{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:6DZO3RQQKUX7SR4Z5JTPVVV5XI","short_pith_number":"pith:6DZO3RQQ","schema_version":"1.0","canonical_sha256":"f0f2edc610552ff94799ea66fad6bdba2f2bbface428c7e7853beb2573506c49","source":{"kind":"arxiv","id":"2412.09875","version":1},"attestation_state":"computed","paper":{"title":"Selective State Space Memory for Large Vision-Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chee Ng, Yuen Fung","submitted_at":"2024-12-13T05:40:50Z","abstract_excerpt":"Large Vision-Language Models (LVLMs) have demonstrated remarkable performance across a wide range of multimodal tasks. However, fine-tuning these models for domain-specific applications remains a computationally intensive challenge. This paper introduces State Space Memory Integration (SSMI), a novel approach for efficient fine-tuning of LVLMs. By integrating lightweight Mamba-based state space modules into the LVLM architecture, SSMI captures long-range dependencies and injects task-specific visual and sequential patterns effectively. Unlike traditional fine-tuning methods, SSMI requires only"},"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":"2412.09875","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-13T05:40:50Z","cross_cats_sorted":[],"title_canon_sha256":"ab9d4c937759321103e26d658a5ea104886556e0ef7ad1c20b355723f69bff4b","abstract_canon_sha256":"2257196e2c8aec27b7abcda0844e0e8ce79dc249cd62310e3e5ce6a17440fc36"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:48:43.186162Z","signature_b64":"USwQwD9ls/vfhev0vRHWxjqUlUGK8IlFMF3xIUfuHbRrxMoDxMUSf07mELzy53SJXowdlXDH4Iws91oZOBHJDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f0f2edc610552ff94799ea66fad6bdba2f2bbface428c7e7853beb2573506c49","last_reissued_at":"2026-07-05T09:48:43.185609Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:48:43.185609Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Selective State Space Memory for Large Vision-Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chee Ng, Yuen Fung","submitted_at":"2024-12-13T05:40:50Z","abstract_excerpt":"Large Vision-Language Models (LVLMs) have demonstrated remarkable performance across a wide range of multimodal tasks. However, fine-tuning these models for domain-specific applications remains a computationally intensive challenge. This paper introduces State Space Memory Integration (SSMI), a novel approach for efficient fine-tuning of LVLMs. By integrating lightweight Mamba-based state space modules into the LVLM architecture, SSMI captures long-range dependencies and injects task-specific visual and sequential patterns effectively. Unlike traditional fine-tuning methods, SSMI requires only"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.09875","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/2412.09875/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":"2412.09875","created_at":"2026-07-05T09:48:43.185689+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.09875v1","created_at":"2026-07-05T09:48:43.185689+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.09875","created_at":"2026-07-05T09:48:43.185689+00:00"},{"alias_kind":"pith_short_12","alias_value":"6DZO3RQQKUX7","created_at":"2026-07-05T09:48:43.185689+00:00"},{"alias_kind":"pith_short_16","alias_value":"6DZO3RQQKUX7SR4Z","created_at":"2026-07-05T09:48:43.185689+00:00"},{"alias_kind":"pith_short_8","alias_value":"6DZO3RQQ","created_at":"2026-07-05T09:48:43.185689+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6DZO3RQQKUX7SR4Z5JTPVVV5XI","json":"https://pith.science/pith/6DZO3RQQKUX7SR4Z5JTPVVV5XI.json","graph_json":"https://pith.science/api/pith-number/6DZO3RQQKUX7SR4Z5JTPVVV5XI/graph.json","events_json":"https://pith.science/api/pith-number/6DZO3RQQKUX7SR4Z5JTPVVV5XI/events.json","paper":"https://pith.science/paper/6DZO3RQQ"},"agent_actions":{"view_html":"https://pith.science/pith/6DZO3RQQKUX7SR4Z5JTPVVV5XI","download_json":"https://pith.science/pith/6DZO3RQQKUX7SR4Z5JTPVVV5XI.json","view_paper":"https://pith.science/paper/6DZO3RQQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.09875&json=true","fetch_graph":"https://pith.science/api/pith-number/6DZO3RQQKUX7SR4Z5JTPVVV5XI/graph.json","fetch_events":"https://pith.science/api/pith-number/6DZO3RQQKUX7SR4Z5JTPVVV5XI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6DZO3RQQKUX7SR4Z5JTPVVV5XI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6DZO3RQQKUX7SR4Z5JTPVVV5XI/action/storage_attestation","attest_author":"https://pith.science/pith/6DZO3RQQKUX7SR4Z5JTPVVV5XI/action/author_attestation","sign_citation":"https://pith.science/pith/6DZO3RQQKUX7SR4Z5JTPVVV5XI/action/citation_signature","submit_replication":"https://pith.science/pith/6DZO3RQQKUX7SR4Z5JTPVVV5XI/action/replication_record"}},"created_at":"2026-07-05T09:48:43.185689+00:00","updated_at":"2026-07-05T09:48:43.185689+00:00"}