{"id":"W4408100200","doi":"10.1109/mnet.2025.3547385","title":"Generative-AI for XR Content Transmission in the Metaverse: Potential Approaches, Challenges, and a Generation-Driven Transmission Framework","year":2025,"lang":"en","type":"article","venue":"IEEE Network","topic":"Robotics and Automated Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada)","funders":"Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Computer science; Transmission (telecommunications); Generative grammar; Metaverse; Content (measure theory); Multimedia; Human–computer interaction; Artificial intelligence; Telecommunications; Virtual reality","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":[0.0003314644,0.0001924602,0.0002660469,0.00005596506,0.0001451794,0.00007127802,0.0001391231,0.0002067399,0.000002575897],"category_scores_gemma":[0.00000364972,0.0001388423,0.0000863791,0.0001530459,0.00002233625,0.00006470952,0.000005128116,0.0002338767,0.000001016931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002785006,"about_ca_system_score_gemma":0.00001804733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004067876,"about_ca_topic_score_gemma":0.00001180516,"domain_scores_codex":[0.9989269,0.0001142734,0.0003088438,0.0002412752,0.0001331485,0.0002754997],"domain_scores_gemma":[0.9996346,0.00009019707,0.0000264814,0.0001763986,0.00002307933,0.00004927001],"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":[0.0000230681,0.00003332715,0.00000600174,0.0001605781,0.0001009741,0.000004905298,0.001274828,0.9540248,0.002617441,0.004001693,0.007285415,0.03046696],"study_design_scores_gemma":[0.0005900986,0.00003588989,0.0001855494,0.0002585239,0.00006341135,0.000003978147,0.0001446787,0.9602761,0.0006698419,0.001701843,0.03589409,0.0001760251],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006435286,0.03264849,0.9553006,0.001914891,0.002002694,0.00104516,0.000005695068,0.0001145914,0.0005325743],"genre_scores_gemma":[0.9798362,0.004283164,0.0134279,0.0003021349,0.001746324,0.0002307106,0.00003164336,0.00003732578,0.0001046296],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9734009,"threshold_uncertainty_score":0.5661826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07484928324194474,"score_gpt":0.2496755048776328,"score_spread":0.174826221635688,"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."}}