{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:CMK5435CCMC45FU53QYSU2TW4U","short_pith_number":"pith:CMK5435C","canonical_record":{"source":{"id":"2412.15650","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-20T08:06:00Z","cross_cats_sorted":[],"title_canon_sha256":"4ab00beb2391238118abc0349ea63485fddd9c88da7b455bc9773a520f22d901","abstract_canon_sha256":"73d6e35563f95d2487b059b5ebf98a7afe8b87f8444fdd31ccd4faacc0fe1d9e"},"schema_version":"1.0"},"canonical_sha256":"1315de6fa21305ce969ddc312a6a76e52f2c16d2d9ccc135189808cf4931448d","source":{"kind":"arxiv","id":"2412.15650","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.15650","created_at":"2026-07-05T09:52:27Z"},{"alias_kind":"arxiv_version","alias_value":"2412.15650v1","created_at":"2026-07-05T09:52:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.15650","created_at":"2026-07-05T09:52:27Z"},{"alias_kind":"pith_short_12","alias_value":"CMK5435CCMC4","created_at":"2026-07-05T09:52:27Z"},{"alias_kind":"pith_short_16","alias_value":"CMK5435CCMC45FU5","created_at":"2026-07-05T09:52:27Z"},{"alias_kind":"pith_short_8","alias_value":"CMK5435C","created_at":"2026-07-05T09:52:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:CMK5435CCMC45FU53QYSU2TW4U","target":"record","payload":{"canonical_record":{"source":{"id":"2412.15650","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-20T08:06:00Z","cross_cats_sorted":[],"title_canon_sha256":"4ab00beb2391238118abc0349ea63485fddd9c88da7b455bc9773a520f22d901","abstract_canon_sha256":"73d6e35563f95d2487b059b5ebf98a7afe8b87f8444fdd31ccd4faacc0fe1d9e"},"schema_version":"1.0"},"canonical_sha256":"1315de6fa21305ce969ddc312a6a76e52f2c16d2d9ccc135189808cf4931448d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:52:27.716593Z","signature_b64":"LGP2kzlx1x/1lZcTg0y5N/itqt4xA7U9lJFFWhOZQFkOKfgVh3ZqJ0Nfj3GUBGt7id702pLXxGtcei/BmO8PDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1315de6fa21305ce969ddc312a6a76e52f2c16d2d9ccc135189808cf4931448d","last_reissued_at":"2026-07-05T09:52:27.716102Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:52:27.716102Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2412.15650","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":"2026-07-05T09:52:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"c2rIqa4wceOVew1nFQT/fqQlyhor7E3G/cowy9ZLdsRh08icB/dDQOwQ/LpnFaaRxcRbnZiRp8X6Qe1uIKGWAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T10:33:48.301653Z"},"content_sha256":"c2b2d2dc2ffe97c086db71fd3477fe93a95559475fe13348d54d3061a4abf890","schema_version":"1.0","event_id":"sha256:c2b2d2dc2ffe97c086db71fd3477fe93a95559475fe13348d54d3061a4abf890"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:CMK5435CCMC45FU53QYSU2TW4U","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Beyond Human Data: Aligning Multimodal Large Language Models by Iterative Self-Evolution","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Changxing Ding, Chao Xue, Qiong Cao, Wentao Tan, Yibing Zhan","submitted_at":"2024-12-20T08:06:00Z","abstract_excerpt":"Human preference alignment can greatly enhance Multimodal Large Language Models (MLLMs), but collecting high-quality preference data is costly. A promising solution is the self-evolution strategy, where models are iteratively trained on data they generate. However, current techniques still rely on human- or GPT-annotated data and sometimes require additional models or ground truth answers. To address these issues, we propose a novel multimodal self-evolution framework that enables the model to autonomously generate high-quality questions and answers using only unannotated images.\n  First, we i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.15650","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/2412.15650/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":"2026-07-05T09:52:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Skadscnn124HCleGOVew9JRK2vIyjWRc2P+9idrGY/X7XpwReCvmo5w/HClzfOZw//RELNckjTqM+EahezhCAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T10:33:48.303078Z"},"content_sha256":"d0553eac81459d7680811583dbdd026aeb264c1725ca05b27a09c75474a9fd5b","schema_version":"1.0","event_id":"sha256:d0553eac81459d7680811583dbdd026aeb264c1725ca05b27a09c75474a9fd5b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CMK5435CCMC45FU53QYSU2TW4U/bundle.json","state_url":"https://pith.science/pith/CMK5435CCMC45FU53QYSU2TW4U/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CMK5435CCMC45FU53QYSU2TW4U/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":"2026-08-09T10:33:48Z","links":{"resolver":"https://pith.science/pith/CMK5435CCMC45FU53QYSU2TW4U","bundle":"https://pith.science/pith/CMK5435CCMC45FU53QYSU2TW4U/bundle.json","state":"https://pith.science/pith/CMK5435CCMC45FU53QYSU2TW4U/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CMK5435CCMC45FU53QYSU2TW4U/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:CMK5435CCMC45FU53QYSU2TW4U","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":"73d6e35563f95d2487b059b5ebf98a7afe8b87f8444fdd31ccd4faacc0fe1d9e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-20T08:06:00Z","title_canon_sha256":"4ab00beb2391238118abc0349ea63485fddd9c88da7b455bc9773a520f22d901"},"schema_version":"1.0","source":{"id":"2412.15650","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.15650","created_at":"2026-07-05T09:52:27Z"},{"alias_kind":"arxiv_version","alias_value":"2412.15650v1","created_at":"2026-07-05T09:52:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.15650","created_at":"2026-07-05T09:52:27Z"},{"alias_kind":"pith_short_12","alias_value":"CMK5435CCMC4","created_at":"2026-07-05T09:52:27Z"},{"alias_kind":"pith_short_16","alias_value":"CMK5435CCMC45FU5","created_at":"2026-07-05T09:52:27Z"},{"alias_kind":"pith_short_8","alias_value":"CMK5435C","created_at":"2026-07-05T09:52:27Z"}],"graph_snapshots":[{"event_id":"sha256:d0553eac81459d7680811583dbdd026aeb264c1725ca05b27a09c75474a9fd5b","target":"graph","created_at":"2026-07-05T09:52:27Z","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/2412.15650/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Human preference alignment can greatly enhance Multimodal Large Language Models (MLLMs), but collecting high-quality preference data is costly. A promising solution is the self-evolution strategy, where models are iteratively trained on data they generate. However, current techniques still rely on human- or GPT-annotated data and sometimes require additional models or ground truth answers. To address these issues, we propose a novel multimodal self-evolution framework that enables the model to autonomously generate high-quality questions and answers using only unannotated images.\n  First, we i","authors_text":"Changxing Ding, Chao Xue, Qiong Cao, Wentao Tan, Yibing Zhan","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-20T08:06:00Z","title":"Beyond Human Data: Aligning Multimodal Large Language Models by Iterative Self-Evolution"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.15650","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:c2b2d2dc2ffe97c086db71fd3477fe93a95559475fe13348d54d3061a4abf890","target":"record","created_at":"2026-07-05T09:52:27Z","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":"73d6e35563f95d2487b059b5ebf98a7afe8b87f8444fdd31ccd4faacc0fe1d9e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-20T08:06:00Z","title_canon_sha256":"4ab00beb2391238118abc0349ea63485fddd9c88da7b455bc9773a520f22d901"},"schema_version":"1.0","source":{"id":"2412.15650","kind":"arxiv","version":1}},"canonical_sha256":"1315de6fa21305ce969ddc312a6a76e52f2c16d2d9ccc135189808cf4931448d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1315de6fa21305ce969ddc312a6a76e52f2c16d2d9ccc135189808cf4931448d","first_computed_at":"2026-07-05T09:52:27.716102Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:52:27.716102Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LGP2kzlx1x/1lZcTg0y5N/itqt4xA7U9lJFFWhOZQFkOKfgVh3ZqJ0Nfj3GUBGt7id702pLXxGtcei/BmO8PDg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:52:27.716593Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.15650","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c2b2d2dc2ffe97c086db71fd3477fe93a95559475fe13348d54d3061a4abf890","sha256:d0553eac81459d7680811583dbdd026aeb264c1725ca05b27a09c75474a9fd5b"],"state_sha256":"012a82dfa3cccf2e831a851a1a8904f544312fb1716d73b70f8cc3a576d78b00"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sCbqUD4xdb+dHNg9TOfm2VIDNkZJlsivZjmJHSJGKCf3xFYaygs+I4a+SSBEUUS+XM9bdoIkwVdXCif6GOezDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T10:33:48.310905Z","bundle_sha256":"7e994a9f90647c46fe4f5ffde6a45183d5bcedf915870e0722009c3f59601dd0"}}