILW2WI/bundle.json","state":"https://pith.science/pith/JFRBLTJJORDPPNOZSXMYILW2WI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JFRBLTJJORDPPNOZSXMYILW2WI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:JFRBLTJJORDPPNOZSXMYILW2WI","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":"899546631e8bea46822f45856835128046aa44b00bd9f76e6a4efb39585bfeb0","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-03-04T18:32:12Z","title_canon_sha256":"8b056cb8cf8f46cf477571cf1c981355472d6fcaa962ab4ac1d9632f66d3e09f"},"schema_version":"1.0","source":{"id":"2403.02302","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.02302","created_at":"2026-07-05T10:03:24Z"},{"alias_kind":"arxiv_version","alias_value":"2403.02302v4","created_at":"2026-07-05T10:03:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.02302","created_at":"2026-07-05T10:03:24Z"},{"alias_kind":"pith_short_12","alias_value":"JFRBLTJJORDP","created_at":"2026-07-05T10:03:24Z"},{"alias_kind":"pith_short_16","alias_value":"JFRBLTJJORDPPNOZ","created_at":"2026-07-05T10:03:24Z"},{"alias_kind":"pith_short_8","alias_value":"JFRBLTJJ","created_at":"2026-07-05T10:03:24Z"}],"graph_snapshots":[{"event_id":"sha256:1fc00a98d254b1286d641eb43de4d1bbda7a48f0310f91976a6971b7f0b10f01","target":"graph","created_at":"2026-07-05T10:03:24Z","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/2403.02302/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multimodal Large Language Models (MLLMs) have recently gained immense popularity. Powerful commercial models like ChatGPT-4V and Gemini, as well as open-source ones such as LLaVA, are essentially general-purpose models and are applied to solve a wide variety of tasks, including those in computer vision. These neural networks possess such strong general knowledge and reasoning abilities that they have proven capable of working even on tasks for which they were not specifically trained. We compared the capabilities of the most powerful MLLMs to date: ShareGPT4V, ChatGPT, LLaVA-Next in a speciali","authors_text":"Grigorii Alekseenko, Irina Tolstykh, Maksim Kuprashevich","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-03-04T18:32:12Z","title":"Beyond Specialization: Assessing the Capabilities of MLLMs in Age and Gender Estimation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.02302","kind":"arxiv","version":4},"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:ec0b430a05838413a78db9f80d133bec5b44355ed5d19eb888aa6d179d1f670e","target":"record","created_at":"2026-07-05T10:03:24Z","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":"899546631e8bea46822f45856835128046aa44b00bd9f76e6a4efb39585bfeb0","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-03-04T18:32:12Z","title_canon_sha256":"8b056cb8cf8f46cf477571cf1c981355472d6fcaa962ab4ac1d9632f66d3e09f"},"schema_version":"1.0","source":{"id":"2403.02302","kind":"arxiv","version":4}},"canonical_sha256":"496215cd297446f7b5d995d9842edab20edbd77632c50a9b618e25876f01df54","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"496215cd297446f7b5d995d9842edab20edbd77632c50a9b618e25876f01df54","first_computed_at":"2026-07-05T10:03:24.194864Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:03:24.194864Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"yzO8GoO3yOU5o/NUZPDj3FDMeTdh/WDtQHGIU+1wO2oWs/e3c+6MVi6ZZgFeDzYAjB/+GPIRvscpJFK7B009Cg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:03:24.195333Z","signed_message":"canonical_sha256_bytes"},"source_id":"2403.02302","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ec0b430a05838413a78db9f80d133bec5b44355ed5d19eb888aa6d179d1f670e","sha256:1fc00a98d254b1286d641eb43de4d1bbda7a48f0310f91976a6971b7f0b10f01"],"state_sha256":"740a96d4f77a4253b7c0a2f93d85f148d96fa95a9d38c707d99c8ab2baa26c4f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"z/l8YBF6a0ycRccSSA8z3L6TtFM87crjnmiBFUwOSx+O01SNQcEGwBVqYIV3Zq+i8aZxEBqEulQe7iWJtCRfCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T12:51:45.345028Z","bundle_sha256":"acf31132f2c70f618bc8b0f799ab88eeabb2886f5f947d0d59a361307f71c41c"}}