{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:5D7RUK6UOKN2ZKQCIQBCXXDUCH","short_pith_number":"pith:5D7RUK6U","schema_version":"1.0","canonical_sha256":"e8ff1a2bd4729bacaa0244022bdc7411c592fb4660ee50b98b9dbc1a7320e500","source":{"kind":"arxiv","id":"2505.02604","version":5},"attestation_state":"computed","paper":{"title":"Connecting Independently Trained Modes via Layer-Wise Connectivity","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Maksim Kitsak, Peter Hofstee, Yongding Tian, Zaid Al-Ars","submitted_at":"2025-05-05T12:16:55Z","abstract_excerpt":"Empirical studies have shown that continuous low-loss paths can be constructed between independently trained neural network models. This phenomenon, known as mode connectivity, refers to the existence of such paths between distinct modes-i.e., well-trained solutions in parameter space. However, existing empirical methods do not reliably connect independently trained modes and have been evaluated mainly on a narrow set of architectures (e.g., basic CNNs, VGG, and ResNet), leaving their effectiveness on newer models unclear. In this work, we propose a new empirical algorithm for connecting indep"},"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":"2505.02604","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-05T12:16:55Z","cross_cats_sorted":[],"title_canon_sha256":"4a7af76da454d4598e3dae0e520e2ed075a5fb9038531bc9302381cefd1be0d8","abstract_canon_sha256":"b0a56086389029e617a907063335c25192b3e31f51dbf62efe8212ec0b722a97"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-29T01:04:52.951268Z","signature_b64":"Bm0+Nze+WI7QK+1PdJf7EnFAzlxFEW938ihJvgig6chGjp44KQ0G+yI52Ngv2gFhED+V2gwzb/WP/U08h0/TDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e8ff1a2bd4729bacaa0244022bdc7411c592fb4660ee50b98b9dbc1a7320e500","last_reissued_at":"2026-05-29T01:04:52.950716Z","signature_status":"signed_v1","first_computed_at":"2026-05-29T01:04:52.950716Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Connecting Independently Trained Modes via Layer-Wise Connectivity","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Maksim Kitsak, Peter Hofstee, Yongding Tian, Zaid Al-Ars","submitted_at":"2025-05-05T12:16:55Z","abstract_excerpt":"Empirical studies have shown that continuous low-loss paths can be constructed between independently trained neural network models. This phenomenon, known as mode connectivity, refers to the existence of such paths between distinct modes-i.e., well-trained solutions in parameter space. However, existing empirical methods do not reliably connect independently trained modes and have been evaluated mainly on a narrow set of architectures (e.g., basic CNNs, VGG, and ResNet), leaving their effectiveness on newer models unclear. In this work, we propose a new empirical algorithm for connecting indep"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.02604","kind":"arxiv","version":5},"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/2505.02604/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":"2505.02604","created_at":"2026-05-29T01:04:52.950775+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.02604v5","created_at":"2026-05-29T01:04:52.950775+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.02604","created_at":"2026-05-29T01:04:52.950775+00:00"},{"alias_kind":"pith_short_12","alias_value":"5D7RUK6UOKN2","created_at":"2026-05-29T01:04:52.950775+00:00"},{"alias_kind":"pith_short_16","alias_value":"5D7RUK6UOKN2ZKQC","created_at":"2026-05-29T01:04:52.950775+00:00"},{"alias_kind":"pith_short_8","alias_value":"5D7RUK6U","created_at":"2026-05-29T01:04:52.950775+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/5D7RUK6UOKN2ZKQCIQBCXXDUCH","json":"https://pith.science/pith/5D7RUK6UOKN2ZKQCIQBCXXDUCH.json","graph_json":"https://pith.science/api/pith-number/5D7RUK6UOKN2ZKQCIQBCXXDUCH/graph.json","events_json":"https://pith.science/api/pith-number/5D7RUK6UOKN2ZKQCIQBCXXDUCH/events.json","paper":"https://pith.science/paper/5D7RUK6U"},"agent_actions":{"view_html":"https://pith.science/pith/5D7RUK6UOKN2ZKQCIQBCXXDUCH","download_json":"https://pith.science/pith/5D7RUK6UOKN2ZKQCIQBCXXDUCH.json","view_paper":"https://pith.science/paper/5D7RUK6U","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.02604&json=true","fetch_graph":"https://pith.science/api/pith-number/5D7RUK6UOKN2ZKQCIQBCXXDUCH/graph.json","fetch_events":"https://pith.science/api/pith-number/5D7RUK6UOKN2ZKQCIQBCXXDUCH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5D7RUK6UOKN2ZKQCIQBCXXDUCH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5D7RUK6UOKN2ZKQCIQBCXXDUCH/action/storage_attestation","attest_author":"https://pith.science/pith/5D7RUK6UOKN2ZKQCIQBCXXDUCH/action/author_attestation","sign_citation":"https://pith.science/pith/5D7RUK6UOKN2ZKQCIQBCXXDUCH/action/citation_signature","submit_replication":"https://pith.science/pith/5D7RUK6UOKN2ZKQCIQBCXXDUCH/action/replication_record"}},"created_at":"2026-05-29T01:04:52.950775+00:00","updated_at":"2026-05-29T01:04:52.950775+00:00"}