{"id":"W4411157337","doi":"10.46586/tches.v2025.i3.493-515","title":"Accelerating EdDSA Signature Verification with Faster Scalar Size Halving","year":2025,"lang":"en","type":"article","venue":"IACR Transactions on Cryptographic Hardware and Embedded Systems","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Signature (topology); Scalar (mathematics); Computer science; Mathematics; Physics; Algorithm; Geometry","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":[0.0003686004,0.0002894565,0.0003018358,0.000429095,0.0005747592,0.0007796426,0.000505193,0.0002293588,0.00000927333],"category_scores_gemma":[0.00002649227,0.0002310944,0.00009492092,0.001330915,0.00007652779,0.0007363606,0.00001391943,0.0005389654,0.000003320345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003844942,"about_ca_system_score_gemma":0.00007347659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005009694,"about_ca_topic_score_gemma":0.00002125065,"domain_scores_codex":[0.998193,0.0001608974,0.0003434869,0.0006425097,0.0003343075,0.0003257466],"domain_scores_gemma":[0.9987012,0.0002418765,0.0001230303,0.0006153861,0.0002228343,0.0000956733],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005039974,0.001088905,0.00185464,0.003916429,0.00155052,0.0002089694,0.01432578,0.004938542,0.08344149,0.292783,0.003018846,0.5923689],"study_design_scores_gemma":[0.01494534,0.004234025,0.006390727,0.03468831,0.001666771,0.00145642,0.02090773,0.5606565,0.2301124,0.06607603,0.04607083,0.01279499],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00356594,0.002377187,0.9910565,0.0005671858,0.0003949022,0.000460171,0.000008500894,0.0008375124,0.0007320996],"genre_scores_gemma":[0.8879113,0.00005711526,0.1113651,0.0002860745,0.00003880206,0.000146073,0.000003092411,0.00001816176,0.0001743272],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8843453,"threshold_uncertainty_score":0.9423754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009931151980072331,"score_gpt":0.2435255318692568,"score_spread":0.2335943798891845,"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."}}