{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:5MMRIMHNHTH7F7QWHVZFSMC5QM","short_pith_number":"pith:5MMRIMHN","schema_version":"1.0","canonical_sha256":"eb191430ed3ccff2fe163d7259305d8312bdd9872b8bcd36a84f8e236421cc8a","source":{"kind":"arxiv","id":"2606.29837","version":1},"attestation_state":"computed","paper":{"title":"Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fan Zhang, Jiyang Li, Kaifeng Chen, Lechao Cheng, Shengeng Tang, Tuanrui Hui, Yantao Pan, Yaxiong Wang, Zhun Zhong","submitted_at":"2026-06-29T06:24:53Z","abstract_excerpt":"Dataset distillation (DD) condenses large corpora into compact, information-rich subsets for efficient training and reuse. However, under noisy supervision, DD risks condensing corrupted associations together with useful signals, degrading robustness. Conventional noisy-label remedies (sample selection, loss weighting, label correction) tightly couple noise estimation with model optimization, often require clean anchors, and can amplify confirmation bias-assumptions that are misaligned with DD's goal of compact, plug-and-play supervision. We therefore propose a trajectory-based DD framework th"},"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":"2606.29837","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-29T06:24:53Z","cross_cats_sorted":[],"title_canon_sha256":"fd9aad2ceee6cd5cf51aac2021497c27ccb6a0677ed8744ead0c94b6fa63b7a4","abstract_canon_sha256":"30432b8aff992e3c084ef59c2d250785cc9be53064b846ed2d85d79e2078e558"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-30T02:17:37.544008Z","signature_b64":"LTDm4WXsUm6T4Qf2QJBr7cwscMrxi5QHPh6vRREMM7OJhQ8SwzeNqBpwvnoJBE54jQlmn7wXYBfugG8quoPPBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eb191430ed3ccff2fe163d7259305d8312bdd9872b8bcd36a84f8e236421cc8a","last_reissued_at":"2026-06-30T02:17:37.543557Z","signature_status":"signed_v1","first_computed_at":"2026-06-30T02:17:37.543557Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fan Zhang, Jiyang Li, Kaifeng Chen, Lechao Cheng, Shengeng Tang, Tuanrui Hui, Yantao Pan, Yaxiong Wang, Zhun Zhong","submitted_at":"2026-06-29T06:24:53Z","abstract_excerpt":"Dataset distillation (DD) condenses large corpora into compact, information-rich subsets for efficient training and reuse. However, under noisy supervision, DD risks condensing corrupted associations together with useful signals, degrading robustness. Conventional noisy-label remedies (sample selection, loss weighting, label correction) tightly couple noise estimation with model optimization, often require clean anchors, and can amplify confirmation bias-assumptions that are misaligned with DD's goal of compact, plug-and-play supervision. We therefore propose a trajectory-based DD framework th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.29837","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/2606.29837/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":"2606.29837","created_at":"2026-06-30T02:17:37.543618+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.29837v1","created_at":"2026-06-30T02:17:37.543618+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.29837","created_at":"2026-06-30T02:17:37.543618+00:00"},{"alias_kind":"pith_short_12","alias_value":"5MMRIMHNHTH7","created_at":"2026-06-30T02:17:37.543618+00:00"},{"alias_kind":"pith_short_16","alias_value":"5MMRIMHNHTH7F7QW","created_at":"2026-06-30T02:17:37.543618+00:00"},{"alias_kind":"pith_short_8","alias_value":"5MMRIMHN","created_at":"2026-06-30T02:17:37.543618+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/5MMRIMHNHTH7F7QWHVZFSMC5QM","json":"https://pith.science/pith/5MMRIMHNHTH7F7QWHVZFSMC5QM.json","graph_json":"https://pith.science/api/pith-number/5MMRIMHNHTH7F7QWHVZFSMC5QM/graph.json","events_json":"https://pith.science/api/pith-number/5MMRIMHNHTH7F7QWHVZFSMC5QM/events.json","paper":"https://pith.science/paper/5MMRIMHN"},"agent_actions":{"view_html":"https://pith.science/pith/5MMRIMHNHTH7F7QWHVZFSMC5QM","download_json":"https://pith.science/pith/5MMRIMHNHTH7F7QWHVZFSMC5QM.json","view_paper":"https://pith.science/paper/5MMRIMHN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.29837&json=true","fetch_graph":"https://pith.science/api/pith-number/5MMRIMHNHTH7F7QWHVZFSMC5QM/graph.json","fetch_events":"https://pith.science/api/pith-number/5MMRIMHNHTH7F7QWHVZFSMC5QM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5MMRIMHNHTH7F7QWHVZFSMC5QM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5MMRIMHNHTH7F7QWHVZFSMC5QM/action/storage_attestation","attest_author":"https://pith.science/pith/5MMRIMHNHTH7F7QWHVZFSMC5QM/action/author_attestation","sign_citation":"https://pith.science/pith/5MMRIMHNHTH7F7QWHVZFSMC5QM/action/citation_signature","submit_replication":"https://pith.science/pith/5MMRIMHNHTH7F7QWHVZFSMC5QM/action/replication_record"}},"created_at":"2026-06-30T02:17:37.543618+00:00","updated_at":"2026-06-30T02:17:37.543618+00:00"}