{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:IW4DIF3N2FH6BT743QHCRFO5P7","short_pith_number":"pith:IW4DIF3N","canonical_record":{"source":{"id":"2401.14856","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-01-26T13:36:12Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"39178a08d3033fa3760e0d153be2378d59da1f77be08b6eed0494e356aa374a6","abstract_canon_sha256":"e3ceb97388c0d52945742f69e68a77c1775eba099ba5436a8d2c8485fe688474"},"schema_version":"1.0"},"canonical_sha256":"45b834176dd14fe0cffcdc0e2895dd7ff8288c3fb3c2151b055524cb64c04552","source":{"kind":"arxiv","id":"2401.14856","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.14856","created_at":"2026-07-05T07:38:02Z"},{"alias_kind":"arxiv_version","alias_value":"2401.14856v1","created_at":"2026-07-05T07:38:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.14856","created_at":"2026-07-05T07:38:02Z"},{"alias_kind":"pith_short_12","alias_value":"IW4DIF3N2FH6","created_at":"2026-07-05T07:38:02Z"},{"alias_kind":"pith_short_16","alias_value":"IW4DIF3N2FH6BT74","created_at":"2026-07-05T07:38:02Z"},{"alias_kind":"pith_short_8","alias_value":"IW4DIF3N","created_at":"2026-07-05T07:38:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:IW4DIF3N2FH6BT743QHCRFO5P7","target":"record","payload":{"canonical_record":{"source":{"id":"2401.14856","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-01-26T13:36:12Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"39178a08d3033fa3760e0d153be2378d59da1f77be08b6eed0494e356aa374a6","abstract_canon_sha256":"e3ceb97388c0d52945742f69e68a77c1775eba099ba5436a8d2c8485fe688474"},"schema_version":"1.0"},"canonical_sha256":"45b834176dd14fe0cffcdc0e2895dd7ff8288c3fb3c2151b055524cb64c04552","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:38:02.792156Z","signature_b64":"EuQGboSDza1Bw+IjWASF9PYiPlj5yBFGVvdz2e4+R13fFsMfgSoixLfflzLYVKMY856edOJL/GS6GvukM5adAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"45b834176dd14fe0cffcdc0e2895dd7ff8288c3fb3c2151b055524cb64c04552","last_reissued_at":"2026-07-05T07:38:02.791705Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:38:02.791705Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2401.14856","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":"2026-07-05T07:38:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7PhOXHhFM0rt38C00tPdSwa5vJ1jQ0nKTUH5buS4ZEK52VHd7XcX/WCMYMPmgSZ4UcTbEE3r4U62kxlZlsHHCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T17:17:36.628694Z"},"content_sha256":"7b2e7b89c07f2c8d00160ac072050399401883bb4b79e652cfcf792dead933d1","schema_version":"1.0","event_id":"sha256:7b2e7b89c07f2c8d00160ac072050399401883bb4b79e652cfcf792dead933d1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:IW4DIF3N2FH6BT743QHCRFO5P7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Memory-Inspired Temporal Prompt Interaction for Text-Image Classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Hao Sun, Lanfen Lin, Rui Qin, Xinyao Yu, Yen-Wei Chen, Zhenjia Bai, Ziwei Niu","submitted_at":"2024-01-26T13:36:12Z","abstract_excerpt":"In recent years, large-scale pre-trained multimodal models (LMM) generally emerge to integrate the vision and language modalities, achieving considerable success in various natural language processing and computer vision tasks. The growing size of LMMs, however, results in a significant computational cost for fine-tuning these models for downstream tasks. Hence, prompt-based interaction strategy is studied to align modalities more efficiently. In this contex, we propose a novel prompt-based multimodal interaction strategy inspired by human memory strategy, namely Memory-Inspired Temporal Promp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.14856","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/2401.14856/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":"2026-07-05T07:38:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7owaYwVJ17s0El5domaD6Y5zfrUa0JJcGPBPw8acNJ5ZJtDoub4b8yW2v4ZXFZjlfqFrFUaEz6hMLpPaV6XTAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T17:17:36.629660Z"},"content_sha256":"8a91f3fd062aff384d3ed1790e71842526d35d0c098e8e2a9fbf7c53fffb4bf2","schema_version":"1.0","event_id":"sha256:8a91f3fd062aff384d3ed1790e71842526d35d0c098e8e2a9fbf7c53fffb4bf2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IW4DIF3N2FH6BT743QHCRFO5P7/bundle.json","state_url":"https://pith.science/pith/IW4DIF3N2FH6BT743QHCRFO5P7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IW4DIF3N2FH6BT743QHCRFO5P7/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":"2026-08-07T17:17:36Z","links":{"resolver":"https://pith.science/pith/IW4DIF3N2FH6BT743QHCRFO5P7","bundle":"https://pith.science/pith/IW4DIF3N2FH6BT743QHCRFO5P7/bundle.json","state":"https://pith.science/pith/IW4DIF3N2FH6BT743QHCRFO5P7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IW4DIF3N2FH6BT743QHCRFO5P7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:IW4DIF3N2FH6BT743QHCRFO5P7","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":"e3ceb97388c0d52945742f69e68a77c1775eba099ba5436a8d2c8485fe688474","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-01-26T13:36:12Z","title_canon_sha256":"39178a08d3033fa3760e0d153be2378d59da1f77be08b6eed0494e356aa374a6"},"schema_version":"1.0","source":{"id":"2401.14856","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.14856","created_at":"2026-07-05T07:38:02Z"},{"alias_kind":"arxiv_version","alias_value":"2401.14856v1","created_at":"2026-07-05T07:38:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.14856","created_at":"2026-07-05T07:38:02Z"},{"alias_kind":"pith_short_12","alias_value":"IW4DIF3N2FH6","created_at":"2026-07-05T07:38:02Z"},{"alias_kind":"pith_short_16","alias_value":"IW4DIF3N2FH6BT74","created_at":"2026-07-05T07:38:02Z"},{"alias_kind":"pith_short_8","alias_value":"IW4DIF3N","created_at":"2026-07-05T07:38:02Z"}],"graph_snapshots":[{"event_id":"sha256:8a91f3fd062aff384d3ed1790e71842526d35d0c098e8e2a9fbf7c53fffb4bf2","target":"graph","created_at":"2026-07-05T07:38:02Z","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/2401.14856/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In recent years, large-scale pre-trained multimodal models (LMM) generally emerge to integrate the vision and language modalities, achieving considerable success in various natural language processing and computer vision tasks. The growing size of LMMs, however, results in a significant computational cost for fine-tuning these models for downstream tasks. Hence, prompt-based interaction strategy is studied to align modalities more efficiently. In this contex, we propose a novel prompt-based multimodal interaction strategy inspired by human memory strategy, namely Memory-Inspired Temporal Promp","authors_text":"Hao Sun, Lanfen Lin, Rui Qin, Xinyao Yu, Yen-Wei Chen, Zhenjia Bai, Ziwei Niu","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-01-26T13:36:12Z","title":"Memory-Inspired Temporal Prompt Interaction for Text-Image Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.14856","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":"sha256:7b2e7b89c07f2c8d00160ac072050399401883bb4b79e652cfcf792dead933d1","target":"record","created_at":"2026-07-05T07:38:02Z","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":"e3ceb97388c0d52945742f69e68a77c1775eba099ba5436a8d2c8485fe688474","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-01-26T13:36:12Z","title_canon_sha256":"39178a08d3033fa3760e0d153be2378d59da1f77be08b6eed0494e356aa374a6"},"schema_version":"1.0","source":{"id":"2401.14856","kind":"arxiv","version":1}},"canonical_sha256":"45b834176dd14fe0cffcdc0e2895dd7ff8288c3fb3c2151b055524cb64c04552","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"45b834176dd14fe0cffcdc0e2895dd7ff8288c3fb3c2151b055524cb64c04552","first_computed_at":"2026-07-05T07:38:02.791705Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:38:02.791705Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EuQGboSDza1Bw+IjWASF9PYiPlj5yBFGVvdz2e4+R13fFsMfgSoixLfflzLYVKMY856edOJL/GS6GvukM5adAw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:38:02.792156Z","signed_message":"canonical_sha256_bytes"},"source_id":"2401.14856","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7b2e7b89c07f2c8d00160ac072050399401883bb4b79e652cfcf792dead933d1","sha256:8a91f3fd062aff384d3ed1790e71842526d35d0c098e8e2a9fbf7c53fffb4bf2"],"state_sha256":"8ffa592b231306ed51d80fedb75fa5a983dc451411012bd4c8f8dada9dfb3734"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bZBQuChKjK2bRmscaMzdV1gudqX+wwPszPa0HMUDrQ9W0OB0bIkpiPb+eRqfHanNDIV9jpT9vXnFpvxXaxH0Cw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T17:17:36.637158Z","bundle_sha256":"976415c75a7536f2923b5a005edb917a0b2174fcfbdb93281bf3449557c76286"}}