{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:XWYOYNBFLZVVL5UMDQV4W5NH7J","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":"6c3ab3295d7bed20546091a132052aaf4242234a8d9efb0a3479ef44531a0571","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-11-04T19:55:32Z","title_canon_sha256":"feaddea3892d8ab7cc5890b437dca389967a3e0b19807810d9eb09098aee385c"},"schema_version":"1.0","source":{"id":"2411.02564","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.02564","created_at":"2026-07-05T09:33:40Z"},{"alias_kind":"arxiv_version","alias_value":"2411.02564v2","created_at":"2026-07-05T09:33:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.02564","created_at":"2026-07-05T09:33:40Z"},{"alias_kind":"pith_short_12","alias_value":"XWYOYNBFLZVV","created_at":"2026-07-05T09:33:40Z"},{"alias_kind":"pith_short_16","alias_value":"XWYOYNBFLZVVL5UM","created_at":"2026-07-05T09:33:40Z"},{"alias_kind":"pith_short_8","alias_value":"XWYOYNBF","created_at":"2026-07-05T09:33:40Z"}],"graph_snapshots":[{"event_id":"sha256:d9584d36855875154010eb45b23ac197ee8e392f8fdd2196e08f671a7c55b626","target":"graph","created_at":"2026-07-05T09:33:40Z","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/2411.02564/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Instruction tuning constitutes a prevalent technique for tailoring Large Vision Language Models (LVLMs) to meet individual task requirements. To date, most of the existing approaches are confined to single-task adaptation, whereas the requirements in real-world scenarios are inherently varied and continually evolving. Thus an ideal LVLM should sustain continual instruction tuning in the face of stream-task distributions (i.e., different domains, emerging capabilities, and new datasets) while minimizing the forgetting of previously acquired knowledge. To achieve this, we propose a new benchmark","authors_text":"Henghui Ding, Ian Reid, Jiahua Dong, Meng Cao, Tiancai Wang, Xiangyu Zhang, Xiaodan Liang, Yingfei Liu, Yuyang Liu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-11-04T19:55:32Z","title":"Continual LLaVA: Continual Instruction Tuning in Large Vision-Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.02564","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:ad307fadb48c7b4e2fe2d1939713ad4445cc801b6b19b84420d0e7fa01cf8830","target":"record","created_at":"2026-07-05T09:33:40Z","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":"6c3ab3295d7bed20546091a132052aaf4242234a8d9efb0a3479ef44531a0571","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-11-04T19:55:32Z","title_canon_sha256":"feaddea3892d8ab7cc5890b437dca389967a3e0b19807810d9eb09098aee385c"},"schema_version":"1.0","source":{"id":"2411.02564","kind":"arxiv","version":2}},"canonical_sha256":"bdb0ec34255e6b55f68c1c2bcb75a7fa5dc42aefecadc244912a4fc2eecad6ba","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bdb0ec34255e6b55f68c1c2bcb75a7fa5dc42aefecadc244912a4fc2eecad6ba","first_computed_at":"2026-07-05T09:33:40.309152Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:33:40.309152Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"agHW4NiqQaTtYcqEXMNltUddWLNZfyDzP0RWkn/GEUNDlIrx1eqX5Z7CqVGT68HlKUPADe91UZ7asDuSsNS3AA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:33:40.309786Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.02564","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ad307fadb48c7b4e2fe2d1939713ad4445cc801b6b19b84420d0e7fa01cf8830","sha256:d9584d36855875154010eb45b23ac197ee8e392f8fdd2196e08f671a7c55b626"],"state_sha256":"9a4e4543fe34a4dff94bb75b13437e2c1c73e57de9b101f0bc66b9b526020133"}