{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:F65PK4MXD7PBXA53TNMN2DMW6Y","short_pith_number":"pith:F65PK4MX","canonical_record":{"source":{"id":"2605.26941","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2026-05-26T12:31:48Z","cross_cats_sorted":["cs.MM"],"title_canon_sha256":"e530a6ba52d9b2d27f1b12522b34fd40aa71a40f11384c57df80b929ca91a434","abstract_canon_sha256":"a16b0c56783804b072fab3a86d10e6203b456a68a0be807ed91fd9e8822172c2"},"schema_version":"1.0"},"canonical_sha256":"2fbaf571971fde1b83bb9b58dd0d96f624d6963c84673009b4b7a44bdb739a14","source":{"kind":"arxiv","id":"2605.26941","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.26941","created_at":"2026-05-27T01:06:20Z"},{"alias_kind":"arxiv_version","alias_value":"2605.26941v1","created_at":"2026-05-27T01:06:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.26941","created_at":"2026-05-27T01:06:20Z"},{"alias_kind":"pith_short_12","alias_value":"F65PK4MXD7PB","created_at":"2026-05-27T01:06:20Z"},{"alias_kind":"pith_short_16","alias_value":"F65PK4MXD7PBXA53","created_at":"2026-05-27T01:06:20Z"},{"alias_kind":"pith_short_8","alias_value":"F65PK4MX","created_at":"2026-05-27T01:06:20Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:F65PK4MXD7PBXA53TNMN2DMW6Y","target":"record","payload":{"canonical_record":{"source":{"id":"2605.26941","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2026-05-26T12:31:48Z","cross_cats_sorted":["cs.MM"],"title_canon_sha256":"e530a6ba52d9b2d27f1b12522b34fd40aa71a40f11384c57df80b929ca91a434","abstract_canon_sha256":"a16b0c56783804b072fab3a86d10e6203b456a68a0be807ed91fd9e8822172c2"},"schema_version":"1.0"},"canonical_sha256":"2fbaf571971fde1b83bb9b58dd0d96f624d6963c84673009b4b7a44bdb739a14","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-27T01:06:20.876979Z","signature_b64":"9fPYMOYCkRP9qjJTsswLeCw+w/PwclWaKPjlEFZzL69xr9RJbzoEWdLGUTALmgaGaYPGiMWWMRydppCbH4qlBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2fbaf571971fde1b83bb9b58dd0d96f624d6963c84673009b4b7a44bdb739a14","last_reissued_at":"2026-05-27T01:06:20.876460Z","signature_status":"signed_v1","first_computed_at":"2026-05-27T01:06:20.876460Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.26941","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":"2026-05-27T01:06:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qbLx/RvfJ9+TPQJyRoz0pqmigDCd/WODPWMJd2dIaa82iFJP8eNFGI+3I1xJx7a+G5f4l0hC9lYEsHE0hPalBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T00:36:41.101950Z"},"content_sha256":"b9aa7d761f35cc05248f18cfcbac3ff9a69b11729d68423e6da2fd59844c0b82","schema_version":"1.0","event_id":"sha256:b9aa7d761f35cc05248f18cfcbac3ff9a69b11729d68423e6da2fd59844c0b82"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:F65PK4MXD7PBXA53TNMN2DMW6Y","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"The 2nd EReL@MIR Workshop on Efficient Representation Learning for Multimodal Information Retrieval","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MM"],"primary_cat":"cs.IR","authors_text":"Alexandros Karatzoglou, Ioannis Arapakis, Joemon M. Jose, Junchen Fu, Qian Li, Qijiong Liu, Xin Xin, Xi Wang, Xuri Ge","submitted_at":"2026-05-26T12:31:48Z","abstract_excerpt":"Multimodal representation learning has attracted increasing attention in AI, driven by the strong performance of large, pretrained multimodal foundation models such as Qwen, LLaVA, and CLIP. These models deliver impressive performance on a range of multimodal information retrieval (MIR) tasks, including web search, cross-modal retrieval, and recommender systems. Yet their massive parameter counts create major efficiency bottlenecks when adapting their representations for IR tasks during training, deployment, and inference. These limitations hinder the practical use of foundation models for rep"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.26941","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/2605.26941/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":"2026-05-27T01:06:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fOa7rB5N4FU++8gMuhjioU2Y3n2PmtNX51a5Ymuf75QN/hZxQPfEP5bQmZeg+A3Ik4bLV3KCSWRB2zu8g4w+Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T00:36:41.102335Z"},"content_sha256":"766bcdb6cb8c38353060797e5bf4849186be4167d21080a8f31fb9ef5d481b92","schema_version":"1.0","event_id":"sha256:766bcdb6cb8c38353060797e5bf4849186be4167d21080a8f31fb9ef5d481b92"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/F65PK4MXD7PBXA53TNMN2DMW6Y/bundle.json","state_url":"https://pith.science/pith/F65PK4MXD7PBXA53TNMN2DMW6Y/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/F65PK4MXD7PBXA53TNMN2DMW6Y/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":"2026-05-28T00:36:41Z","links":{"resolver":"https://pith.science/pith/F65PK4MXD7PBXA53TNMN2DMW6Y","bundle":"https://pith.science/pith/F65PK4MXD7PBXA53TNMN2DMW6Y/bundle.json","state":"https://pith.science/pith/F65PK4MXD7PBXA53TNMN2DMW6Y/state.json","well_known_bundle":"https://pith.science/.well-known/pith/F65PK4MXD7PBXA53TNMN2DMW6Y/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:F65PK4MXD7PBXA53TNMN2DMW6Y","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":"a16b0c56783804b072fab3a86d10e6203b456a68a0be807ed91fd9e8822172c2","cross_cats_sorted":["cs.MM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2026-05-26T12:31:48Z","title_canon_sha256":"e530a6ba52d9b2d27f1b12522b34fd40aa71a40f11384c57df80b929ca91a434"},"schema_version":"1.0","source":{"id":"2605.26941","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.26941","created_at":"2026-05-27T01:06:20Z"},{"alias_kind":"arxiv_version","alias_value":"2605.26941v1","created_at":"2026-05-27T01:06:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.26941","created_at":"2026-05-27T01:06:20Z"},{"alias_kind":"pith_short_12","alias_value":"F65PK4MXD7PB","created_at":"2026-05-27T01:06:20Z"},{"alias_kind":"pith_short_16","alias_value":"F65PK4MXD7PBXA53","created_at":"2026-05-27T01:06:20Z"},{"alias_kind":"pith_short_8","alias_value":"F65PK4MX","created_at":"2026-05-27T01:06:20Z"}],"graph_snapshots":[{"event_id":"sha256:766bcdb6cb8c38353060797e5bf4849186be4167d21080a8f31fb9ef5d481b92","target":"graph","created_at":"2026-05-27T01:06:20Z","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/2605.26941/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multimodal representation learning has attracted increasing attention in AI, driven by the strong performance of large, pretrained multimodal foundation models such as Qwen, LLaVA, and CLIP. These models deliver impressive performance on a range of multimodal information retrieval (MIR) tasks, including web search, cross-modal retrieval, and recommender systems. Yet their massive parameter counts create major efficiency bottlenecks when adapting their representations for IR tasks during training, deployment, and inference. These limitations hinder the practical use of foundation models for rep","authors_text":"Alexandros Karatzoglou, Ioannis Arapakis, Joemon M. Jose, Junchen Fu, Qian Li, Qijiong Liu, Xin Xin, Xi Wang, Xuri Ge","cross_cats":["cs.MM"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2026-05-26T12:31:48Z","title":"The 2nd EReL@MIR Workshop on Efficient Representation Learning for Multimodal Information Retrieval"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.26941","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:b9aa7d761f35cc05248f18cfcbac3ff9a69b11729d68423e6da2fd59844c0b82","target":"record","created_at":"2026-05-27T01:06:20Z","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":"a16b0c56783804b072fab3a86d10e6203b456a68a0be807ed91fd9e8822172c2","cross_cats_sorted":["cs.MM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2026-05-26T12:31:48Z","title_canon_sha256":"e530a6ba52d9b2d27f1b12522b34fd40aa71a40f11384c57df80b929ca91a434"},"schema_version":"1.0","source":{"id":"2605.26941","kind":"arxiv","version":1}},"canonical_sha256":"2fbaf571971fde1b83bb9b58dd0d96f624d6963c84673009b4b7a44bdb739a14","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2fbaf571971fde1b83bb9b58dd0d96f624d6963c84673009b4b7a44bdb739a14","first_computed_at":"2026-05-27T01:06:20.876460Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-27T01:06:20.876460Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9fPYMOYCkRP9qjJTsswLeCw+w/PwclWaKPjlEFZzL69xr9RJbzoEWdLGUTALmgaGaYPGiMWWMRydppCbH4qlBA==","signature_status":"signed_v1","signed_at":"2026-05-27T01:06:20.876979Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.26941","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b9aa7d761f35cc05248f18cfcbac3ff9a69b11729d68423e6da2fd59844c0b82","sha256:766bcdb6cb8c38353060797e5bf4849186be4167d21080a8f31fb9ef5d481b92"],"state_sha256":"3347300b55785e263dc898b2249e27bca6e0e57dd03e394237a7f2c8f1a684ff"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hS/AhPPYKsJqeZznKFCgAGvGQKqtrbz8pOoAVe1+M+fuPC1XFrL0zXk7r9p3qK75JDgC2uiDb7oi+s8P5RqABg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T00:36:41.104328Z","bundle_sha256":"abc47f6129f49476a3679fa80851d3a04f1702b2108edf04e464f7885ddbc7f5"}}