{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:OJ4PQGZCIWPR4RR7MBCLT5P5V4","short_pith_number":"pith:OJ4PQGZC","canonical_record":{"source":{"id":"2607.00926","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-01T13:29:54Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"e1507426b732ffd42fad2c697327f067446ffbf4fb00f8973ccaef85992a1084","abstract_canon_sha256":"afb551b85f2cf7797ee788d6fc560ac8bc78dcf3b4981a72b19d6ef6acee9524"},"schema_version":"1.0"},"canonical_sha256":"7278f81b22459f1e463f6044b9f5fdaf0411f29e21e7ef480cd08a4eed8b9af7","source":{"kind":"arxiv","id":"2607.00926","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.00926","created_at":"2026-07-02T01:18:23Z"},{"alias_kind":"arxiv_version","alias_value":"2607.00926v1","created_at":"2026-07-02T01:18:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.00926","created_at":"2026-07-02T01:18:23Z"},{"alias_kind":"pith_short_12","alias_value":"OJ4PQGZCIWPR","created_at":"2026-07-02T01:18:23Z"},{"alias_kind":"pith_short_16","alias_value":"OJ4PQGZCIWPR4RR7","created_at":"2026-07-02T01:18:23Z"},{"alias_kind":"pith_short_8","alias_value":"OJ4PQGZC","created_at":"2026-07-02T01:18:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:OJ4PQGZCIWPR4RR7MBCLT5P5V4","target":"record","payload":{"canonical_record":{"source":{"id":"2607.00926","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-01T13:29:54Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"e1507426b732ffd42fad2c697327f067446ffbf4fb00f8973ccaef85992a1084","abstract_canon_sha256":"afb551b85f2cf7797ee788d6fc560ac8bc78dcf3b4981a72b19d6ef6acee9524"},"schema_version":"1.0"},"canonical_sha256":"7278f81b22459f1e463f6044b9f5fdaf0411f29e21e7ef480cd08a4eed8b9af7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-02T01:18:23.744957Z","signature_b64":"rv7lQT5KzBdRW8kQuxFJI5dVPXT5mlYrN3jtpwzWNpDHItxOOLS8ALsTbbMjNGnUGTsaPF1NK/jBCZTgdNe4DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7278f81b22459f1e463f6044b9f5fdaf0411f29e21e7ef480cd08a4eed8b9af7","last_reissued_at":"2026-07-02T01:18:23.744571Z","signature_status":"signed_v1","first_computed_at":"2026-07-02T01:18:23.744571Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2607.00926","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":"2026-07-02T01:18:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"G20uzS84ldOq6Ovznq98phnxgKJVl51P+CWzCLP0DICQ1dWViP9aiEorBMWOul/Velv9Cq6kdfsh12xESC5VDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T07:55:41.313402Z"},"content_sha256":"c936025017e42aa70b4f2f0caa60aaeb4469b0e800e07fbf37f0ce3508219d16","schema_version":"1.0","event_id":"sha256:c936025017e42aa70b4f2f0caa60aaeb4469b0e800e07fbf37f0ce3508219d16"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:OJ4PQGZCIWPR4RR7MBCLT5P5V4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Human-Machine Collaboration on Generative Meta-Learning: Model and Algorithm","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Midhun Parakkal Unni, Samuel Kaski","submitted_at":"2026-07-01T13:29:54Z","abstract_excerpt":"Generalizing machine learning models to environments that differ from their training distribution remains a critical hurdle, particularly when data from the target domain is entirely or partially unavailable. We propose Generative Meta-Learning with Human Feedback (GMHF), a novel framework that bridges this domain gap by leveraging expert intuition to guide data synthesis. Grounded in a theoretical analysis of generalization error, we derive bounds demonstrating that aligning the distribution of generated data with human beliefs regarding the target physics significantly mitigates risk. GMHF o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.00926","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/2607.00926/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":"2026-07-02T01:18:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zuIkXVoaHLRVmaoEqH5SK25Rs+OWGKpwSR0SGSDYtb469m9R3iFzEJcEm+WSNXUPTaL/gPWDJyDTIkEY0cqqAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T07:55:41.313777Z"},"content_sha256":"eff0825ea47cc3696dc472e18300febd08ef9855e40eedce39d29f9c72a8f977","schema_version":"1.0","event_id":"sha256:eff0825ea47cc3696dc472e18300febd08ef9855e40eedce39d29f9c72a8f977"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OJ4PQGZCIWPR4RR7MBCLT5P5V4/bundle.json","state_url":"https://pith.science/pith/OJ4PQGZCIWPR4RR7MBCLT5P5V4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OJ4PQGZCIWPR4RR7MBCLT5P5V4/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":"2026-07-07T07:55:41Z","links":{"resolver":"https://pith.science/pith/OJ4PQGZCIWPR4RR7MBCLT5P5V4","bundle":"https://pith.science/pith/OJ4PQGZCIWPR4RR7MBCLT5P5V4/bundle.json","state":"https://pith.science/pith/OJ4PQGZCIWPR4RR7MBCLT5P5V4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OJ4PQGZCIWPR4RR7MBCLT5P5V4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:OJ4PQGZCIWPR4RR7MBCLT5P5V4","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":"afb551b85f2cf7797ee788d6fc560ac8bc78dcf3b4981a72b19d6ef6acee9524","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-01T13:29:54Z","title_canon_sha256":"e1507426b732ffd42fad2c697327f067446ffbf4fb00f8973ccaef85992a1084"},"schema_version":"1.0","source":{"id":"2607.00926","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.00926","created_at":"2026-07-02T01:18:23Z"},{"alias_kind":"arxiv_version","alias_value":"2607.00926v1","created_at":"2026-07-02T01:18:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.00926","created_at":"2026-07-02T01:18:23Z"},{"alias_kind":"pith_short_12","alias_value":"OJ4PQGZCIWPR","created_at":"2026-07-02T01:18:23Z"},{"alias_kind":"pith_short_16","alias_value":"OJ4PQGZCIWPR4RR7","created_at":"2026-07-02T01:18:23Z"},{"alias_kind":"pith_short_8","alias_value":"OJ4PQGZC","created_at":"2026-07-02T01:18:23Z"}],"graph_snapshots":[{"event_id":"sha256:eff0825ea47cc3696dc472e18300febd08ef9855e40eedce39d29f9c72a8f977","target":"graph","created_at":"2026-07-02T01:18:23Z","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/2607.00926/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Generalizing machine learning models to environments that differ from their training distribution remains a critical hurdle, particularly when data from the target domain is entirely or partially unavailable. We propose Generative Meta-Learning with Human Feedback (GMHF), a novel framework that bridges this domain gap by leveraging expert intuition to guide data synthesis. Grounded in a theoretical analysis of generalization error, we derive bounds demonstrating that aligning the distribution of generated data with human beliefs regarding the target physics significantly mitigates risk. GMHF o","authors_text":"Midhun Parakkal Unni, Samuel Kaski","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-01T13:29:54Z","title":"Human-Machine Collaboration on Generative Meta-Learning: Model and Algorithm"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.00926","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:c936025017e42aa70b4f2f0caa60aaeb4469b0e800e07fbf37f0ce3508219d16","target":"record","created_at":"2026-07-02T01:18:23Z","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":"afb551b85f2cf7797ee788d6fc560ac8bc78dcf3b4981a72b19d6ef6acee9524","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-01T13:29:54Z","title_canon_sha256":"e1507426b732ffd42fad2c697327f067446ffbf4fb00f8973ccaef85992a1084"},"schema_version":"1.0","source":{"id":"2607.00926","kind":"arxiv","version":1}},"canonical_sha256":"7278f81b22459f1e463f6044b9f5fdaf0411f29e21e7ef480cd08a4eed8b9af7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7278f81b22459f1e463f6044b9f5fdaf0411f29e21e7ef480cd08a4eed8b9af7","first_computed_at":"2026-07-02T01:18:23.744571Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-02T01:18:23.744571Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rv7lQT5KzBdRW8kQuxFJI5dVPXT5mlYrN3jtpwzWNpDHItxOOLS8ALsTbbMjNGnUGTsaPF1NK/jBCZTgdNe4DQ==","signature_status":"signed_v1","signed_at":"2026-07-02T01:18:23.744957Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.00926","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c936025017e42aa70b4f2f0caa60aaeb4469b0e800e07fbf37f0ce3508219d16","sha256:eff0825ea47cc3696dc472e18300febd08ef9855e40eedce39d29f9c72a8f977"],"state_sha256":"d8ca6cbbec2f937275c2ce90e8e7ea676ff811adb5b0665d8c44bf4f3da78fd0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WQdmGrVFVIKjCBMT9pEsuGd7Xe5yfB0Pn8aSDAOUseNe7KySQ6vMJ88iBe1uuHk97Q/wnVRmFrhPUX8N7xPvDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T07:55:41.315730Z","bundle_sha256":"765c41e1f38b52f360dee5f6b4262ce6d7f9e85b0033bb40b9a6addd95209a05"}}