{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:NBGFPKUFKVVVHUFWLWNBWW7LXP","short_pith_number":"pith:NBGFPKUF","canonical_record":{"source":{"id":"2410.02458","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2024-10-03T14:50:33Z","cross_cats_sorted":["cs.CL","cs.CV"],"title_canon_sha256":"1807ff6eaef25b41251654e961e2188954eef51a34f0c9e67b2f418c80e1e6fb","abstract_canon_sha256":"ba5e801a5d3fbfafb4b686ae631316976e03466afcfba9eacfaceb3c20ef1e3a"},"schema_version":"1.0"},"canonical_sha256":"684c57aa85556b53d0b65d9a1b5bebbbc9f3161ff9c32703ef58d1fdc4ebdcc4","source":{"kind":"arxiv","id":"2410.02458","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.02458","created_at":"2026-07-05T11:55:36Z"},{"alias_kind":"arxiv_version","alias_value":"2410.02458v3","created_at":"2026-07-05T11:55:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.02458","created_at":"2026-07-05T11:55:36Z"},{"alias_kind":"pith_short_12","alias_value":"NBGFPKUFKVVV","created_at":"2026-07-05T11:55:36Z"},{"alias_kind":"pith_short_16","alias_value":"NBGFPKUFKVVVHUFW","created_at":"2026-07-05T11:55:36Z"},{"alias_kind":"pith_short_8","alias_value":"NBGFPKUF","created_at":"2026-07-05T11:55:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:NBGFPKUFKVVVHUFWLWNBWW7LXP","target":"record","payload":{"canonical_record":{"source":{"id":"2410.02458","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2024-10-03T14:50:33Z","cross_cats_sorted":["cs.CL","cs.CV"],"title_canon_sha256":"1807ff6eaef25b41251654e961e2188954eef51a34f0c9e67b2f418c80e1e6fb","abstract_canon_sha256":"ba5e801a5d3fbfafb4b686ae631316976e03466afcfba9eacfaceb3c20ef1e3a"},"schema_version":"1.0"},"canonical_sha256":"684c57aa85556b53d0b65d9a1b5bebbbc9f3161ff9c32703ef58d1fdc4ebdcc4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:55:36.407253Z","signature_b64":"M7wz08CE97BKtNjFnFwflQMAVOxF85H+IuSTDOXM6S5CPzXpPt/NRC0SClWRHGQZnelAuWAdbR54XDg/IU/NDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"684c57aa85556b53d0b65d9a1b5bebbbc9f3161ff9c32703ef58d1fdc4ebdcc4","last_reissued_at":"2026-07-05T11:55:36.406623Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:55:36.406623Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.02458","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:55:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qFLPGjVUHy4P5cRMw9nQb31Hj0nK5UrmrtsoBPoYhRmbqLTgwMxwKsx+9AIMAvecG1R+uMHiM0RTM0ZxoekTCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T09:40:08.980796Z"},"content_sha256":"eafd285677d5d8d2efc1c6cdaac9338f92dcf6fd6afbd44c4c770387cbae9229","schema_version":"1.0","event_id":"sha256:eafd285677d5d8d2efc1c6cdaac9338f92dcf6fd6afbd44c4c770387cbae9229"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:NBGFPKUFKVVVHUFWLWNBWW7LXP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MedVisionLlama: Leveraging Pre-Trained Large Language Model Layers to Enhance Medical Image Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL","cs.CV"],"primary_cat":"eess.IV","authors_text":"Aman Chadha, Amir Shmuel, Gurucharan Marthi Krishna Kumar, Janine Mendola","submitted_at":"2024-10-03T14:50:33Z","abstract_excerpt":"Large Language Models (LLMs), known for their versatility in textual data, are increasingly being explored for their potential to enhance medical image segmentation, a crucial task for accurate diagnostic imaging. This study explores enhancing Vision Transformers (ViTs) for medical image segmentation by integrating pre-trained LLM transformer blocks. Our approach, which incorporates a frozen LLM transformer block into the encoder of a ViT-based model, leads to substantial improvements in segmentation performance across various medical imaging modalities. We propose a Hybrid Attention Mechanism"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.02458","kind":"arxiv","version":3},"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/2410.02458/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":"2026-07-05T11:55:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Q0/lOIq77lLJUqmRSs2Sq7hA4eEksqeM8KwkLDjHfXXPq27q7rwpz3bqZJxOGouWea3jJzR+S4WpEZ/h/JO+CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T09:40:08.981173Z"},"content_sha256":"9242bf7c7b39a3f518e773e8e3873424ff90245e2bfbf409cac7d0d07301e1e3","schema_version":"1.0","event_id":"sha256:9242bf7c7b39a3f518e773e8e3873424ff90245e2bfbf409cac7d0d07301e1e3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NBGFPKUFKVVVHUFWLWNBWW7LXP/bundle.json","state_url":"https://pith.science/pith/NBGFPKUFKVVVHUFWLWNBWW7LXP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NBGFPKUFKVVVHUFWLWNBWW7LXP/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":"2026-07-07T09:40:08Z","links":{"resolver":"https://pith.science/pith/NBGFPKUFKVVVHUFWLWNBWW7LXP","bundle":"https://pith.science/pith/NBGFPKUFKVVVHUFWLWNBWW7LXP/bundle.json","state":"https://pith.science/pith/NBGFPKUFKVVVHUFWLWNBWW7LXP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NBGFPKUFKVVVHUFWLWNBWW7LXP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:NBGFPKUFKVVVHUFWLWNBWW7LXP","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":"ba5e801a5d3fbfafb4b686ae631316976e03466afcfba9eacfaceb3c20ef1e3a","cross_cats_sorted":["cs.CL","cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2024-10-03T14:50:33Z","title_canon_sha256":"1807ff6eaef25b41251654e961e2188954eef51a34f0c9e67b2f418c80e1e6fb"},"schema_version":"1.0","source":{"id":"2410.02458","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.02458","created_at":"2026-07-05T11:55:36Z"},{"alias_kind":"arxiv_version","alias_value":"2410.02458v3","created_at":"2026-07-05T11:55:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.02458","created_at":"2026-07-05T11:55:36Z"},{"alias_kind":"pith_short_12","alias_value":"NBGFPKUFKVVV","created_at":"2026-07-05T11:55:36Z"},{"alias_kind":"pith_short_16","alias_value":"NBGFPKUFKVVVHUFW","created_at":"2026-07-05T11:55:36Z"},{"alias_kind":"pith_short_8","alias_value":"NBGFPKUF","created_at":"2026-07-05T11:55:36Z"}],"graph_snapshots":[{"event_id":"sha256:9242bf7c7b39a3f518e773e8e3873424ff90245e2bfbf409cac7d0d07301e1e3","target":"graph","created_at":"2026-07-05T11:55:36Z","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/2410.02458/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large Language Models (LLMs), known for their versatility in textual data, are increasingly being explored for their potential to enhance medical image segmentation, a crucial task for accurate diagnostic imaging. This study explores enhancing Vision Transformers (ViTs) for medical image segmentation by integrating pre-trained LLM transformer blocks. Our approach, which incorporates a frozen LLM transformer block into the encoder of a ViT-based model, leads to substantial improvements in segmentation performance across various medical imaging modalities. We propose a Hybrid Attention Mechanism","authors_text":"Aman Chadha, Amir Shmuel, Gurucharan Marthi Krishna Kumar, Janine Mendola","cross_cats":["cs.CL","cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2024-10-03T14:50:33Z","title":"MedVisionLlama: Leveraging Pre-Trained Large Language Model Layers to Enhance Medical Image Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.02458","kind":"arxiv","version":3},"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:eafd285677d5d8d2efc1c6cdaac9338f92dcf6fd6afbd44c4c770387cbae9229","target":"record","created_at":"2026-07-05T11:55:36Z","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":"ba5e801a5d3fbfafb4b686ae631316976e03466afcfba9eacfaceb3c20ef1e3a","cross_cats_sorted":["cs.CL","cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2024-10-03T14:50:33Z","title_canon_sha256":"1807ff6eaef25b41251654e961e2188954eef51a34f0c9e67b2f418c80e1e6fb"},"schema_version":"1.0","source":{"id":"2410.02458","kind":"arxiv","version":3}},"canonical_sha256":"684c57aa85556b53d0b65d9a1b5bebbbc9f3161ff9c32703ef58d1fdc4ebdcc4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"684c57aa85556b53d0b65d9a1b5bebbbc9f3161ff9c32703ef58d1fdc4ebdcc4","first_computed_at":"2026-07-05T11:55:36.406623Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:55:36.406623Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"M7wz08CE97BKtNjFnFwflQMAVOxF85H+IuSTDOXM6S5CPzXpPt/NRC0SClWRHGQZnelAuWAdbR54XDg/IU/NDA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:55:36.407253Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.02458","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:eafd285677d5d8d2efc1c6cdaac9338f92dcf6fd6afbd44c4c770387cbae9229","sha256:9242bf7c7b39a3f518e773e8e3873424ff90245e2bfbf409cac7d0d07301e1e3"],"state_sha256":"6e0c21c9a43c1e5e188263f34b1ad840b8a6ee2aad9a81a614662dac2eba26d5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7lY/YhDGpLmhOlI5X56q69iZjdZQLiM0+3+dkqefFWZwugAv7g/IJV2Nq593bP8usHSTwDk3o+DokA9E8T70Dg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T09:40:08.983086Z","bundle_sha256":"2808ca477a2439527f76c4d0a2b191ee969753a1a68210b95259bacc19c9b672"}}