{"id":"W4417130918","doi":"10.1109/tccn.2025.3641513","title":"RadioDiff-Flux: Efficient Radio Map Construction via Generative Denoise Diffusion Model Trajectory Midpoint Reuse","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Key Research and Development Program of China","keywords":"Inference; Scalability; Transmitter; Midpoint; Representation (politics); Reuse; Latency (audio); Ground truth","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","sts"],"consensus_categories":[],"category_scores_codex":[0.0006720152,0.0007195055,0.0006616087,0.0008118508,0.002758801,0.0002507718,0.000633417,0.0004054669,0.00007433412],"category_scores_gemma":[0.00001703399,0.0008353451,0.0003288868,0.0007975132,0.0006461821,0.0002144031,0.00005187041,0.001496485,0.00002586835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004528007,"about_ca_system_score_gemma":0.0002030177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000418931,"about_ca_topic_score_gemma":0.0001146697,"domain_scores_codex":[0.9964045,0.0007256035,0.001114217,0.0008018101,0.0003124235,0.0006414617],"domain_scores_gemma":[0.9964662,0.0009854407,0.0002416336,0.001594309,0.000447632,0.0002648142],"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":[0.0003283849,0.0006500109,0.00001053178,0.0002292154,0.0006733996,0.000003083651,0.00393897,0.5412841,0.025783,0.0001042393,0.0001196251,0.4268755],"study_design_scores_gemma":[0.002010759,0.0001160853,0.00001507044,0.001981298,0.0007681425,0.00002218067,0.0009676141,0.9735655,0.01895397,0.000504352,0.0003871126,0.0007079007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03476509,0.01652993,0.9430937,0.0005575782,0.001986395,0.001256351,0.000141733,0.0002514391,0.001417832],"genre_scores_gemma":[0.9460589,0.02940707,0.02315153,0.0003275938,0.0001412401,0.0003084483,0.00007344776,0.00009115147,0.0004405942],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9199421,"threshold_uncertainty_score":0.9994097,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03111315105316424,"score_gpt":0.2559903759900948,"score_spread":0.2248772249369306,"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."}}