{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:QRK7HZICTJQMDISWKD7DGNLA6G","short_pith_number":"pith:QRK7HZIC","schema_version":"1.0","canonical_sha256":"8455f3e5029a60c1a25650fe333560f19958809016311bc49d8f4b2688180b4e","source":{"kind":"arxiv","id":"2605.23410","version":1},"attestation_state":"computed","paper":{"title":"What Linear Probes Miss: Multi-View Probing for Weight-Space Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Eunwoo Heo, Jaejun Yoo, Kyeongkook Seo","submitted_at":"2026-05-22T09:18:01Z","abstract_excerpt":"The explosive growth of open-source model repositories has created a Model Jungle, where checkpoints are frequently shared without adequate documentation or metadata. While weight-space learning offers a pathway to identify and analyze these models directly from their parameters, processing full-scale weights is computationally prohibitive. Probing-based methods have emerged as a lightweight alternative, extracting permutation-equivariant representations via learnable probe vectors. However, existing probing methods are limited by a single-view design: they capture first-order structures but f"},"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":"2605.23410","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-22T09:18:01Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"507b05f558ef700fac67719dcb95fbc10fed2a55734ed96c47a2bee47c0318db","abstract_canon_sha256":"644b9e3458b0b428e2c50f6d4505ecaa3e80cd4b774d37c7ed1d1b1332234d4a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-25T02:01:53.137217Z","signature_b64":"BBV5dZYaxZGKpDPvq4c4VLr0UCND8LUmfK7MrujKSK42VrA6RdghvOStxlZEDmnOyYUInuzPuTpm1eP5ZorKDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8455f3e5029a60c1a25650fe333560f19958809016311bc49d8f4b2688180b4e","last_reissued_at":"2026-05-25T02:01:53.136594Z","signature_status":"signed_v1","first_computed_at":"2026-05-25T02:01:53.136594Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"What Linear Probes Miss: Multi-View Probing for Weight-Space Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Eunwoo Heo, Jaejun Yoo, Kyeongkook Seo","submitted_at":"2026-05-22T09:18:01Z","abstract_excerpt":"The explosive growth of open-source model repositories has created a Model Jungle, where checkpoints are frequently shared without adequate documentation or metadata. While weight-space learning offers a pathway to identify and analyze these models directly from their parameters, processing full-scale weights is computationally prohibitive. Probing-based methods have emerged as a lightweight alternative, extracting permutation-equivariant representations via learnable probe vectors. However, existing probing methods are limited by a single-view design: they capture first-order structures but f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.23410","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/2605.23410/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":"2605.23410","created_at":"2026-05-25T02:01:53.136689+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.23410v1","created_at":"2026-05-25T02:01:53.136689+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.23410","created_at":"2026-05-25T02:01:53.136689+00:00"},{"alias_kind":"pith_short_12","alias_value":"QRK7HZICTJQM","created_at":"2026-05-25T02:01:53.136689+00:00"},{"alias_kind":"pith_short_16","alias_value":"QRK7HZICTJQMDISW","created_at":"2026-05-25T02:01:53.136689+00:00"},{"alias_kind":"pith_short_8","alias_value":"QRK7HZIC","created_at":"2026-05-25T02:01:53.136689+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/QRK7HZICTJQMDISWKD7DGNLA6G","json":"https://pith.science/pith/QRK7HZICTJQMDISWKD7DGNLA6G.json","graph_json":"https://pith.science/api/pith-number/QRK7HZICTJQMDISWKD7DGNLA6G/graph.json","events_json":"https://pith.science/api/pith-number/QRK7HZICTJQMDISWKD7DGNLA6G/events.json","paper":"https://pith.science/paper/QRK7HZIC"},"agent_actions":{"view_html":"https://pith.science/pith/QRK7HZICTJQMDISWKD7DGNLA6G","download_json":"https://pith.science/pith/QRK7HZICTJQMDISWKD7DGNLA6G.json","view_paper":"https://pith.science/paper/QRK7HZIC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.23410&json=true","fetch_graph":"https://pith.science/api/pith-number/QRK7HZICTJQMDISWKD7DGNLA6G/graph.json","fetch_events":"https://pith.science/api/pith-number/QRK7HZICTJQMDISWKD7DGNLA6G/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QRK7HZICTJQMDISWKD7DGNLA6G/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QRK7HZICTJQMDISWKD7DGNLA6G/action/storage_attestation","attest_author":"https://pith.science/pith/QRK7HZICTJQMDISWKD7DGNLA6G/action/author_attestation","sign_citation":"https://pith.science/pith/QRK7HZICTJQMDISWKD7DGNLA6G/action/citation_signature","submit_replication":"https://pith.science/pith/QRK7HZICTJQMDISWKD7DGNLA6G/action/replication_record"}},"created_at":"2026-05-25T02:01:53.136689+00:00","updated_at":"2026-05-25T02:01:53.136689+00:00"}