{"id":"W4416478222","doi":"10.1145/3680207.3765592","title":"Demo: Split-and-Pipeline: Collaborative Large Model Inference on Edge Devices","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Testbed; Inference; Enhanced Data Rates for GSM Evolution; Scheme (mathematics); Edge device; Throughput; Data transmission; Edge computing","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000299299,0.0005115562,0.000477458,0.0002270271,0.0007890016,0.0004062623,0.001330948,0.0002072627,0.00003908991],"category_scores_gemma":[0.0001726161,0.0004782288,0.000073925,0.003512451,0.0002149781,0.0008430801,0.001251449,0.0005135977,0.0001769491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001087774,"about_ca_system_score_gemma":0.0006404763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006645985,"about_ca_topic_score_gemma":0.0001569814,"domain_scores_codex":[0.9966161,0.0001315591,0.0006920975,0.001426016,0.0003596199,0.0007746218],"domain_scores_gemma":[0.9967138,0.0008962244,0.0002476761,0.001270429,0.0006152168,0.0002566786],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002626832,0.0001996179,0.0003193239,0.00003799743,0.0000231494,0.000002322654,0.0003138828,0.02552746,0.00008714153,0.8936296,0.005266679,0.07456663],"study_design_scores_gemma":[0.0005472969,0.00007524442,0.0005251527,0.0001481214,0.00003125757,9.905457e-7,0.00008043655,0.9295663,0.001009757,0.04821878,0.01937934,0.0004173176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002633516,0.001502791,0.941187,0.009111384,0.000301235,0.0009649799,0.00005463054,0.0002265901,0.04401789],"genre_scores_gemma":[0.8744414,0.001119953,0.100476,0.00853738,0.00009006047,0.0001727794,0.00000748522,0.00001905352,0.01513586],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9040388,"threshold_uncertainty_score":0.9997669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02126014214548735,"score_gpt":0.3278883683632451,"score_spread":0.3066282262177578,"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."}}