{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:Q2XFY7XIG5AVFRPV2D5F2DJGZL","short_pith_number":"pith:Q2XFY7XI","schema_version":"1.0","canonical_sha256":"86ae5c7ee8374152c5f5d0fa5d0d26cafda49cfa2a92ed51dfabc55fb2788443","source":{"kind":"arxiv","id":"2502.11453","version":1},"attestation_state":"computed","paper":{"title":"Connector-S: A Survey of Connectors in Multi-modal Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Ji Wu, Miao Li, Xi Chen, Xun Zhu, Yiming Shi, Zheng Zhang","submitted_at":"2025-02-17T05:28:04Z","abstract_excerpt":"With the rapid advancements in multi-modal large language models (MLLMs), connectors play a pivotal role in bridging diverse modalities and enhancing model performance. However, the design and evolution of connectors have not been comprehensively analyzed, leaving gaps in understanding how these components function and hindering the development of more powerful connectors. In this survey, we systematically review the current progress of connectors in MLLMs and present a structured taxonomy that categorizes connectors into atomic operations (mapping, compression, mixture of experts) and holisti"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2502.11453","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-02-17T05:28:04Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"c01a474801f5e747cbe09ffa4609162948cc2e786b8bd65b3d2aae5913b9b3b1","abstract_canon_sha256":"9e8c2aa236ca8e60aa74b000b7d7fd3344348964db2ba4ee16fc62908e1fe1e4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:15:28.878460Z","signature_b64":"uqqqgMt9+Kjwjyn74PT8ryJl0U2IinW1Y3pNA0KoTZNtxglmAjE6WNNhj/f+4PtintsBFO9Y9mJZKSKKa+j5DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"86ae5c7ee8374152c5f5d0fa5d0d26cafda49cfa2a92ed51dfabc55fb2788443","last_reissued_at":"2026-07-05T10:15:28.877882Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:15:28.877882Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Connector-S: A Survey of Connectors in Multi-modal Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Ji Wu, Miao Li, Xi Chen, Xun Zhu, Yiming Shi, Zheng Zhang","submitted_at":"2025-02-17T05:28:04Z","abstract_excerpt":"With the rapid advancements in multi-modal large language models (MLLMs), connectors play a pivotal role in bridging diverse modalities and enhancing model performance. However, the design and evolution of connectors have not been comprehensively analyzed, leaving gaps in understanding how these components function and hindering the development of more powerful connectors. In this survey, we systematically review the current progress of connectors in MLLMs and present a structured taxonomy that categorizes connectors into atomic operations (mapping, compression, mixture of experts) and holisti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.11453","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/2502.11453/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2502.11453","created_at":"2026-07-05T10:15:28.877949+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.11453v1","created_at":"2026-07-05T10:15:28.877949+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.11453","created_at":"2026-07-05T10:15:28.877949+00:00"},{"alias_kind":"pith_short_12","alias_value":"Q2XFY7XIG5AV","created_at":"2026-07-05T10:15:28.877949+00:00"},{"alias_kind":"pith_short_16","alias_value":"Q2XFY7XIG5AVFRPV","created_at":"2026-07-05T10:15:28.877949+00:00"},{"alias_kind":"pith_short_8","alias_value":"Q2XFY7XI","created_at":"2026-07-05T10:15:28.877949+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2510.21828","citing_title":"Structured and Abstractive Reasoning on Multi-modal Relational Knowledge Images","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2604.21027","citing_title":"HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering","ref_index":141,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Q2XFY7XIG5AVFRPV2D5F2DJGZL","json":"https://pith.science/pith/Q2XFY7XIG5AVFRPV2D5F2DJGZL.json","graph_json":"https://pith.science/api/pith-number/Q2XFY7XIG5AVFRPV2D5F2DJGZL/graph.json","events_json":"https://pith.science/api/pith-number/Q2XFY7XIG5AVFRPV2D5F2DJGZL/events.json","paper":"https://pith.science/paper/Q2XFY7XI"},"agent_actions":{"view_html":"https://pith.science/pith/Q2XFY7XIG5AVFRPV2D5F2DJGZL","download_json":"https://pith.science/pith/Q2XFY7XIG5AVFRPV2D5F2DJGZL.json","view_paper":"https://pith.science/paper/Q2XFY7XI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.11453&json=true","fetch_graph":"https://pith.science/api/pith-number/Q2XFY7XIG5AVFRPV2D5F2DJGZL/graph.json","fetch_events":"https://pith.science/api/pith-number/Q2XFY7XIG5AVFRPV2D5F2DJGZL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Q2XFY7XIG5AVFRPV2D5F2DJGZL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Q2XFY7XIG5AVFRPV2D5F2DJGZL/action/storage_attestation","attest_author":"https://pith.science/pith/Q2XFY7XIG5AVFRPV2D5F2DJGZL/action/author_attestation","sign_citation":"https://pith.science/pith/Q2XFY7XIG5AVFRPV2D5F2DJGZL/action/citation_signature","submit_replication":"https://pith.science/pith/Q2XFY7XIG5AVFRPV2D5F2DJGZL/action/replication_record"}},"created_at":"2026-07-05T10:15:28.877949+00:00","updated_at":"2026-07-05T10:15:28.877949+00:00"}