{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:TXG7E54IC2TNTS3W3XETRE2HKT","short_pith_number":"pith:TXG7E54I","canonical_record":{"source":{"id":"2405.19420","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-29T18:01:58Z","cross_cats_sorted":["cs.AI","q-bio.NC"],"title_canon_sha256":"d29febcf8cbb1a4d709d8dc7775deea9265496ece0a96ab15c33533cdd8e3c80","abstract_canon_sha256":"65e84e0efb0a05f6e0055faf2bca27a78fd7e8caa231f4848dc68054d13c3be2"},"schema_version":"1.0"},"canonical_sha256":"9dcdf2778816a6d9cb76ddc938934754d27067ebb7c8872a5fbd36b7bd11ef30","source":{"kind":"arxiv","id":"2405.19420","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.19420","created_at":"2026-07-05T10:07:41Z"},{"alias_kind":"arxiv_version","alias_value":"2405.19420v3","created_at":"2026-07-05T10:07:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.19420","created_at":"2026-07-05T10:07:41Z"},{"alias_kind":"pith_short_12","alias_value":"TXG7E54IC2TN","created_at":"2026-07-05T10:07:41Z"},{"alias_kind":"pith_short_16","alias_value":"TXG7E54IC2TNTS3W","created_at":"2026-07-05T10:07:41Z"},{"alias_kind":"pith_short_8","alias_value":"TXG7E54I","created_at":"2026-07-05T10:07:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:TXG7E54IC2TNTS3W3XETRE2HKT","target":"record","payload":{"canonical_record":{"source":{"id":"2405.19420","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-29T18:01:58Z","cross_cats_sorted":["cs.AI","q-bio.NC"],"title_canon_sha256":"d29febcf8cbb1a4d709d8dc7775deea9265496ece0a96ab15c33533cdd8e3c80","abstract_canon_sha256":"65e84e0efb0a05f6e0055faf2bca27a78fd7e8caa231f4848dc68054d13c3be2"},"schema_version":"1.0"},"canonical_sha256":"9dcdf2778816a6d9cb76ddc938934754d27067ebb7c8872a5fbd36b7bd11ef30","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:07:41.825761Z","signature_b64":"oCFsxU8KbpKGL/t2Q2dylsnybiOKKhR80pDyq+V+4Xqz39wf1ewXShAWDc8/DwL5oJhFFoAjLSJ6EZ9So6HwDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9dcdf2778816a6d9cb76ddc938934754d27067ebb7c8872a5fbd36b7bd11ef30","last_reissued_at":"2026-07-05T10:07:41.825269Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:07:41.825269Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2405.19420","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:07:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0VwCrqSJy5eUyPlIhgHZAxFQzM4daACtcQG4He5v6/pT68JHIwI+eF0lfAXEj4UkF8OcLOVJOknQHQmIFGy0AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T03:45:29.633708Z"},"content_sha256":"476f538c6aa6e9e0418871b9716c2fdd6ff7e07f03cdd25c0a242b2ee479a0d3","schema_version":"1.0","event_id":"sha256:476f538c6aa6e9e0418871b9716c2fdd6ff7e07f03cdd25c0a242b2ee479a0d3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:TXG7E54IC2TNTS3W3XETRE2HKT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Human-Aligned Representations with Contrastive Learning and Generative Similarity","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","q-bio.NC"],"primary_cat":"cs.LG","authors_text":"Declan Campbell, Gianluca Bencomo, Jake Snell, Liyi Zhang, Raja Marjieh, Sreejan Kumar, Thomas L. Griffiths","submitted_at":"2024-05-29T18:01:58Z","abstract_excerpt":"Humans rely on effective representations to learn from few examples and abstract useful information from sensory data. Inducing such representations in machine learning models has been shown to improve their performance on various benchmarks such as few-shot learning and robustness. However, finding effective training procedures to achieve that goal can be challenging as psychologically rich training data such as human similarity judgments are expensive to scale, and Bayesian models of human inductive biases are often intractable for complex, realistic domains. Here, we address this challenge "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.19420","kind":"arxiv","version":3},"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/2405.19420/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":"2026-07-05T10:07:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oMOTH9BJqkzGvt5kZxoTbSiY39UnTAP9PgV6Fj0vjdosykga7QNuPyu4MkaXxq1XVdCJuGmD6jMF79Uv4qbVDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T03:45:29.634225Z"},"content_sha256":"a93f12f7aa279666d346110a6558c984e926ae625b792919848573593361e7a5","schema_version":"1.0","event_id":"sha256:a93f12f7aa279666d346110a6558c984e926ae625b792919848573593361e7a5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TXG7E54IC2TNTS3W3XETRE2HKT/bundle.json","state_url":"https://pith.science/pith/TXG7E54IC2TNTS3W3XETRE2HKT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TXG7E54IC2TNTS3W3XETRE2HKT/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":"2026-08-10T03:45:29Z","links":{"resolver":"https://pith.science/pith/TXG7E54IC2TNTS3W3XETRE2HKT","bundle":"https://pith.science/pith/TXG7E54IC2TNTS3W3XETRE2HKT/bundle.json","state":"https://pith.science/pith/TXG7E54IC2TNTS3W3XETRE2HKT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TXG7E54IC2TNTS3W3XETRE2HKT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:TXG7E54IC2TNTS3W3XETRE2HKT","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":"65e84e0efb0a05f6e0055faf2bca27a78fd7e8caa231f4848dc68054d13c3be2","cross_cats_sorted":["cs.AI","q-bio.NC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-29T18:01:58Z","title_canon_sha256":"d29febcf8cbb1a4d709d8dc7775deea9265496ece0a96ab15c33533cdd8e3c80"},"schema_version":"1.0","source":{"id":"2405.19420","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.19420","created_at":"2026-07-05T10:07:41Z"},{"alias_kind":"arxiv_version","alias_value":"2405.19420v3","created_at":"2026-07-05T10:07:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.19420","created_at":"2026-07-05T10:07:41Z"},{"alias_kind":"pith_short_12","alias_value":"TXG7E54IC2TN","created_at":"2026-07-05T10:07:41Z"},{"alias_kind":"pith_short_16","alias_value":"TXG7E54IC2TNTS3W","created_at":"2026-07-05T10:07:41Z"},{"alias_kind":"pith_short_8","alias_value":"TXG7E54I","created_at":"2026-07-05T10:07:41Z"}],"graph_snapshots":[{"event_id":"sha256:a93f12f7aa279666d346110a6558c984e926ae625b792919848573593361e7a5","target":"graph","created_at":"2026-07-05T10:07:41Z","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/2405.19420/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Humans rely on effective representations to learn from few examples and abstract useful information from sensory data. Inducing such representations in machine learning models has been shown to improve their performance on various benchmarks such as few-shot learning and robustness. However, finding effective training procedures to achieve that goal can be challenging as psychologically rich training data such as human similarity judgments are expensive to scale, and Bayesian models of human inductive biases are often intractable for complex, realistic domains. Here, we address this challenge ","authors_text":"Declan Campbell, Gianluca Bencomo, Jake Snell, Liyi Zhang, Raja Marjieh, Sreejan Kumar, Thomas L. Griffiths","cross_cats":["cs.AI","q-bio.NC"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-29T18:01:58Z","title":"Learning Human-Aligned Representations with Contrastive Learning and Generative Similarity"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.19420","kind":"arxiv","version":3},"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":"sha256:476f538c6aa6e9e0418871b9716c2fdd6ff7e07f03cdd25c0a242b2ee479a0d3","target":"record","created_at":"2026-07-05T10:07:41Z","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":"65e84e0efb0a05f6e0055faf2bca27a78fd7e8caa231f4848dc68054d13c3be2","cross_cats_sorted":["cs.AI","q-bio.NC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-29T18:01:58Z","title_canon_sha256":"d29febcf8cbb1a4d709d8dc7775deea9265496ece0a96ab15c33533cdd8e3c80"},"schema_version":"1.0","source":{"id":"2405.19420","kind":"arxiv","version":3}},"canonical_sha256":"9dcdf2778816a6d9cb76ddc938934754d27067ebb7c8872a5fbd36b7bd11ef30","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9dcdf2778816a6d9cb76ddc938934754d27067ebb7c8872a5fbd36b7bd11ef30","first_computed_at":"2026-07-05T10:07:41.825269Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:07:41.825269Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"oCFsxU8KbpKGL/t2Q2dylsnybiOKKhR80pDyq+V+4Xqz39wf1ewXShAWDc8/DwL5oJhFFoAjLSJ6EZ9So6HwDg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:07:41.825761Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.19420","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:476f538c6aa6e9e0418871b9716c2fdd6ff7e07f03cdd25c0a242b2ee479a0d3","sha256:a93f12f7aa279666d346110a6558c984e926ae625b792919848573593361e7a5"],"state_sha256":"420538f4fa340a0de627eba054ca5c0010048dd3b14b0c2ed92dfd477374e2a4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"34gX5p38sbG2m3TzlJGVfOQNrl/PYlqp8Ch2gutDjA3oW4boW7aBy9EAfBL0JKjikDYAZP5qSiH+SRB0WrTjAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T03:45:29.639727Z","bundle_sha256":"2e7f29f829772918247f53715f94bb6fe82ff0d4d0a637baa31d7ca091a5a947"}}