{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:AKHQIE6SLRG2HZRVVR2ZWPLM2B","short_pith_number":"pith:AKHQIE6S","canonical_record":{"source":{"id":"2608.00976","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-08-02T03:59:07Z","cross_cats_sorted":[],"title_canon_sha256":"a1eaac78ec86f54bc86fc19116d049608561f53b13424540f81eb1061b0dd7ae","abstract_canon_sha256":"c3a2dc42fb73cffb5b08a577d980f981c7fb3fb3f7b6eaf22cefc9e923d87a1c"},"schema_version":"1.0"},"canonical_sha256":"028f0413d25c4da3e635ac759b3d6cd067436ee180e976b73dfc057e7fae4dff","source":{"kind":"arxiv","id":"2608.00976","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2608.00976","created_at":"2026-08-04T01:56:09Z"},{"alias_kind":"arxiv_version","alias_value":"2608.00976v1","created_at":"2026-08-04T01:56:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.00976","created_at":"2026-08-04T01:56:09Z"},{"alias_kind":"pith_short_12","alias_value":"AKHQIE6SLRG2","created_at":"2026-08-04T01:56:09Z"},{"alias_kind":"pith_short_16","alias_value":"AKHQIE6SLRG2HZRV","created_at":"2026-08-04T01:56:09Z"},{"alias_kind":"pith_short_8","alias_value":"AKHQIE6S","created_at":"2026-08-04T01:56:09Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:AKHQIE6SLRG2HZRVVR2ZWPLM2B","target":"record","payload":{"canonical_record":{"source":{"id":"2608.00976","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-08-02T03:59:07Z","cross_cats_sorted":[],"title_canon_sha256":"a1eaac78ec86f54bc86fc19116d049608561f53b13424540f81eb1061b0dd7ae","abstract_canon_sha256":"c3a2dc42fb73cffb5b08a577d980f981c7fb3fb3f7b6eaf22cefc9e923d87a1c"},"schema_version":"1.0"},"canonical_sha256":"028f0413d25c4da3e635ac759b3d6cd067436ee180e976b73dfc057e7fae4dff","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-04T01:56:09.069084Z","signature_b64":"JYMip29K0+etUSLL4SruRTbkx6Xfdw7thuI7UIEdkmwM6yoHJ7PL2L3+uuHvwfEnAVtVnmUT28g5WF4zOLNzBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"028f0413d25c4da3e635ac759b3d6cd067436ee180e976b73dfc057e7fae4dff","last_reissued_at":"2026-08-04T01:56:09.067452Z","signature_status":"signed_v1","first_computed_at":"2026-08-04T01:56:09.067452Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2608.00976","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":"2026-08-04T01:56:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"J0DQKVS/LlXPRyGNHM+vzhy8xsmyapc7Y2o5Az2hpF3KX8g3F9ccowUGSjSQXGBM7lPNtRO6c19mEorum9QtDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T05:42:44.828480Z"},"content_sha256":"e56b17368e63adb94a86b5896f07f62dbed996a63ac2705e67104d55b4ca5623","schema_version":"1.0","event_id":"sha256:e56b17368e63adb94a86b5896f07f62dbed996a63ac2705e67104d55b4ca5623"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:AKHQIE6SLRG2HZRVVR2ZWPLM2B","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Location-Aware Fine-Grained Representation Learning for Medical Vision Foundation Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Myeongkyun Kang, Xiaoxiao Li, Yanting Yang","submitted_at":"2026-08-02T03:59:07Z","abstract_excerpt":"Fine-grained visual representations are essential for medical image analysis, particularly when diagnostically relevant evidence is subtle and spatially localized. Modern transformer-based medical vision encoders must therefore learn patch-level representations that are both clinically meaningful and spatially consistent. Without these properties, large vision-language models (LVLMs) operate on an ambiguous visual foundation, limiting their ability to generate clinically reliable and spatially grounded responses. However, existing training strategies for medical vision encoders rarely achieve "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.00976","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/2608.00976/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":"2026-08-04T01:56:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+NHG1wk6usdH4T8ylNXFrPaPF9ongPyrWT9Ao+BgYrGH7ym5HYWcdVrPhHif5Re/9zVE35qARxXJDw2rIcW2AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T05:42:44.828976Z"},"content_sha256":"6ca51e7e8e3afc7706921e07cfa4e25c65d383be13ec93f03849df3d75993aea","schema_version":"1.0","event_id":"sha256:6ca51e7e8e3afc7706921e07cfa4e25c65d383be13ec93f03849df3d75993aea"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AKHQIE6SLRG2HZRVVR2ZWPLM2B/bundle.json","state_url":"https://pith.science/pith/AKHQIE6SLRG2HZRVVR2ZWPLM2B/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AKHQIE6SLRG2HZRVVR2ZWPLM2B/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":"2026-08-06T05:42:44Z","links":{"resolver":"https://pith.science/pith/AKHQIE6SLRG2HZRVVR2ZWPLM2B","bundle":"https://pith.science/pith/AKHQIE6SLRG2HZRVVR2ZWPLM2B/bundle.json","state":"https://pith.science/pith/AKHQIE6SLRG2HZRVVR2ZWPLM2B/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AKHQIE6SLRG2HZRVVR2ZWPLM2B/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:AKHQIE6SLRG2HZRVVR2ZWPLM2B","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":"c3a2dc42fb73cffb5b08a577d980f981c7fb3fb3f7b6eaf22cefc9e923d87a1c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-08-02T03:59:07Z","title_canon_sha256":"a1eaac78ec86f54bc86fc19116d049608561f53b13424540f81eb1061b0dd7ae"},"schema_version":"1.0","source":{"id":"2608.00976","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2608.00976","created_at":"2026-08-04T01:56:09Z"},{"alias_kind":"arxiv_version","alias_value":"2608.00976v1","created_at":"2026-08-04T01:56:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.00976","created_at":"2026-08-04T01:56:09Z"},{"alias_kind":"pith_short_12","alias_value":"AKHQIE6SLRG2","created_at":"2026-08-04T01:56:09Z"},{"alias_kind":"pith_short_16","alias_value":"AKHQIE6SLRG2HZRV","created_at":"2026-08-04T01:56:09Z"},{"alias_kind":"pith_short_8","alias_value":"AKHQIE6S","created_at":"2026-08-04T01:56:09Z"}],"graph_snapshots":[{"event_id":"sha256:6ca51e7e8e3afc7706921e07cfa4e25c65d383be13ec93f03849df3d75993aea","target":"graph","created_at":"2026-08-04T01:56:09Z","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/2608.00976/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Fine-grained visual representations are essential for medical image analysis, particularly when diagnostically relevant evidence is subtle and spatially localized. Modern transformer-based medical vision encoders must therefore learn patch-level representations that are both clinically meaningful and spatially consistent. Without these properties, large vision-language models (LVLMs) operate on an ambiguous visual foundation, limiting their ability to generate clinically reliable and spatially grounded responses. However, existing training strategies for medical vision encoders rarely achieve ","authors_text":"Myeongkyun Kang, Xiaoxiao Li, Yanting Yang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-08-02T03:59:07Z","title":"Location-Aware Fine-Grained Representation Learning for Medical Vision Foundation Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.00976","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:e56b17368e63adb94a86b5896f07f62dbed996a63ac2705e67104d55b4ca5623","target":"record","created_at":"2026-08-04T01:56:09Z","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":"c3a2dc42fb73cffb5b08a577d980f981c7fb3fb3f7b6eaf22cefc9e923d87a1c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-08-02T03:59:07Z","title_canon_sha256":"a1eaac78ec86f54bc86fc19116d049608561f53b13424540f81eb1061b0dd7ae"},"schema_version":"1.0","source":{"id":"2608.00976","kind":"arxiv","version":1}},"canonical_sha256":"028f0413d25c4da3e635ac759b3d6cd067436ee180e976b73dfc057e7fae4dff","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"028f0413d25c4da3e635ac759b3d6cd067436ee180e976b73dfc057e7fae4dff","first_computed_at":"2026-08-04T01:56:09.067452Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-08-04T01:56:09.067452Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JYMip29K0+etUSLL4SruRTbkx6Xfdw7thuI7UIEdkmwM6yoHJ7PL2L3+uuHvwfEnAVtVnmUT28g5WF4zOLNzBA==","signature_status":"signed_v1","signed_at":"2026-08-04T01:56:09.069084Z","signed_message":"canonical_sha256_bytes"},"source_id":"2608.00976","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e56b17368e63adb94a86b5896f07f62dbed996a63ac2705e67104d55b4ca5623","sha256:6ca51e7e8e3afc7706921e07cfa4e25c65d383be13ec93f03849df3d75993aea"],"state_sha256":"c09209037d7daca80d808f90be73ebec6a45d8e808fb562129e74846a04d8957"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9z1SjqME2BdM4m0jhORFWcA5kfZpCVpynHSadHDKZtF5Q57YgE2m2qdtGMHn5gobixPGVi+KwznvufgLeqmLCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T05:42:44.834988Z","bundle_sha256":"79cb2eccc899d9db9e230a40f44952e749bde727709ba6bd0efcdd4f8c9b41b6"}}