{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:AKGS4VM5NOJJQ7OETDM23CJKN7","short_pith_number":"pith:AKGS4VM5","canonical_record":{"source":{"id":"2506.18074","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-22T15:41:22Z","cross_cats_sorted":["cs.AI","cs.SY","eess.SY"],"title_canon_sha256":"d67c2a8d765599be5cff97e1a6c58f4512a2272664192126dcdb5cea4a2109ee","abstract_canon_sha256":"7e0088139b4a63df11b02bfdff4c686e856c10bbf14ba38059fbd05c992d2574"},"schema_version":"1.0"},"canonical_sha256":"028d2e559d6b92987dc498d9ad892a6fc4c4bedaf67a0afbb951afc5e64eb2b9","source":{"kind":"arxiv","id":"2506.18074","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.18074","created_at":"2026-07-05T11:25:28Z"},{"alias_kind":"arxiv_version","alias_value":"2506.18074v1","created_at":"2026-07-05T11:25:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.18074","created_at":"2026-07-05T11:25:28Z"},{"alias_kind":"pith_short_12","alias_value":"AKGS4VM5NOJJ","created_at":"2026-07-05T11:25:28Z"},{"alias_kind":"pith_short_16","alias_value":"AKGS4VM5NOJJQ7OE","created_at":"2026-07-05T11:25:28Z"},{"alias_kind":"pith_short_8","alias_value":"AKGS4VM5","created_at":"2026-07-05T11:25:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:AKGS4VM5NOJJQ7OETDM23CJKN7","target":"record","payload":{"canonical_record":{"source":{"id":"2506.18074","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-22T15:41:22Z","cross_cats_sorted":["cs.AI","cs.SY","eess.SY"],"title_canon_sha256":"d67c2a8d765599be5cff97e1a6c58f4512a2272664192126dcdb5cea4a2109ee","abstract_canon_sha256":"7e0088139b4a63df11b02bfdff4c686e856c10bbf14ba38059fbd05c992d2574"},"schema_version":"1.0"},"canonical_sha256":"028d2e559d6b92987dc498d9ad892a6fc4c4bedaf67a0afbb951afc5e64eb2b9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:25:28.109822Z","signature_b64":"F1zuqZpxKJCeeE4ikeFsDD9VVVcaTu3tZY0K/2ljJ1F+fHzwbNybIn35wZRKBL1oPAvc4s0fLWXPPOxYZ0jICg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"028d2e559d6b92987dc498d9ad892a6fc4c4bedaf67a0afbb951afc5e64eb2b9","last_reissued_at":"2026-07-05T11:25:28.109465Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:25:28.109465Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.18074","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:25:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"y9GJ44AyXETB1dx38xX/ds5WHsL9b4+l6UXjFvXX/MnYtgmIkGC0fpLO8edcCt/yP4CKhlPCkAvLvIL7PvMGDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T23:11:31.182654Z"},"content_sha256":"39780389d98226e293d8fc381ddd3a2decf7a4194f251bbd67eaaf01490ec5b4","schema_version":"1.0","event_id":"sha256:39780389d98226e293d8fc381ddd3a2decf7a4194f251bbd67eaaf01490ec5b4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:AKGS4VM5NOJJQ7OETDM23CJKN7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Distributionally robust minimization in meta-learning for system identification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.SY","eess.SY"],"primary_cat":"cs.LG","authors_text":"Dario Piga, Marco Forgione, Matteo Rufolo","submitted_at":"2025-06-22T15:41:22Z","abstract_excerpt":"Meta learning aims at learning how to solve tasks, and thus it allows to estimate models that can be quickly adapted to new scenarios. This work explores distributionally robust minimization in meta learning for system identification. Standard meta learning approaches optimize the expected loss, overlooking task variability. We use an alternative approach, adopting a distributionally robust optimization paradigm that prioritizes high-loss tasks, enhancing performance in worst-case scenarios. Evaluated on a meta model trained on a class of synthetic dynamical systems and tested in both in-distr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.18074","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/2506.18074/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:25:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1FWc0nCs6XDLZeRdSmFV3BSDWhR0cdLHTBXQKEB02LV0DrlF59WqfPqrS9Tc8KrJ/3fyVeATfOWTAQdK56FjAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T23:11:31.183348Z"},"content_sha256":"e27927f45efe02aacda6aff6e5f3b3d999591adbb9e7ef5cae2986f816a3de9d","schema_version":"1.0","event_id":"sha256:e27927f45efe02aacda6aff6e5f3b3d999591adbb9e7ef5cae2986f816a3de9d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AKGS4VM5NOJJQ7OETDM23CJKN7/bundle.json","state_url":"https://pith.science/pith/AKGS4VM5NOJJQ7OETDM23CJKN7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AKGS4VM5NOJJQ7OETDM23CJKN7/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-08T23:11:31Z","links":{"resolver":"https://pith.science/pith/AKGS4VM5NOJJQ7OETDM23CJKN7","bundle":"https://pith.science/pith/AKGS4VM5NOJJQ7OETDM23CJKN7/bundle.json","state":"https://pith.science/pith/AKGS4VM5NOJJQ7OETDM23CJKN7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AKGS4VM5NOJJQ7OETDM23CJKN7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:AKGS4VM5NOJJQ7OETDM23CJKN7","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":"7e0088139b4a63df11b02bfdff4c686e856c10bbf14ba38059fbd05c992d2574","cross_cats_sorted":["cs.AI","cs.SY","eess.SY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-22T15:41:22Z","title_canon_sha256":"d67c2a8d765599be5cff97e1a6c58f4512a2272664192126dcdb5cea4a2109ee"},"schema_version":"1.0","source":{"id":"2506.18074","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.18074","created_at":"2026-07-05T11:25:28Z"},{"alias_kind":"arxiv_version","alias_value":"2506.18074v1","created_at":"2026-07-05T11:25:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.18074","created_at":"2026-07-05T11:25:28Z"},{"alias_kind":"pith_short_12","alias_value":"AKGS4VM5NOJJ","created_at":"2026-07-05T11:25:28Z"},{"alias_kind":"pith_short_16","alias_value":"AKGS4VM5NOJJQ7OE","created_at":"2026-07-05T11:25:28Z"},{"alias_kind":"pith_short_8","alias_value":"AKGS4VM5","created_at":"2026-07-05T11:25:28Z"}],"graph_snapshots":[{"event_id":"sha256:e27927f45efe02aacda6aff6e5f3b3d999591adbb9e7ef5cae2986f816a3de9d","target":"graph","created_at":"2026-07-05T11:25:28Z","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/2506.18074/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Meta learning aims at learning how to solve tasks, and thus it allows to estimate models that can be quickly adapted to new scenarios. This work explores distributionally robust minimization in meta learning for system identification. Standard meta learning approaches optimize the expected loss, overlooking task variability. We use an alternative approach, adopting a distributionally robust optimization paradigm that prioritizes high-loss tasks, enhancing performance in worst-case scenarios. Evaluated on a meta model trained on a class of synthetic dynamical systems and tested in both in-distr","authors_text":"Dario Piga, Marco Forgione, Matteo Rufolo","cross_cats":["cs.AI","cs.SY","eess.SY"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-22T15:41:22Z","title":"Distributionally robust minimization in meta-learning for system identification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.18074","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":"sha256:39780389d98226e293d8fc381ddd3a2decf7a4194f251bbd67eaaf01490ec5b4","target":"record","created_at":"2026-07-05T11:25:28Z","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":"7e0088139b4a63df11b02bfdff4c686e856c10bbf14ba38059fbd05c992d2574","cross_cats_sorted":["cs.AI","cs.SY","eess.SY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-22T15:41:22Z","title_canon_sha256":"d67c2a8d765599be5cff97e1a6c58f4512a2272664192126dcdb5cea4a2109ee"},"schema_version":"1.0","source":{"id":"2506.18074","kind":"arxiv","version":1}},"canonical_sha256":"028d2e559d6b92987dc498d9ad892a6fc4c4bedaf67a0afbb951afc5e64eb2b9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"028d2e559d6b92987dc498d9ad892a6fc4c4bedaf67a0afbb951afc5e64eb2b9","first_computed_at":"2026-07-05T11:25:28.109465Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:25:28.109465Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"F1zuqZpxKJCeeE4ikeFsDD9VVVcaTu3tZY0K/2ljJ1F+fHzwbNybIn35wZRKBL1oPAvc4s0fLWXPPOxYZ0jICg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:25:28.109822Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.18074","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:39780389d98226e293d8fc381ddd3a2decf7a4194f251bbd67eaaf01490ec5b4","sha256:e27927f45efe02aacda6aff6e5f3b3d999591adbb9e7ef5cae2986f816a3de9d"],"state_sha256":"dbddc9bd8174b0895d7d46649a33b205001fb639b694f26c28a45f0dc245e1b3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YG0S3K/2l1Pb9WpnamBZZgOPWmuf+Qy40E7OzSJRmOTIutJFmKFIzUvx/X+d2NTJfdYExkejJw2mBFIXxa5cBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T23:11:31.187699Z","bundle_sha256":"e8c6ea5f8a30b6735aad305ac5352bd28062646d1a7d0ec4612c6a5613b7bf31"}}