{"id":"W4416220745","doi":"10.1162/neco.a.39","title":"Possible Principles for Aligned Structure Learning Agents","year":2025,"lang":"en","type":"article","venue":"Neural Computation","topic":"Child and Animal Learning Development","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Barrie Urology Group; Université de Montréal; McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Sketch; Scalability; Representation (politics); Path (computing); Core (optical fiber); Range (aeronautics); Artificial neural network; Feature learning","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":[0.00007085355,0.00008752559,0.00009967435,0.00007830586,0.0001825012,0.00004290238,0.00006960116,0.00005291779,0.00009824472],"category_scores_gemma":[0.00004668455,0.00008209493,0.00004259944,0.0001301521,0.0000123826,0.00003474257,0.00002655428,0.00008640117,0.00001847661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002569447,"about_ca_system_score_gemma":0.00001949696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007971335,"about_ca_topic_score_gemma":0.000003776387,"domain_scores_codex":[0.9993293,0.00005836433,0.0001599694,0.0002296207,0.00007059769,0.000152119],"domain_scores_gemma":[0.9997164,0.00008836191,0.00006853427,0.0000547749,0.00004907398,0.00002282579],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0008924659,0.0002521265,0.2073437,0.0002706937,0.0004274214,0.00002538345,0.01236693,0.1499365,0.01434717,0.09253301,0.03065337,0.4909513],"study_design_scores_gemma":[0.001030738,0.0001587944,0.9398412,0.00003444613,0.0000203229,0.000004130764,0.0001987263,0.02240397,0.0004202721,0.001698564,0.03403893,0.0001498834],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9842646,0.00005983932,0.007654198,0.0006567931,0.0008180095,0.0002753667,0.000003796304,0.000129709,0.006137726],"genre_scores_gemma":[0.9920242,5.293251e-7,0.001546422,0.0004351437,0.00007215806,0.00001131832,0.00009155989,0.000009397047,0.005809244],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7324976,"threshold_uncertainty_score":0.3347734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03476532634734793,"score_gpt":0.3456662749309087,"score_spread":0.3109009485835608,"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."}}