{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:KN2YQQKWFF4GMGLQZDBARF2ZHP","short_pith_number":"pith:KN2YQQKW","canonical_record":{"source":{"id":"2508.19733","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-27T09:57:45Z","cross_cats_sorted":[],"title_canon_sha256":"c2af710d07342abb7868f96b569eca06379de7ffd18d3065edff7e79ee504dd5","abstract_canon_sha256":"c2dc83dd77e5a3381661c0e262f2a4462e66ec610d0ab28876b2e1d18b020986"},"schema_version":"1.0"},"canonical_sha256":"53758841562978661970c8c20897593bf72dbba4218ee13d0ca2c108b7649bad","source":{"kind":"arxiv","id":"2508.19733","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.19733","created_at":"2026-07-05T12:00:52Z"},{"alias_kind":"arxiv_version","alias_value":"2508.19733v2","created_at":"2026-07-05T12:00:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.19733","created_at":"2026-07-05T12:00:52Z"},{"alias_kind":"pith_short_12","alias_value":"KN2YQQKWFF4G","created_at":"2026-07-05T12:00:52Z"},{"alias_kind":"pith_short_16","alias_value":"KN2YQQKWFF4GMGLQ","created_at":"2026-07-05T12:00:52Z"},{"alias_kind":"pith_short_8","alias_value":"KN2YQQKW","created_at":"2026-07-05T12:00:52Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:KN2YQQKWFF4GMGLQZDBARF2ZHP","target":"record","payload":{"canonical_record":{"source":{"id":"2508.19733","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-27T09:57:45Z","cross_cats_sorted":[],"title_canon_sha256":"c2af710d07342abb7868f96b569eca06379de7ffd18d3065edff7e79ee504dd5","abstract_canon_sha256":"c2dc83dd77e5a3381661c0e262f2a4462e66ec610d0ab28876b2e1d18b020986"},"schema_version":"1.0"},"canonical_sha256":"53758841562978661970c8c20897593bf72dbba4218ee13d0ca2c108b7649bad","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:00:52.429577Z","signature_b64":"fLSYLQMMA5RjOQG0cblKqsulyqo/GPqBKzVip2VrgNYp7+C5gZT/P/jz4j2cNElqKUEr2Y1sfU6HCiiyNNUyAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"53758841562978661970c8c20897593bf72dbba4218ee13d0ca2c108b7649bad","last_reissued_at":"2026-07-05T12:00:52.429041Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:00:52.429041Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2508.19733","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":"2026-07-05T12:00:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vDjlwSAjUVxel37MA3RfuchIivbqoyg8sP/XDZh9HY+pbFi8g4qvCgiS4g2BN+PJ4Jf3qlw2Jir6Eo+cC/iwDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T15:07:05.138271Z"},"content_sha256":"d3cc2879043337b26ba29ce14b71ff91b4a1a818698cc9fab5396d6fd7369631","schema_version":"1.0","event_id":"sha256:d3cc2879043337b26ba29ce14b71ff91b4a1a818698cc9fab5396d6fd7369631"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:KN2YQQKWFF4GMGLQZDBARF2ZHP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Tune My Adam, Please!","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Frank Hutter, Samuel M\\\"uller, Steven Adriaensen, Theodoros Athanasiadis","submitted_at":"2025-08-27T09:57:45Z","abstract_excerpt":"The Adam optimizer remains one of the most widely used optimizers in deep learning, and effectively tuning its hyperparameters is key to optimizing performance. However, tuning can be tedious and costly. Freeze-thaw Bayesian Optimization (BO) is a recent promising approach for low-budget hyperparameter tuning, but is limited by generic surrogates without prior knowledge of how hyperparameters affect learning. We propose Adam-PFN, a new surrogate model for Freeze-thaw BO of Adam's hyperparameters, pre-trained on learning curves from TaskSet, together with a new learning curve augmentation metho"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.19733","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/2508.19733/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":"2026-07-05T12:00:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QV0l/Jwb3ftxqr0rSSHvYDJpxE2M7yEJC4LWYvnai3xZausPagQ8JuEoq3VmB6CcWwNbaIaT4qzOKEdhWRwKDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T15:07:05.138773Z"},"content_sha256":"c0c87aa10bce3e38f808698fad4dea0237666035bb80bd40d74b21975a1f758c","schema_version":"1.0","event_id":"sha256:c0c87aa10bce3e38f808698fad4dea0237666035bb80bd40d74b21975a1f758c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KN2YQQKWFF4GMGLQZDBARF2ZHP/bundle.json","state_url":"https://pith.science/pith/KN2YQQKWFF4GMGLQZDBARF2ZHP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KN2YQQKWFF4GMGLQZDBARF2ZHP/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":"2026-08-06T15:07:05Z","links":{"resolver":"https://pith.science/pith/KN2YQQKWFF4GMGLQZDBARF2ZHP","bundle":"https://pith.science/pith/KN2YQQKWFF4GMGLQZDBARF2ZHP/bundle.json","state":"https://pith.science/pith/KN2YQQKWFF4GMGLQZDBARF2ZHP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KN2YQQKWFF4GMGLQZDBARF2ZHP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:KN2YQQKWFF4GMGLQZDBARF2ZHP","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":"c2dc83dd77e5a3381661c0e262f2a4462e66ec610d0ab28876b2e1d18b020986","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-27T09:57:45Z","title_canon_sha256":"c2af710d07342abb7868f96b569eca06379de7ffd18d3065edff7e79ee504dd5"},"schema_version":"1.0","source":{"id":"2508.19733","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.19733","created_at":"2026-07-05T12:00:52Z"},{"alias_kind":"arxiv_version","alias_value":"2508.19733v2","created_at":"2026-07-05T12:00:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.19733","created_at":"2026-07-05T12:00:52Z"},{"alias_kind":"pith_short_12","alias_value":"KN2YQQKWFF4G","created_at":"2026-07-05T12:00:52Z"},{"alias_kind":"pith_short_16","alias_value":"KN2YQQKWFF4GMGLQ","created_at":"2026-07-05T12:00:52Z"},{"alias_kind":"pith_short_8","alias_value":"KN2YQQKW","created_at":"2026-07-05T12:00:52Z"}],"graph_snapshots":[{"event_id":"sha256:c0c87aa10bce3e38f808698fad4dea0237666035bb80bd40d74b21975a1f758c","target":"graph","created_at":"2026-07-05T12:00:52Z","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/2508.19733/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The Adam optimizer remains one of the most widely used optimizers in deep learning, and effectively tuning its hyperparameters is key to optimizing performance. However, tuning can be tedious and costly. Freeze-thaw Bayesian Optimization (BO) is a recent promising approach for low-budget hyperparameter tuning, but is limited by generic surrogates without prior knowledge of how hyperparameters affect learning. We propose Adam-PFN, a new surrogate model for Freeze-thaw BO of Adam's hyperparameters, pre-trained on learning curves from TaskSet, together with a new learning curve augmentation metho","authors_text":"Frank Hutter, Samuel M\\\"uller, Steven Adriaensen, Theodoros Athanasiadis","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-27T09:57:45Z","title":"Tune My Adam, Please!"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.19733","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:d3cc2879043337b26ba29ce14b71ff91b4a1a818698cc9fab5396d6fd7369631","target":"record","created_at":"2026-07-05T12:00:52Z","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":"c2dc83dd77e5a3381661c0e262f2a4462e66ec610d0ab28876b2e1d18b020986","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-27T09:57:45Z","title_canon_sha256":"c2af710d07342abb7868f96b569eca06379de7ffd18d3065edff7e79ee504dd5"},"schema_version":"1.0","source":{"id":"2508.19733","kind":"arxiv","version":2}},"canonical_sha256":"53758841562978661970c8c20897593bf72dbba4218ee13d0ca2c108b7649bad","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"53758841562978661970c8c20897593bf72dbba4218ee13d0ca2c108b7649bad","first_computed_at":"2026-07-05T12:00:52.429041Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T12:00:52.429041Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fLSYLQMMA5RjOQG0cblKqsulyqo/GPqBKzVip2VrgNYp7+C5gZT/P/jz4j2cNElqKUEr2Y1sfU6HCiiyNNUyAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T12:00:52.429577Z","signed_message":"canonical_sha256_bytes"},"source_id":"2508.19733","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d3cc2879043337b26ba29ce14b71ff91b4a1a818698cc9fab5396d6fd7369631","sha256:c0c87aa10bce3e38f808698fad4dea0237666035bb80bd40d74b21975a1f758c"],"state_sha256":"4caf807a10bbd3f46416680af4e9fa40e4cc513f21963c3a2c6e52c847278280"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5rSq2OX9Rrppi1ClnhLLpAJnUuUm8rCie4oPFQ3hpCX2pA3aHAaPSkt+gj29mE2NHslRs5GewQIBCu3jyqxnAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T15:07:05.143117Z","bundle_sha256":"d39248eb2d4fc306540930872b127a1e401dae9c51524f815aa8fef39de37696"}}