{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006543183,0.0000859691,0.0001082021,0.0001245472,0.0001636792,0.0001119198,0.0005115871,0.00006671506,0.0000064107],"category_scores_gemma":[0.0008999301,0.00007603493,0.00004933359,0.0004187253,0.00002240238,0.0003269045,0.0001104111,0.00007589732,0.000005312283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004319529,"about_ca_system_score_gemma":0.00003410419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005850282,"about_ca_topic_score_gemma":2.072504e-7,"domain_scores_codex":[0.9991416,0.0001189943,0.0001735762,0.0003125829,0.00008753646,0.0001657137],"domain_scores_gemma":[0.9993078,0.000189563,0.00006647075,0.0003170422,0.00009398531,0.00002518534],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007796787,0.00008880693,0.00009922642,0.00005838401,0.00001538378,1.153666e-7,0.0001561859,0.6031849,0.0003660379,0.3137691,0.0001887621,0.08206528],"study_design_scores_gemma":[0.0001284954,0.00001875872,0.0001029831,0.000007482767,0.000006735296,8.153309e-7,0.0000335367,0.9947894,0.003490568,0.0003665418,0.0009676495,0.00008701454],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00002500438,0.00003603361,0.9889861,0.0001647302,0.0001408157,0.0003175207,4.116291e-7,0.0002928137,0.01003662],"genre_scores_gemma":[0.01465385,0.00000996569,0.9830949,0.0001811824,0.00001205868,0.0002239429,0.000003471465,0.000005035467,0.001815538],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3916045,"threshold_uncertainty_score":0.3100614,"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."}}