{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:4ZT5NTHVNOKTRE5GW2FNXA2RPZ","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":"33f5334bdc5eb902ec3159ad3e95b7fff7b5c5867010fef256ab1c696f946712","cross_cats_sorted":["cs.AI","math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-10T07:52:35Z","title_canon_sha256":"9dfe3de6fcafa3041605f2b4a562fa42d7a1f4c3d1e9856778f1a343cbb91055"},"schema_version":"1.0","source":{"id":"2506.08533","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.08533","created_at":"2026-07-05T11:18:51Z"},{"alias_kind":"arxiv_version","alias_value":"2506.08533v1","created_at":"2026-07-05T11:18:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.08533","created_at":"2026-07-05T11:18:51Z"},{"alias_kind":"pith_short_12","alias_value":"4ZT5NTHVNOKT","created_at":"2026-07-05T11:18:51Z"},{"alias_kind":"pith_short_16","alias_value":"4ZT5NTHVNOKTRE5G","created_at":"2026-07-05T11:18:51Z"},{"alias_kind":"pith_short_8","alias_value":"4ZT5NTHV","created_at":"2026-07-05T11:18:51Z"}],"graph_snapshots":[{"event_id":"sha256:9572ca595dc50126595688695138921c8ee35e33648c310e5c95763f087b5a64","target":"graph","created_at":"2026-07-05T11:18:51Z","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/2506.08533/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper introduces Evolutionary Multi-Objective Network Architecture Search (EMNAS) for the first time to optimize neural network architectures in large-scale Reinforcement Learning (RL) for Autonomous Driving (AD). EMNAS uses genetic algorithms to automate network design, tailored to enhance rewards and reduce model size without compromising performance. Additionally, parallelization techniques are employed to accelerate the search, and teacher-student methodologies are implemented to ensure scalable optimization. This research underscores the potential of transfer learning as a robust fra","authors_text":"Alexandra Gianzina, Andreas Ebert, Hanno Gottschalk, Nihal Acharya Adde","cross_cats":["cs.AI","math.OC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-10T07:52:35Z","title":"Robust Evolutionary Multi-Objective Network Architecture Search for Reinforcement Learning (EMNAS-RL)"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.08533","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:da63d286683805c76d09ac93932aa0424be7f67751b3462f2d0b51a2c9d95799","target":"record","created_at":"2026-07-05T11:18:51Z","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":"33f5334bdc5eb902ec3159ad3e95b7fff7b5c5867010fef256ab1c696f946712","cross_cats_sorted":["cs.AI","math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-10T07:52:35Z","title_canon_sha256":"9dfe3de6fcafa3041605f2b4a562fa42d7a1f4c3d1e9856778f1a343cbb91055"},"schema_version":"1.0","source":{"id":"2506.08533","kind":"arxiv","version":1}},"canonical_sha256":"e667d6ccf56b953893a6b68adb83517e4d1ef1fe08c2e440e103746b33353496","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e667d6ccf56b953893a6b68adb83517e4d1ef1fe08c2e440e103746b33353496","first_computed_at":"2026-07-05T11:18:51.376395Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:18:51.376395Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"i+wD5eYPV34ZBskEGU9rcBU1Ui5nWwFlfqHkIxedykChy+SHwy/8fHMli8eQK+uzwXVC96qRQtupvdkgVbZTBg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:18:51.376874Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.08533","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:da63d286683805c76d09ac93932aa0424be7f67751b3462f2d0b51a2c9d95799","sha256:9572ca595dc50126595688695138921c8ee35e33648c310e5c95763f087b5a64"],"state_sha256":"83ddeadc808593c745c9171442b2ded7dc7175851960536525d409ce08b7ff2f"}