{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:X7FBMVUT2QT23NPDZNVX7TWWER","short_pith_number":"pith:X7FBMVUT","schema_version":"1.0","canonical_sha256":"bfca165693d427adb5e3cb6b7fced624546f839ee2db6fa44ed4a9b87901aaea","source":{"kind":"arxiv","id":"2607.04755","version":1},"attestation_state":"computed","paper":{"title":"Continual Model Merging with Test-Time Adaptation for Whole-Slide Image Analysis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Doanh C. Bui, Duc-Thanh Le, Khang Nguyen, Ma\\\"i K. Nguyen","submitted_at":"2026-07-06T07:48:00Z","abstract_excerpt":"Model merging offers a practical alternative to conventional continual learning by integrating independently fine-tuned models without retaining previous training data. Recent state-of-the-art model merging methods employ test-time adaptation (TTA-guided merging) to address distribution shifts by adjusting merging-related variables using unlabeled target data. However, these methods have primarily been studied in multi-task or single-target settings, and their behavior under sequential continual learning remains insufficiently understood. We present a benchmark study that maps this family of m"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2607.04755","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-06T07:48:00Z","cross_cats_sorted":[],"title_canon_sha256":"3ffd7398820ad1287901cd9b3d8edda490c286b779c612695ac2b100f0d5fef3","abstract_canon_sha256":"62be42da12faddbad5a6d2eaf19c39c846dfa2a84a1dd6adeccf90b79f126332"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-07T02:20:01.776906Z","signature_b64":"1WrnYUrjhMynBY3DyT8pnFb7qxzXNncC1/zZMwVqRqaKoVube+qOAngT1FERBIVx+IC/CUgA5Hoio/h5tvToDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bfca165693d427adb5e3cb6b7fced624546f839ee2db6fa44ed4a9b87901aaea","last_reissued_at":"2026-07-07T02:20:01.776117Z","signature_status":"signed_v1","first_computed_at":"2026-07-07T02:20:01.776117Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Continual Model Merging with Test-Time Adaptation for Whole-Slide Image Analysis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Doanh C. Bui, Duc-Thanh Le, Khang Nguyen, Ma\\\"i K. Nguyen","submitted_at":"2026-07-06T07:48:00Z","abstract_excerpt":"Model merging offers a practical alternative to conventional continual learning by integrating independently fine-tuned models without retaining previous training data. Recent state-of-the-art model merging methods employ test-time adaptation (TTA-guided merging) to address distribution shifts by adjusting merging-related variables using unlabeled target data. However, these methods have primarily been studied in multi-task or single-target settings, and their behavior under sequential continual learning remains insufficiently understood. We present a benchmark study that maps this family of m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.04755","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/2607.04755/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2607.04755","created_at":"2026-07-07T02:20:01.776222+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.04755v1","created_at":"2026-07-07T02:20:01.776222+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.04755","created_at":"2026-07-07T02:20:01.776222+00:00"},{"alias_kind":"pith_short_12","alias_value":"X7FBMVUT2QT2","created_at":"2026-07-07T02:20:01.776222+00:00"},{"alias_kind":"pith_short_16","alias_value":"X7FBMVUT2QT23NPD","created_at":"2026-07-07T02:20:01.776222+00:00"},{"alias_kind":"pith_short_8","alias_value":"X7FBMVUT","created_at":"2026-07-07T02:20:01.776222+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/X7FBMVUT2QT23NPDZNVX7TWWER","json":"https://pith.science/pith/X7FBMVUT2QT23NPDZNVX7TWWER.json","graph_json":"https://pith.science/api/pith-number/X7FBMVUT2QT23NPDZNVX7TWWER/graph.json","events_json":"https://pith.science/api/pith-number/X7FBMVUT2QT23NPDZNVX7TWWER/events.json","paper":"https://pith.science/paper/X7FBMVUT"},"agent_actions":{"view_html":"https://pith.science/pith/X7FBMVUT2QT23NPDZNVX7TWWER","download_json":"https://pith.science/pith/X7FBMVUT2QT23NPDZNVX7TWWER.json","view_paper":"https://pith.science/paper/X7FBMVUT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.04755&json=true","fetch_graph":"https://pith.science/api/pith-number/X7FBMVUT2QT23NPDZNVX7TWWER/graph.json","fetch_events":"https://pith.science/api/pith-number/X7FBMVUT2QT23NPDZNVX7TWWER/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/X7FBMVUT2QT23NPDZNVX7TWWER/action/timestamp_anchor","attest_storage":"https://pith.science/pith/X7FBMVUT2QT23NPDZNVX7TWWER/action/storage_attestation","attest_author":"https://pith.science/pith/X7FBMVUT2QT23NPDZNVX7TWWER/action/author_attestation","sign_citation":"https://pith.science/pith/X7FBMVUT2QT23NPDZNVX7TWWER/action/citation_signature","submit_replication":"https://pith.science/pith/X7FBMVUT2QT23NPDZNVX7TWWER/action/replication_record"}},"created_at":"2026-07-07T02:20:01.776222+00:00","updated_at":"2026-07-07T02:20:01.776222+00:00"}