{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:RQO52TLNZJTO7XIVVDMTTBB4ET","short_pith_number":"pith:RQO52TLN","canonical_record":{"source":{"id":"2606.12744","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-10T23:14:45Z","cross_cats_sorted":[],"title_canon_sha256":"17bf33b0cd5b1247f80cf9fc5d52ea6c2efb6c768099c2504888c0f2c70b5614","abstract_canon_sha256":"262afda4abbc3ac75e802cd0db513ca23d830ad995c41d871f4caa19fe67d461"},"schema_version":"1.0"},"canonical_sha256":"8c1ddd4d6dca66efdd15a8d939843c24d3340d3b3caf88cdb177ca473f868d7e","source":{"kind":"arxiv","id":"2606.12744","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.12744","created_at":"2026-06-12T01:08:48Z"},{"alias_kind":"arxiv_version","alias_value":"2606.12744v1","created_at":"2026-06-12T01:08:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.12744","created_at":"2026-06-12T01:08:48Z"},{"alias_kind":"pith_short_12","alias_value":"RQO52TLNZJTO","created_at":"2026-06-12T01:08:48Z"},{"alias_kind":"pith_short_16","alias_value":"RQO52TLNZJTO7XIV","created_at":"2026-06-12T01:08:48Z"},{"alias_kind":"pith_short_8","alias_value":"RQO52TLN","created_at":"2026-06-12T01:08:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:RQO52TLNZJTO7XIVVDMTTBB4ET","target":"record","payload":{"canonical_record":{"source":{"id":"2606.12744","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-10T23:14:45Z","cross_cats_sorted":[],"title_canon_sha256":"17bf33b0cd5b1247f80cf9fc5d52ea6c2efb6c768099c2504888c0f2c70b5614","abstract_canon_sha256":"262afda4abbc3ac75e802cd0db513ca23d830ad995c41d871f4caa19fe67d461"},"schema_version":"1.0"},"canonical_sha256":"8c1ddd4d6dca66efdd15a8d939843c24d3340d3b3caf88cdb177ca473f868d7e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-12T01:08:48.914676Z","signature_b64":"B5Sk6Xg4iBqwO2rqNWQ5N++9ULtH/bk6rJV9aVLkImuu0OK1I9VJv2yHEZvGqKOklcNVLZtOQeW5Z9FBgoQbAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8c1ddd4d6dca66efdd15a8d939843c24d3340d3b3caf88cdb177ca473f868d7e","last_reissued_at":"2026-06-12T01:08:48.913798Z","signature_status":"signed_v1","first_computed_at":"2026-06-12T01:08:48.913798Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.12744","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":"2026-06-12T01:08:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"A/2Q9EFDb/+eGGOHkUoZuKoYq1AcAk+T5SZ7BORCHj55jgAG4mL35sCA20o9z0scK93CxapGpIgPl9UE4hr7DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-02T14:42:50.909123Z"},"content_sha256":"a6970b944caf353b165ee444cc1b4d798d3075870ad24fd17690f91ff2e27239","schema_version":"1.0","event_id":"sha256:a6970b944caf353b165ee444cc1b4d798d3075870ad24fd17690f91ff2e27239"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:RQO52TLNZJTO7XIVVDMTTBB4ET","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"GRIP: Feedback-Guided Prompt Retrieval for Large Multimodal Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Emre Kiciman, Garvita Allabadi, Matteo Sodano, Ranveer Chandra, Roberto Estev\\~ao, Vikram Adve, Yuxiong Wang","submitted_at":"2026-06-10T23:14:45Z","abstract_excerpt":"In-Context Learning (ICL) has become a powerful mechanism for adapting Large Language Models (LLMs) to new tasks without fine-tuning. Extending this concept to Large Multimodal Models (LMMs), Multimodal In-Context Learning (M-ICL) relies on retrieving relevant examples, such as images, captions, or question-answer pairs, to guide predictions across tasks like classification, captioning, and visual question answering (VQA). Most existing approaches select in-context examples based on feature-space similarity, assuming that semantically similar samples provide the most useful context. However, o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.12744","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/2606.12744/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":"2026-06-12T01:08:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"djSsQVlSDtDnhTupqhOl8mvQpDQRzc2vr0pSWt8hyjGj+v9JLER1mHVGatiCXTau+kC4CX7z/Ei/RIXUkAHDAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-02T14:42:50.909523Z"},"content_sha256":"ccb07b03db18d08283f59a2a0291fedca92940097ebc997467bca9c0d61a8b69","schema_version":"1.0","event_id":"sha256:ccb07b03db18d08283f59a2a0291fedca92940097ebc997467bca9c0d61a8b69"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RQO52TLNZJTO7XIVVDMTTBB4ET/bundle.json","state_url":"https://pith.science/pith/RQO52TLNZJTO7XIVVDMTTBB4ET/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RQO52TLNZJTO7XIVVDMTTBB4ET/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":"2026-07-02T14:42:50Z","links":{"resolver":"https://pith.science/pith/RQO52TLNZJTO7XIVVDMTTBB4ET","bundle":"https://pith.science/pith/RQO52TLNZJTO7XIVVDMTTBB4ET/bundle.json","state":"https://pith.science/pith/RQO52TLNZJTO7XIVVDMTTBB4ET/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RQO52TLNZJTO7XIVVDMTTBB4ET/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:RQO52TLNZJTO7XIVVDMTTBB4ET","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":"262afda4abbc3ac75e802cd0db513ca23d830ad995c41d871f4caa19fe67d461","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-10T23:14:45Z","title_canon_sha256":"17bf33b0cd5b1247f80cf9fc5d52ea6c2efb6c768099c2504888c0f2c70b5614"},"schema_version":"1.0","source":{"id":"2606.12744","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.12744","created_at":"2026-06-12T01:08:48Z"},{"alias_kind":"arxiv_version","alias_value":"2606.12744v1","created_at":"2026-06-12T01:08:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.12744","created_at":"2026-06-12T01:08:48Z"},{"alias_kind":"pith_short_12","alias_value":"RQO52TLNZJTO","created_at":"2026-06-12T01:08:48Z"},{"alias_kind":"pith_short_16","alias_value":"RQO52TLNZJTO7XIV","created_at":"2026-06-12T01:08:48Z"},{"alias_kind":"pith_short_8","alias_value":"RQO52TLN","created_at":"2026-06-12T01:08:48Z"}],"graph_snapshots":[{"event_id":"sha256:ccb07b03db18d08283f59a2a0291fedca92940097ebc997467bca9c0d61a8b69","target":"graph","created_at":"2026-06-12T01:08:48Z","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/2606.12744/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In-Context Learning (ICL) has become a powerful mechanism for adapting Large Language Models (LLMs) to new tasks without fine-tuning. Extending this concept to Large Multimodal Models (LMMs), Multimodal In-Context Learning (M-ICL) relies on retrieving relevant examples, such as images, captions, or question-answer pairs, to guide predictions across tasks like classification, captioning, and visual question answering (VQA). Most existing approaches select in-context examples based on feature-space similarity, assuming that semantically similar samples provide the most useful context. However, o","authors_text":"Emre Kiciman, Garvita Allabadi, Matteo Sodano, Ranveer Chandra, Roberto Estev\\~ao, Vikram Adve, Yuxiong Wang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-10T23:14:45Z","title":"GRIP: Feedback-Guided Prompt Retrieval for Large Multimodal Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.12744","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:a6970b944caf353b165ee444cc1b4d798d3075870ad24fd17690f91ff2e27239","target":"record","created_at":"2026-06-12T01:08:48Z","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":"262afda4abbc3ac75e802cd0db513ca23d830ad995c41d871f4caa19fe67d461","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-10T23:14:45Z","title_canon_sha256":"17bf33b0cd5b1247f80cf9fc5d52ea6c2efb6c768099c2504888c0f2c70b5614"},"schema_version":"1.0","source":{"id":"2606.12744","kind":"arxiv","version":1}},"canonical_sha256":"8c1ddd4d6dca66efdd15a8d939843c24d3340d3b3caf88cdb177ca473f868d7e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8c1ddd4d6dca66efdd15a8d939843c24d3340d3b3caf88cdb177ca473f868d7e","first_computed_at":"2026-06-12T01:08:48.913798Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-12T01:08:48.913798Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"B5Sk6Xg4iBqwO2rqNWQ5N++9ULtH/bk6rJV9aVLkImuu0OK1I9VJv2yHEZvGqKOklcNVLZtOQeW5Z9FBgoQbAw==","signature_status":"signed_v1","signed_at":"2026-06-12T01:08:48.914676Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.12744","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a6970b944caf353b165ee444cc1b4d798d3075870ad24fd17690f91ff2e27239","sha256:ccb07b03db18d08283f59a2a0291fedca92940097ebc997467bca9c0d61a8b69"],"state_sha256":"8417b82c8872c92241a88444f8a52f8ae197173db1f8a433fe878c0e9afa0110"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YDGBe/KWm+wk+GbFyM3TT4vubZ+08FrO3qOZ04F308prYWBC5oS7/ryg0BTKZm/Xtn57ltr1vYJFxYjqOqlvAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-02T14:42:50.911450Z","bundle_sha256":"d526bb6957aefe5f733d1b1642019e892d4bd252133be169ab36cd078d7cc07b"}}