{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:NYNUATQBNTEJ3BFZ2L7F7AONLX","short_pith_number":"pith:NYNUATQB","canonical_record":{"source":{"id":"2412.01720","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-02T17:10:16Z","cross_cats_sorted":[],"title_canon_sha256":"7012e022c3f1629311d2f6847895e6108ef01de5193765301208509c0d0cc8f1","abstract_canon_sha256":"9e81fdf8d7dde4b824dff0260c38f0936b4ed4e28b2cb2d147ef34af289757c6"},"schema_version":"1.0"},"canonical_sha256":"6e1b404e016cc89d84b9d2fe5f81cd5dda25a56e17c1dfc96b4de554a845acbe","source":{"kind":"arxiv","id":"2412.01720","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.01720","created_at":"2026-07-05T09:43:18Z"},{"alias_kind":"arxiv_version","alias_value":"2412.01720v1","created_at":"2026-07-05T09:43:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.01720","created_at":"2026-07-05T09:43:18Z"},{"alias_kind":"pith_short_12","alias_value":"NYNUATQBNTEJ","created_at":"2026-07-05T09:43:18Z"},{"alias_kind":"pith_short_16","alias_value":"NYNUATQBNTEJ3BFZ","created_at":"2026-07-05T09:43:18Z"},{"alias_kind":"pith_short_8","alias_value":"NYNUATQB","created_at":"2026-07-05T09:43:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:NYNUATQBNTEJ3BFZ2L7F7AONLX","target":"record","payload":{"canonical_record":{"source":{"id":"2412.01720","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-02T17:10:16Z","cross_cats_sorted":[],"title_canon_sha256":"7012e022c3f1629311d2f6847895e6108ef01de5193765301208509c0d0cc8f1","abstract_canon_sha256":"9e81fdf8d7dde4b824dff0260c38f0936b4ed4e28b2cb2d147ef34af289757c6"},"schema_version":"1.0"},"canonical_sha256":"6e1b404e016cc89d84b9d2fe5f81cd5dda25a56e17c1dfc96b4de554a845acbe","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:43:18.092556Z","signature_b64":"g4dSotXhvCUxdpeMUGwL61whJsBZiMLJb1d2mu+P1m9na1v2411JLWChN7/LHAw/U8X0R2+mw8vbndlP2KTiDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6e1b404e016cc89d84b9d2fe5f81cd5dda25a56e17c1dfc96b4de554a845acbe","last_reissued_at":"2026-07-05T09:43:18.092058Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:43:18.092058Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2412.01720","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":"2026-07-05T09:43:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NDGnjdTrVY+/u9bzsCnpWFdthR2Vl5XnccvdYfeWWNA4K8yaHaFvXyWuzxuZhpCT6ZcMf0q+szzahN4ArQ55Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T06:23:52.184368Z"},"content_sha256":"21446ffd0baed9c5ea6a931b406e25484352ae2dbdd40ae1fc5e6caeb613a665","schema_version":"1.0","event_id":"sha256:21446ffd0baed9c5ea6a931b406e25484352ae2dbdd40ae1fc5e6caeb613a665"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:NYNUATQBNTEJ3BFZ2L7F7AONLX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"LamRA: Large Multimodal Model as Your Advanced Retrieval Assistant","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jiangchao Yao, Jiayin Cai, Pingan Chen, Weidi Xie, Xiaolong Jiang, Yanfeng Wang, Yao Hu, Yikun Liu","submitted_at":"2024-12-02T17:10:16Z","abstract_excerpt":"With the rapid advancement of multimodal information retrieval, increasingly complex retrieval tasks have emerged. Existing methods predominately rely on task-specific fine-tuning of vision-language models, often those trained with image-text contrastive learning. In this paper, we explore the possibility of re-purposing generative Large Multimodal Models (LMMs) for retrieval. This approach enables unifying all retrieval tasks under the same formulation and, more importantly, allows for extrapolation towards unseen retrieval tasks without additional training. Our contributions can be summarise"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.01720","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/2412.01720/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":"2026-07-05T09:43:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8oIrjXbX8RchTWEYaWtIDrd+o6XGYH3GcS/ZYx1qapZmc65CsxUo1Aq6+jbMQ5V2WEUX31FSQ/2seBdpE5l3CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T06:23:52.184984Z"},"content_sha256":"838a9bead44c8bc893cf99b3ae8fcf5d0ccbf110cb4f51782abdf4e8fc7e5ad3","schema_version":"1.0","event_id":"sha256:838a9bead44c8bc893cf99b3ae8fcf5d0ccbf110cb4f51782abdf4e8fc7e5ad3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NYNUATQBNTEJ3BFZ2L7F7AONLX/bundle.json","state_url":"https://pith.science/pith/NYNUATQBNTEJ3BFZ2L7F7AONLX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NYNUATQBNTEJ3BFZ2L7F7AONLX/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":"2026-08-10T06:23:52Z","links":{"resolver":"https://pith.science/pith/NYNUATQBNTEJ3BFZ2L7F7AONLX","bundle":"https://pith.science/pith/NYNUATQBNTEJ3BFZ2L7F7AONLX/bundle.json","state":"https://pith.science/pith/NYNUATQBNTEJ3BFZ2L7F7AONLX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NYNUATQBNTEJ3BFZ2L7F7AONLX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:NYNUATQBNTEJ3BFZ2L7F7AONLX","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":"9e81fdf8d7dde4b824dff0260c38f0936b4ed4e28b2cb2d147ef34af289757c6","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-02T17:10:16Z","title_canon_sha256":"7012e022c3f1629311d2f6847895e6108ef01de5193765301208509c0d0cc8f1"},"schema_version":"1.0","source":{"id":"2412.01720","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.01720","created_at":"2026-07-05T09:43:18Z"},{"alias_kind":"arxiv_version","alias_value":"2412.01720v1","created_at":"2026-07-05T09:43:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.01720","created_at":"2026-07-05T09:43:18Z"},{"alias_kind":"pith_short_12","alias_value":"NYNUATQBNTEJ","created_at":"2026-07-05T09:43:18Z"},{"alias_kind":"pith_short_16","alias_value":"NYNUATQBNTEJ3BFZ","created_at":"2026-07-05T09:43:18Z"},{"alias_kind":"pith_short_8","alias_value":"NYNUATQB","created_at":"2026-07-05T09:43:18Z"}],"graph_snapshots":[{"event_id":"sha256:838a9bead44c8bc893cf99b3ae8fcf5d0ccbf110cb4f51782abdf4e8fc7e5ad3","target":"graph","created_at":"2026-07-05T09:43:18Z","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/2412.01720/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"With the rapid advancement of multimodal information retrieval, increasingly complex retrieval tasks have emerged. Existing methods predominately rely on task-specific fine-tuning of vision-language models, often those trained with image-text contrastive learning. In this paper, we explore the possibility of re-purposing generative Large Multimodal Models (LMMs) for retrieval. This approach enables unifying all retrieval tasks under the same formulation and, more importantly, allows for extrapolation towards unseen retrieval tasks without additional training. Our contributions can be summarise","authors_text":"Jiangchao Yao, Jiayin Cai, Pingan Chen, Weidi Xie, Xiaolong Jiang, Yanfeng Wang, Yao Hu, Yikun Liu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-02T17:10:16Z","title":"LamRA: Large Multimodal Model as Your Advanced Retrieval Assistant"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.01720","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":"sha256:21446ffd0baed9c5ea6a931b406e25484352ae2dbdd40ae1fc5e6caeb613a665","target":"record","created_at":"2026-07-05T09:43:18Z","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":"9e81fdf8d7dde4b824dff0260c38f0936b4ed4e28b2cb2d147ef34af289757c6","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-02T17:10:16Z","title_canon_sha256":"7012e022c3f1629311d2f6847895e6108ef01de5193765301208509c0d0cc8f1"},"schema_version":"1.0","source":{"id":"2412.01720","kind":"arxiv","version":1}},"canonical_sha256":"6e1b404e016cc89d84b9d2fe5f81cd5dda25a56e17c1dfc96b4de554a845acbe","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6e1b404e016cc89d84b9d2fe5f81cd5dda25a56e17c1dfc96b4de554a845acbe","first_computed_at":"2026-07-05T09:43:18.092058Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:43:18.092058Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"g4dSotXhvCUxdpeMUGwL61whJsBZiMLJb1d2mu+P1m9na1v2411JLWChN7/LHAw/U8X0R2+mw8vbndlP2KTiDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:43:18.092556Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.01720","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:21446ffd0baed9c5ea6a931b406e25484352ae2dbdd40ae1fc5e6caeb613a665","sha256:838a9bead44c8bc893cf99b3ae8fcf5d0ccbf110cb4f51782abdf4e8fc7e5ad3"],"state_sha256":"8cb4b3963bcea9b10e21d7be82778f07358889458a6f0ef1f43d9ecb00ca7727"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Uuzj1p/6QkAN4iPY1cMOMn8K1ag+/nwd7xDYdYUk7VP5ElT7/dlxLYWMckO7fpLjt7gcZw5UlGw5IA+Gd7rgAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T06:23:52.190398Z","bundle_sha256":"a6e211ed095db8347dfba7c231cc0692dd77e511b70295c76b4a79f1662d841c"}}