{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:TOTKO4QLE4GHGIQMBJIH6IEVLQ","short_pith_number":"pith:TOTKO4QL","canonical_record":{"source":{"id":"2501.00829","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2025-01-01T13:19:58Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"659a5373fe9bfd2596aa4542c47844901de50d92d05673055fee1e5f0f7cf0b3","abstract_canon_sha256":"be2f25e15dd5befb86011ce802212d95b3bcdf5a86689fc49d7aa7e464103d7b"},"schema_version":"1.0"},"canonical_sha256":"9ba6a7720b270c73220c0a507f20955c0d587acc6b6d2ead30d332ae68f0da08","source":{"kind":"arxiv","id":"2501.00829","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.00829","created_at":"2026-07-05T11:19:23Z"},{"alias_kind":"arxiv_version","alias_value":"2501.00829v2","created_at":"2026-07-05T11:19:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.00829","created_at":"2026-07-05T11:19:23Z"},{"alias_kind":"pith_short_12","alias_value":"TOTKO4QLE4GH","created_at":"2026-07-05T11:19:23Z"},{"alias_kind":"pith_short_16","alias_value":"TOTKO4QLE4GHGIQM","created_at":"2026-07-05T11:19:23Z"},{"alias_kind":"pith_short_8","alias_value":"TOTKO4QL","created_at":"2026-07-05T11:19:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:TOTKO4QLE4GHGIQMBJIH6IEVLQ","target":"record","payload":{"canonical_record":{"source":{"id":"2501.00829","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2025-01-01T13:19:58Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"659a5373fe9bfd2596aa4542c47844901de50d92d05673055fee1e5f0f7cf0b3","abstract_canon_sha256":"be2f25e15dd5befb86011ce802212d95b3bcdf5a86689fc49d7aa7e464103d7b"},"schema_version":"1.0"},"canonical_sha256":"9ba6a7720b270c73220c0a507f20955c0d587acc6b6d2ead30d332ae68f0da08","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:19:23.369955Z","signature_b64":"o5acoKWZ6N8TLBovRpQ6G5pJcUwn+UYB1wHF6aIpwJ6JfNky2gdm1785OynYaeL5O5FYE7IwN/ICzQvtURK9Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9ba6a7720b270c73220c0a507f20955c0d587acc6b6d2ead30d332ae68f0da08","last_reissued_at":"2026-07-05T11:19:23.369518Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:19:23.369518Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2501.00829","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":"2026-07-05T11:19:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6n6LzO2EiAc5wnv0D6nnhbKtMz2/jmxLLXNMvHE3P/lVVoW0RXJffJoDxBlGEDgG4Bocnediopo+3uBQsFPFBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-28T02:05:25.189295Z"},"content_sha256":"17168892f872a486a8257a6dd0a50a0175194c948d002df7e147dbb49c936928","schema_version":"1.0","event_id":"sha256:17168892f872a486a8257a6dd0a50a0175194c948d002df7e147dbb49c936928"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:TOTKO4QLE4GHGIQMBJIH6IEVLQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"An LLM-Empowered Adaptive Evolutionary Algorithm For Multi-Component Deep Learning Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.NE","authors_text":"An Guo, Guoquan Wu, Haoxiang Tian, Jun Wei, Shuo Li, Tianwei Zhang, Xingshuo Han, Yuan Zhou. Jie Zhang","submitted_at":"2025-01-01T13:19:58Z","abstract_excerpt":"Multi-objective evolutionary algorithms (MOEAs) are widely used for searching optimal solutions in complex multi-component applications. Traditional MOEAs for multi-component deep learning (MCDL) systems face challenges in enhancing the search efficiency while maintaining the diversity. To combat these, this paper proposes $\\mu$MOEA, the first LLM-empowered adaptive evolutionary search algorithm to detect safety violations in MCDL systems. Inspired by the context-understanding ability of Large Language Models (LLMs), $\\mu$MOEA promotes the LLM to comprehend the optimization problem and generat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.00829","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/2501.00829/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":"2026-07-05T11:19:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"25bIrdRotJwqkngGVl/a+TIypuaAVcesqOXrleShhGdtIohzhZBn4TWT7Rfreh7xsguOifaJb6eBooHULeUAAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-28T02:05:25.189691Z"},"content_sha256":"a6e560ec05eadee3613942cbdacbf655927f75780b3c7ce59447e821fe8452a0","schema_version":"1.0","event_id":"sha256:a6e560ec05eadee3613942cbdacbf655927f75780b3c7ce59447e821fe8452a0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TOTKO4QLE4GHGIQMBJIH6IEVLQ/bundle.json","state_url":"https://pith.science/pith/TOTKO4QLE4GHGIQMBJIH6IEVLQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TOTKO4QLE4GHGIQMBJIH6IEVLQ/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":"2026-07-28T02:05:25Z","links":{"resolver":"https://pith.science/pith/TOTKO4QLE4GHGIQMBJIH6IEVLQ","bundle":"https://pith.science/pith/TOTKO4QLE4GHGIQMBJIH6IEVLQ/bundle.json","state":"https://pith.science/pith/TOTKO4QLE4GHGIQMBJIH6IEVLQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TOTKO4QLE4GHGIQMBJIH6IEVLQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:TOTKO4QLE4GHGIQMBJIH6IEVLQ","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":"be2f25e15dd5befb86011ce802212d95b3bcdf5a86689fc49d7aa7e464103d7b","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2025-01-01T13:19:58Z","title_canon_sha256":"659a5373fe9bfd2596aa4542c47844901de50d92d05673055fee1e5f0f7cf0b3"},"schema_version":"1.0","source":{"id":"2501.00829","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.00829","created_at":"2026-07-05T11:19:23Z"},{"alias_kind":"arxiv_version","alias_value":"2501.00829v2","created_at":"2026-07-05T11:19:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.00829","created_at":"2026-07-05T11:19:23Z"},{"alias_kind":"pith_short_12","alias_value":"TOTKO4QLE4GH","created_at":"2026-07-05T11:19:23Z"},{"alias_kind":"pith_short_16","alias_value":"TOTKO4QLE4GHGIQM","created_at":"2026-07-05T11:19:23Z"},{"alias_kind":"pith_short_8","alias_value":"TOTKO4QL","created_at":"2026-07-05T11:19:23Z"}],"graph_snapshots":[{"event_id":"sha256:a6e560ec05eadee3613942cbdacbf655927f75780b3c7ce59447e821fe8452a0","target":"graph","created_at":"2026-07-05T11:19:23Z","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/2501.00829/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multi-objective evolutionary algorithms (MOEAs) are widely used for searching optimal solutions in complex multi-component applications. Traditional MOEAs for multi-component deep learning (MCDL) systems face challenges in enhancing the search efficiency while maintaining the diversity. To combat these, this paper proposes $\\mu$MOEA, the first LLM-empowered adaptive evolutionary search algorithm to detect safety violations in MCDL systems. Inspired by the context-understanding ability of Large Language Models (LLMs), $\\mu$MOEA promotes the LLM to comprehend the optimization problem and generat","authors_text":"An Guo, Guoquan Wu, Haoxiang Tian, Jun Wei, Shuo Li, Tianwei Zhang, Xingshuo Han, Yuan Zhou. Jie Zhang","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2025-01-01T13:19:58Z","title":"An LLM-Empowered Adaptive Evolutionary Algorithm For Multi-Component Deep Learning Systems"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.00829","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":"sha256:17168892f872a486a8257a6dd0a50a0175194c948d002df7e147dbb49c936928","target":"record","created_at":"2026-07-05T11:19:23Z","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":"be2f25e15dd5befb86011ce802212d95b3bcdf5a86689fc49d7aa7e464103d7b","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2025-01-01T13:19:58Z","title_canon_sha256":"659a5373fe9bfd2596aa4542c47844901de50d92d05673055fee1e5f0f7cf0b3"},"schema_version":"1.0","source":{"id":"2501.00829","kind":"arxiv","version":2}},"canonical_sha256":"9ba6a7720b270c73220c0a507f20955c0d587acc6b6d2ead30d332ae68f0da08","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9ba6a7720b270c73220c0a507f20955c0d587acc6b6d2ead30d332ae68f0da08","first_computed_at":"2026-07-05T11:19:23.369518Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:19:23.369518Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"o5acoKWZ6N8TLBovRpQ6G5pJcUwn+UYB1wHF6aIpwJ6JfNky2gdm1785OynYaeL5O5FYE7IwN/ICzQvtURK9Bg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:19:23.369955Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.00829","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:17168892f872a486a8257a6dd0a50a0175194c948d002df7e147dbb49c936928","sha256:a6e560ec05eadee3613942cbdacbf655927f75780b3c7ce59447e821fe8452a0"],"state_sha256":"e833f24ba75953e2a8c208e37444c01ae1449e9e802deb47785343aceeaf585a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"maPOOLf1lVuOu0sW4MObvAw9s4ehBUiVKz4ENs0wNcDJS4uJPTlrgvXz6ul/42A59Qq6TOetj3O3ls3808Z8BA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-28T02:05:25.192052Z","bundle_sha256":"e604e5bf85814ed8396dab2aad83816a50eb5dfa53cebe4fc4d9c8e77ee10448"}}