{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:STVWV6P2NB6PWEGELLUOW57FMF","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":"548d23a34f9cca1bc49e5ce625d739ea7c91668a35334db7fa8b999429997986","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2025-06-09T09:03:05Z","title_canon_sha256":"465974ab59a573d4d0970b96862a3e9303e3192d07f15db7335fe4168335fc51"},"schema_version":"1.0","source":{"id":"2506.07563","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.07563","created_at":"2026-07-05T11:19:28Z"},{"alias_kind":"arxiv_version","alias_value":"2506.07563v3","created_at":"2026-07-05T11:19:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.07563","created_at":"2026-07-05T11:19:28Z"},{"alias_kind":"pith_short_12","alias_value":"STVWV6P2NB6P","created_at":"2026-07-05T11:19:28Z"},{"alias_kind":"pith_short_16","alias_value":"STVWV6P2NB6PWEGE","created_at":"2026-07-05T11:19:28Z"},{"alias_kind":"pith_short_8","alias_value":"STVWV6P2","created_at":"2026-07-05T11:19:28Z"}],"graph_snapshots":[{"event_id":"sha256:f7a8dcf28651343cde6b9ef7e7c8819c1047881c3b016da51e2e29c51e0102e7","target":"graph","created_at":"2026-07-05T11:19:28Z","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/2506.07563/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Personalized recommendation systems must adapt to user interactions across different domains. Traditional approaches like MLoRA apply a single adaptation per domain but lack flexibility in handling diverse user behaviors. To address this, we propose MoE-MLoRA, a mixture-of-experts framework where each expert is first trained independently to specialize in its domain before a gating network is trained to weight their contributions dynamically. We evaluate MoE-MLoRA across eight CTR models on Movielens and Taobao, showing that it improves performance in large-scale, dynamic datasets (+1.45 Weigh","authors_text":"Aviel Ben Siman Tov, Eyal German, Ken Yaggel","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2025-06-09T09:03:05Z","title":"MoE-MLoRA for Multi-Domain CTR Prediction: Efficient Adaptation with Expert Specialization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.07563","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":"sha256:61fd4d30badc05eb6f326ce7f0f7556383d64e280e8cd9d843f01d1c9b96bc79","target":"record","created_at":"2026-07-05T11:19:28Z","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":"548d23a34f9cca1bc49e5ce625d739ea7c91668a35334db7fa8b999429997986","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2025-06-09T09:03:05Z","title_canon_sha256":"465974ab59a573d4d0970b96862a3e9303e3192d07f15db7335fe4168335fc51"},"schema_version":"1.0","source":{"id":"2506.07563","kind":"arxiv","version":3}},"canonical_sha256":"94eb6af9fa687cfb10c45ae8eb77e5614d83d1b56ea49c75d3d92b535dd07d1a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"94eb6af9fa687cfb10c45ae8eb77e5614d83d1b56ea49c75d3d92b535dd07d1a","first_computed_at":"2026-07-05T11:19:28.730663Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:19:28.730663Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Y9vxF3UKpMvdfvPkrGcE6/zRcm6yKqhrA3UCR4tE+2fQnx6F7HrtE2SAiUIWSJsLbz8ZGu7wyomuM/9OfAOaAw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:19:28.731136Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.07563","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:61fd4d30badc05eb6f326ce7f0f7556383d64e280e8cd9d843f01d1c9b96bc79","sha256:f7a8dcf28651343cde6b9ef7e7c8819c1047881c3b016da51e2e29c51e0102e7"],"state_sha256":"ab1571a2ffe2458dfddeb8b83fb4a38e47818641db447e13db46c1d02c96fd1a"}