{"id":"W7125974758","doi":"10.1109/ase63991.2025.00035","title":"LogMoE: Lightweight Expert Mixture for Cross-System Log Anomaly Detection","year":2025,"lang":"","type":"article","venue":"","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"National Natural Science Foundation of China","keywords":"Anomaly detection; Scalability; Set (abstract data type); Event (particle physics); Software; Generalization; Anomaly (physics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00440023,0.002553789,0.001856842,0.002471236,0.0007419056,0.001609918,0.004715544,0.002387502,0.002114609],"category_scores_gemma":[0.01188311,0.001121405,0.001908209,0.001332897,0.0008596498,0.00467022,0.00432856,0.003805982,0.002018358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001057515,"about_ca_system_score_gemma":0.001721449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006424062,"about_ca_topic_score_gemma":0.01462227,"domain_scores_codex":[0.9972762,0.0005487409,0.0001247011,0.001106908,0.0007002398,0.0002432329],"domain_scores_gemma":[0.9954407,0.002101106,0.0003186242,0.001184306,0.0007309779,0.0002242836],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006653144,0.0009092677,0.02111073,0.0003016847,0.0005607373,0.0004687383,0.0004970961,0.2730666,0.01966284,0.004770019,0.02410918,0.6538779],"study_design_scores_gemma":[0.00001324903,0.00006465471,0.001008961,0.000008504087,0.00002380906,0.00009479457,0.00002771995,0.9883265,0.003190857,0.005191239,0.002027787,0.00002185519],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02513205,0.0003490358,0.948109,0.0002572301,0.00006738272,0.0001707092,0.0005546599,0.02449105,0.000868833],"genre_scores_gemma":[0.41524,0.0002394277,0.5708877,0.001017812,0.0001095595,0.000419248,0.005351625,0.001684553,0.005050125],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006424062,"threshold_uncertainty_score":0.02327091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009875331043543482,"score_gpt":0.2779389944802919,"score_spread":0.2680636634367484,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}