{"id":"W4408151444","doi":"10.1145/3658617.3697721","title":"MTLSO: A Multi-Task Learning Approach for Logic Synthesis Optimization","year":2025,"lang":"en","type":"article","venue":"","topic":"Formal Methods in Verification","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Computer science; Task (project management); Artificial intelligence; Multi-task learning; Machine learning; Systems engineering; Engineering","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":[0.001474316,0.002016522,0.001434608,0.001477505,0.0005392188,0.0009727894,0.002848045,0.001717939,0.004882106],"category_scores_gemma":[0.003332653,0.0007380008,0.001992363,0.001195061,0.00081833,0.001565952,0.00148109,0.002202829,0.001185208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001774251,"about_ca_system_score_gemma":0.001808404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00608392,"about_ca_topic_score_gemma":0.01343181,"domain_scores_codex":[0.9991925,0.000246375,0.00004528585,0.0002649641,0.0001532563,0.0000975417],"domain_scores_gemma":[0.9981409,0.001156092,0.0001492087,0.0002050307,0.0002609604,0.00008775291],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002504978,0.0002993829,0.001660389,0.0002880998,0.0001374565,0.00009308031,0.00005374687,0.6806851,0.003501115,0.004196833,0.01052406,0.2983103],"study_design_scores_gemma":[0.00001635319,0.00004032298,0.00008556696,0.000005883996,0.000009835579,0.00000885688,0.00000648667,0.9940265,0.0005467696,0.004612466,0.0006370331,0.000003801016],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03478007,0.001551116,0.9491686,0.0008402557,0.0001614772,0.0002409419,0.001022565,0.008778667,0.003456232],"genre_scores_gemma":[0.5466923,0.0006367299,0.4344715,0.001464848,0.0002745681,0.0007661488,0.005310241,0.001011527,0.009372218],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00608392,"threshold_uncertainty_score":0.01633227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0501171141136901,"score_gpt":0.318499557619319,"score_spread":0.2683824435056289,"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."}}