{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:Y5EOGTVRIKJLBM3W63GFJQUCH4","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":"ddb5099e1cb1404d42f0e56982b321a5786444207399834dc676c224931d91cf","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-30T06:47:24Z","title_canon_sha256":"02b9febffada03d08a2524de5ce0d23deead9d3a4ef0184132e98771ef54dcc2"},"schema_version":"1.0","source":{"id":"2606.00571","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.00571","created_at":"2026-06-02T01:03:58Z"},{"alias_kind":"arxiv_version","alias_value":"2606.00571v1","created_at":"2026-06-02T01:03:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.00571","created_at":"2026-06-02T01:03:58Z"},{"alias_kind":"pith_short_12","alias_value":"Y5EOGTVRIKJL","created_at":"2026-06-02T01:03:58Z"},{"alias_kind":"pith_short_16","alias_value":"Y5EOGTVRIKJLBM3W","created_at":"2026-06-02T01:03:58Z"},{"alias_kind":"pith_short_8","alias_value":"Y5EOGTVR","created_at":"2026-06-02T01:03:58Z"}],"graph_snapshots":[{"event_id":"sha256:998f8770327db7e382cb762a64ceba9f5db37d9693da3f80e6e343da7c202101","target":"graph","created_at":"2026-06-02T01:03:58Z","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/2606.00571/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Synthetic data are increasingly used to train neural networks, yet distributional mismatch with real data limits their effectiveness when used indiscriminately. A common strategy is to learn data weights via bi-level optimization, which we refer to as Meta-learning for Training-data Selection (MTS). Interestingly, in practice, MTS often performs below expectation. We identify two obstacles in properly training MTS: a poor gradient signal-to-noise ratio (GSNR), which causes optimization difficulties, and lack of informative features that correlates with data quality. We present a mathematical a","authors_text":"Boyang Albert Li, Junqi Zhao, Zilin Du","cross_cats":["cs.AI","cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-30T06:47:24Z","title":"On the Difficulty of Learning a Meta-network for Training Data Selection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.00571","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":"sha256:410c611f6b68de48b83555b8dc919d5bfc2b95bc1fc903d446e68a368db351e7","target":"record","created_at":"2026-06-02T01:03:58Z","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":"ddb5099e1cb1404d42f0e56982b321a5786444207399834dc676c224931d91cf","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-30T06:47:24Z","title_canon_sha256":"02b9febffada03d08a2524de5ce0d23deead9d3a4ef0184132e98771ef54dcc2"},"schema_version":"1.0","source":{"id":"2606.00571","kind":"arxiv","version":1}},"canonical_sha256":"c748e34eb14292b0b376f6cc54c2823f221bae71f4b3b748f4521a5a15d2410c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c748e34eb14292b0b376f6cc54c2823f221bae71f4b3b748f4521a5a15d2410c","first_computed_at":"2026-06-02T01:03:58.606942Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-02T01:03:58.606942Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5g46kwde5Uppg8CPrve90HXgfH0ght91Zskg9kK7RYgVcbmqsyWPYV50xF0Lx/fgIRg07cHhJ2uRR6GDdArmCA==","signature_status":"signed_v1","signed_at":"2026-06-02T01:03:58.607406Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.00571","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:410c611f6b68de48b83555b8dc919d5bfc2b95bc1fc903d446e68a368db351e7","sha256:998f8770327db7e382cb762a64ceba9f5db37d9693da3f80e6e343da7c202101"],"state_sha256":"91b41c5f87b8c27bf21e0ebac0af27b0d6764a2335b20b39acbee90ce476748d"}