{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:V24JFKKCWYPBUAGCSAEORO5KXC","short_pith_number":"pith:V24JFKKC","canonical_record":{"source":{"id":"2212.04458","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-12-08T18:30:22Z","cross_cats_sorted":["cs.AI","cs.NE","stat.ML"],"title_canon_sha256":"3d4d21e20a7bf0ebd4bd4da441fdee42a94cb80b368cedd961c67db2ee08967d","abstract_canon_sha256":"2f8a21e02641a64358d50574e9930e269637da5ec5e0b0f5923c7833daa3f826"},"schema_version":"1.0"},"canonical_sha256":"aeb892a942b61e1a00c29008e8bbaab88114f1f82cf38b76a34978bc07492012","source":{"kind":"arxiv","id":"2212.04458","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.04458","created_at":"2026-07-05T07:31:26Z"},{"alias_kind":"arxiv_version","alias_value":"2212.04458v2","created_at":"2026-07-05T07:31:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.04458","created_at":"2026-07-05T07:31:26Z"},{"alias_kind":"pith_short_12","alias_value":"V24JFKKCWYPB","created_at":"2026-07-05T07:31:26Z"},{"alias_kind":"pith_short_16","alias_value":"V24JFKKCWYPBUAGC","created_at":"2026-07-05T07:31:26Z"},{"alias_kind":"pith_short_8","alias_value":"V24JFKKC","created_at":"2026-07-05T07:31:26Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:V24JFKKCWYPBUAGCSAEORO5KXC","target":"record","payload":{"canonical_record":{"source":{"id":"2212.04458","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-12-08T18:30:22Z","cross_cats_sorted":["cs.AI","cs.NE","stat.ML"],"title_canon_sha256":"3d4d21e20a7bf0ebd4bd4da441fdee42a94cb80b368cedd961c67db2ee08967d","abstract_canon_sha256":"2f8a21e02641a64358d50574e9930e269637da5ec5e0b0f5923c7833daa3f826"},"schema_version":"1.0"},"canonical_sha256":"aeb892a942b61e1a00c29008e8bbaab88114f1f82cf38b76a34978bc07492012","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:31:26.483265Z","signature_b64":"ZuqjrMz7J+VOSbxAOPoLZovqWdC0gOAg5xhdVTo9KQ0lREWXSQa5vphLdbUlWqsR1nsVGdfpYCGO0BWkKbDjCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aeb892a942b61e1a00c29008e8bbaab88114f1f82cf38b76a34978bc07492012","last_reissued_at":"2026-07-05T07:31:26.482811Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:31:26.482811Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2212.04458","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:31:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ERS3zVX0AivRxbScyTR6B9MY0OqS+KV+TNwGXa4Nq6snd86vsst33830GIM40GmL6yiwFqnRr/QCnDeXuGFZCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T10:13:05.152594Z"},"content_sha256":"d225d8b8aa3646578fc751776715859de685086e84bcca5371a0865dd9f324d4","schema_version":"1.0","event_id":"sha256:d225d8b8aa3646578fc751776715859de685086e84bcca5371a0865dd9f324d4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:V24JFKKCWYPBUAGCSAEORO5KXC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"General-Purpose In-Context Learning by Meta-Learning Transformers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.NE","stat.ML"],"primary_cat":"cs.LG","authors_text":"James Harrison, Jascha Sohl-Dickstein, Louis Kirsch, Luke Metz","submitted_at":"2022-12-08T18:30:22Z","abstract_excerpt":"Modern machine learning requires system designers to specify aspects of the learning pipeline, such as losses, architectures, and optimizers. Meta-learning, or learning-to-learn, instead aims to learn those aspects, and promises to unlock greater capabilities with less manual effort. One particularly ambitious goal of meta-learning is to train general-purpose in-context learning algorithms from scratch, using only black-box models with minimal inductive bias. Such a model takes in training data, and produces test-set predictions across a wide range of problems, without any explicit definition "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.04458","kind":"arxiv","version":2},"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/2212.04458/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":"2026-07-05T07:31:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tobwh6pSe3Ih1kacQ5lLFjbycOxsyWuro/NvswWl141K6RAE0E2FLZ3BOrCaNnX9NZ0PUHbOxErxNpmkzzURDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T10:13:05.153141Z"},"content_sha256":"0ea767c569f39622ed33d6728dff07838e2f2631b34478c2be3b6d5e4b57de57","schema_version":"1.0","event_id":"sha256:0ea767c569f39622ed33d6728dff07838e2f2631b34478c2be3b6d5e4b57de57"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/V24JFKKCWYPBUAGCSAEORO5KXC/bundle.json","state_url":"https://pith.science/pith/V24JFKKCWYPBUAGCSAEORO5KXC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/V24JFKKCWYPBUAGCSAEORO5KXC/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":"2026-08-07T10:13:05Z","links":{"resolver":"https://pith.science/pith/V24JFKKCWYPBUAGCSAEORO5KXC","bundle":"https://pith.science/pith/V24JFKKCWYPBUAGCSAEORO5KXC/bundle.json","state":"https://pith.science/pith/V24JFKKCWYPBUAGCSAEORO5KXC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/V24JFKKCWYPBUAGCSAEORO5KXC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:V24JFKKCWYPBUAGCSAEORO5KXC","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":"2f8a21e02641a64358d50574e9930e269637da5ec5e0b0f5923c7833daa3f826","cross_cats_sorted":["cs.AI","cs.NE","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-12-08T18:30:22Z","title_canon_sha256":"3d4d21e20a7bf0ebd4bd4da441fdee42a94cb80b368cedd961c67db2ee08967d"},"schema_version":"1.0","source":{"id":"2212.04458","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.04458","created_at":"2026-07-05T07:31:26Z"},{"alias_kind":"arxiv_version","alias_value":"2212.04458v2","created_at":"2026-07-05T07:31:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.04458","created_at":"2026-07-05T07:31:26Z"},{"alias_kind":"pith_short_12","alias_value":"V24JFKKCWYPB","created_at":"2026-07-05T07:31:26Z"},{"alias_kind":"pith_short_16","alias_value":"V24JFKKCWYPBUAGC","created_at":"2026-07-05T07:31:26Z"},{"alias_kind":"pith_short_8","alias_value":"V24JFKKC","created_at":"2026-07-05T07:31:26Z"}],"graph_snapshots":[{"event_id":"sha256:0ea767c569f39622ed33d6728dff07838e2f2631b34478c2be3b6d5e4b57de57","target":"graph","created_at":"2026-07-05T07:31:26Z","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/2212.04458/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Modern machine learning requires system designers to specify aspects of the learning pipeline, such as losses, architectures, and optimizers. Meta-learning, or learning-to-learn, instead aims to learn those aspects, and promises to unlock greater capabilities with less manual effort. One particularly ambitious goal of meta-learning is to train general-purpose in-context learning algorithms from scratch, using only black-box models with minimal inductive bias. Such a model takes in training data, and produces test-set predictions across a wide range of problems, without any explicit definition ","authors_text":"James Harrison, Jascha Sohl-Dickstein, Louis Kirsch, Luke Metz","cross_cats":["cs.AI","cs.NE","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-12-08T18:30:22Z","title":"General-Purpose In-Context Learning by Meta-Learning Transformers"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.04458","kind":"arxiv","version":2},"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:d225d8b8aa3646578fc751776715859de685086e84bcca5371a0865dd9f324d4","target":"record","created_at":"2026-07-05T07:31:26Z","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":"2f8a21e02641a64358d50574e9930e269637da5ec5e0b0f5923c7833daa3f826","cross_cats_sorted":["cs.AI","cs.NE","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-12-08T18:30:22Z","title_canon_sha256":"3d4d21e20a7bf0ebd4bd4da441fdee42a94cb80b368cedd961c67db2ee08967d"},"schema_version":"1.0","source":{"id":"2212.04458","kind":"arxiv","version":2}},"canonical_sha256":"aeb892a942b61e1a00c29008e8bbaab88114f1f82cf38b76a34978bc07492012","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"aeb892a942b61e1a00c29008e8bbaab88114f1f82cf38b76a34978bc07492012","first_computed_at":"2026-07-05T07:31:26.482811Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:31:26.482811Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ZuqjrMz7J+VOSbxAOPoLZovqWdC0gOAg5xhdVTo9KQ0lREWXSQa5vphLdbUlWqsR1nsVGdfpYCGO0BWkKbDjCA==","signature_status":"signed_v1","signed_at":"2026-07-05T07:31:26.483265Z","signed_message":"canonical_sha256_bytes"},"source_id":"2212.04458","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d225d8b8aa3646578fc751776715859de685086e84bcca5371a0865dd9f324d4","sha256:0ea767c569f39622ed33d6728dff07838e2f2631b34478c2be3b6d5e4b57de57"],"state_sha256":"6df0165a708aef2f69cfe9d59e9bd522065dcdf6641d496ef14b9e481d2ba9bb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oLbNtsPzjfMnpOcWgXA41ZA1Lf9WNsbb1SeeXDlt3xPjFrh8b5K8a1LOVO0sSJCpbiLUf+n28Op7Y7rAj31kDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T10:13:05.157233Z","bundle_sha256":"79e8966dd389c3bf2ab5d87142b0e6d475a306c27775bf9e8fc40cfabcc6de4d"}}