{"id":"W6979343050","doi":"","title":"Lightweight Transformer via Unrolling of Mixed Graph Algorithms for Traffic Forecast","year":2025,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; Natural Sciences and Engineering Research Council of Canada","keywords":"Graph; Construct (python library); Artificial neural network; Transformer; Directed graph; Iterative method; Smoothness","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009500717,0.001494714,0.001049733,0.0006852213,0.0004002941,0.0009238427,0.00191765,0.001408373,0.004688017],"category_scores_gemma":[0.004528109,0.0006693594,0.0008588175,0.0006176353,0.0009777965,0.001938432,0.001465549,0.00249119,0.001004051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001356245,"about_ca_system_score_gemma":0.001824554,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01338324,"about_ca_topic_score_gemma":0.02009115,"domain_scores_codex":[0.9996266,0.0001080802,0.00001837714,0.0001014157,0.00008584157,0.00005974407],"domain_scores_gemma":[0.998841,0.0007147426,0.00007767089,0.0001479716,0.0001480089,0.00007071301],"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":[0.0001278171,0.00005847959,0.0005238277,0.0000528915,0.00003275525,0.0000512169,0.00005854165,0.8921669,0.002353076,0.01113754,0.002578581,0.09085848],"study_design_scores_gemma":[0.000004950998,0.000006052071,0.00001216343,0.000001458618,0.000001623234,0.000003048471,0.000002663809,0.9966298,0.0002205761,0.002981093,0.0001350876,0.000001461902],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01114291,0.000108342,0.9853242,0.0001893922,0.00004200045,0.0000333995,0.0000783444,0.001973093,0.001108354],"genre_scores_gemma":[0.4431007,0.0001811154,0.5497721,0.0004675232,0.00007767903,0.0002138373,0.0006691391,0.0008592674,0.004658623],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01338324,"threshold_uncertainty_score":0.02661067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02785678645910126,"score_gpt":0.1685570877691258,"score_spread":0.1407003013100246,"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."}}