{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:MGM5GVVLEG2FH5YDVOVGGXX7SI","short_pith_number":"pith:MGM5GVVL","canonical_record":{"source":{"id":"2505.19205","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-25T16:05:41Z","cross_cats_sorted":["cs.AI","cs.MA"],"title_canon_sha256":"b44ecb295ebf4f2b9c16d9bb5ff561f8b4e12265bb66ce2962bfde78da42040f","abstract_canon_sha256":"64148ac9a3fb63978345f3f5b4ad638a1aa09510d0406fdde92a0a2ad652d63c"},"schema_version":"1.0"},"canonical_sha256":"6199d356ab21b453f703abaa635eff920c9b2d3540bd9b592ee4ba1d84d589b3","source":{"kind":"arxiv","id":"2505.19205","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.19205","created_at":"2026-07-05T11:11:22Z"},{"alias_kind":"arxiv_version","alias_value":"2505.19205v2","created_at":"2026-07-05T11:11:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.19205","created_at":"2026-07-05T11:11:22Z"},{"alias_kind":"pith_short_12","alias_value":"MGM5GVVLEG2F","created_at":"2026-07-05T11:11:22Z"},{"alias_kind":"pith_short_16","alias_value":"MGM5GVVLEG2FH5YD","created_at":"2026-07-05T11:11:22Z"},{"alias_kind":"pith_short_8","alias_value":"MGM5GVVL","created_at":"2026-07-05T11:11:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:MGM5GVVLEG2FH5YDVOVGGXX7SI","target":"record","payload":{"canonical_record":{"source":{"id":"2505.19205","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-25T16:05:41Z","cross_cats_sorted":["cs.AI","cs.MA"],"title_canon_sha256":"b44ecb295ebf4f2b9c16d9bb5ff561f8b4e12265bb66ce2962bfde78da42040f","abstract_canon_sha256":"64148ac9a3fb63978345f3f5b4ad638a1aa09510d0406fdde92a0a2ad652d63c"},"schema_version":"1.0"},"canonical_sha256":"6199d356ab21b453f703abaa635eff920c9b2d3540bd9b592ee4ba1d84d589b3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:11:22.756080Z","signature_b64":"l+fxViWe69wWA4c63JURL2icQKhvrhCS7PTGjDd0p5txguw4u+hUGgZlST2svCaFf1gdXRP/2QXYIo+cUVJsDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6199d356ab21b453f703abaa635eff920c9b2d3540bd9b592ee4ba1d84d589b3","last_reissued_at":"2026-07-05T11:11:22.755590Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:11:22.755590Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.19205","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":"2026-07-05T11:11:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tOGoe9ZL5bmxeG41fKpsOZF3qvG0Y+qDDxdAJgRnFPRMN3CmwgKvKKQq2GKsebadBu76GKm6A5BqulJI/7DUBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T03:51:43.435952Z"},"content_sha256":"2b0efe7e26a34af0fc433cd9bf58c457921729300363aed7e5720b14fddec46a","schema_version":"1.0","event_id":"sha256:2b0efe7e26a34af0fc433cd9bf58c457921729300363aed7e5720b14fddec46a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:MGM5GVVLEG2FH5YDVOVGGXX7SI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"OptiMindTune: A Multi-Agent Framework for Intelligent Hyperparameter Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.MA"],"primary_cat":"cs.LG","authors_text":"Meher Bhaskar Madiraju, Meher Sai Preetam Madiraju","submitted_at":"2025-05-25T16:05:41Z","abstract_excerpt":"Hyperparameter optimization (HPO) is a critical yet challenging aspect of machine learning model development, significantly impacting model performance and generalization. Traditional HPO methods often struggle with high dimensionality, complex interdependencies, and computational expense. This paper introduces OptiMindTune, a novel multi-agent framework designed to intelligently and efficiently optimize hyperparameters. OptiMindTune leverages the collaborative intelligence of three specialized AI agents -- a Recommender Agent, an Evaluator Agent, and a Decision Agent -- each powered by Google"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.19205","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/2505.19205/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":"2026-07-05T11:11:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Bvv3LMqBfqTpb1lWJ9XmmUryVW27F3gT6C72Wh/vLrs6Lgf5usFrrHOLwaL7PWvJpvOkaDIkkAHXrMmePI6YDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T03:51:43.436501Z"},"content_sha256":"423967243a1de7cd99636d9321cb06f30b96926f6b8eadae243055215992ee90","schema_version":"1.0","event_id":"sha256:423967243a1de7cd99636d9321cb06f30b96926f6b8eadae243055215992ee90"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MGM5GVVLEG2FH5YDVOVGGXX7SI/bundle.json","state_url":"https://pith.science/pith/MGM5GVVLEG2FH5YDVOVGGXX7SI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MGM5GVVLEG2FH5YDVOVGGXX7SI/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":"2026-08-06T03:51:43Z","links":{"resolver":"https://pith.science/pith/MGM5GVVLEG2FH5YDVOVGGXX7SI","bundle":"https://pith.science/pith/MGM5GVVLEG2FH5YDVOVGGXX7SI/bundle.json","state":"https://pith.science/pith/MGM5GVVLEG2FH5YDVOVGGXX7SI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MGM5GVVLEG2FH5YDVOVGGXX7SI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:MGM5GVVLEG2FH5YDVOVGGXX7SI","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":"64148ac9a3fb63978345f3f5b4ad638a1aa09510d0406fdde92a0a2ad652d63c","cross_cats_sorted":["cs.AI","cs.MA"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-25T16:05:41Z","title_canon_sha256":"b44ecb295ebf4f2b9c16d9bb5ff561f8b4e12265bb66ce2962bfde78da42040f"},"schema_version":"1.0","source":{"id":"2505.19205","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.19205","created_at":"2026-07-05T11:11:22Z"},{"alias_kind":"arxiv_version","alias_value":"2505.19205v2","created_at":"2026-07-05T11:11:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.19205","created_at":"2026-07-05T11:11:22Z"},{"alias_kind":"pith_short_12","alias_value":"MGM5GVVLEG2F","created_at":"2026-07-05T11:11:22Z"},{"alias_kind":"pith_short_16","alias_value":"MGM5GVVLEG2FH5YD","created_at":"2026-07-05T11:11:22Z"},{"alias_kind":"pith_short_8","alias_value":"MGM5GVVL","created_at":"2026-07-05T11:11:22Z"}],"graph_snapshots":[{"event_id":"sha256:423967243a1de7cd99636d9321cb06f30b96926f6b8eadae243055215992ee90","target":"graph","created_at":"2026-07-05T11:11:22Z","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/2505.19205/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Hyperparameter optimization (HPO) is a critical yet challenging aspect of machine learning model development, significantly impacting model performance and generalization. Traditional HPO methods often struggle with high dimensionality, complex interdependencies, and computational expense. This paper introduces OptiMindTune, a novel multi-agent framework designed to intelligently and efficiently optimize hyperparameters. OptiMindTune leverages the collaborative intelligence of three specialized AI agents -- a Recommender Agent, an Evaluator Agent, and a Decision Agent -- each powered by Google","authors_text":"Meher Bhaskar Madiraju, Meher Sai Preetam Madiraju","cross_cats":["cs.AI","cs.MA"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-25T16:05:41Z","title":"OptiMindTune: A Multi-Agent Framework for Intelligent Hyperparameter Optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.19205","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":"sha256:2b0efe7e26a34af0fc433cd9bf58c457921729300363aed7e5720b14fddec46a","target":"record","created_at":"2026-07-05T11:11:22Z","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":"64148ac9a3fb63978345f3f5b4ad638a1aa09510d0406fdde92a0a2ad652d63c","cross_cats_sorted":["cs.AI","cs.MA"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-25T16:05:41Z","title_canon_sha256":"b44ecb295ebf4f2b9c16d9bb5ff561f8b4e12265bb66ce2962bfde78da42040f"},"schema_version":"1.0","source":{"id":"2505.19205","kind":"arxiv","version":2}},"canonical_sha256":"6199d356ab21b453f703abaa635eff920c9b2d3540bd9b592ee4ba1d84d589b3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6199d356ab21b453f703abaa635eff920c9b2d3540bd9b592ee4ba1d84d589b3","first_computed_at":"2026-07-05T11:11:22.755590Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:11:22.755590Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"l+fxViWe69wWA4c63JURL2icQKhvrhCS7PTGjDd0p5txguw4u+hUGgZlST2svCaFf1gdXRP/2QXYIo+cUVJsDA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:11:22.756080Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.19205","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2b0efe7e26a34af0fc433cd9bf58c457921729300363aed7e5720b14fddec46a","sha256:423967243a1de7cd99636d9321cb06f30b96926f6b8eadae243055215992ee90"],"state_sha256":"7c496606081ff38a537e3a80e57a6b43e4897a617ed58ad5b365e44ba8907a25"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sVn6GbXW6RAT05RY1hXzNMn1DULu8sV9QdEOwvqboYOACTSfU7wYM3TpEurmcjc3up2PYl+kaWqIJCEGfdbeAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T03:51:43.442458Z","bundle_sha256":"71f7b409fd30bbd699a55eedcb1dfbbd5b17081921bf479e2f13ee3390f256ed"}}