The Autism Diagnostic Observation Schedule (ADOS) is a semi-structured assessment used for diagnosing Autism Spectrum Disorder (ASD). This paper presents revised algorithms for the ADOS Modules 1–3 to improve diagnostic validity. The original ADOS had limitations in distinguishing between ASD and non-spectrum disorders, particularly for milder cases. The revised algorithm includes two new domains: Social Affect and Restricted, Repetitive Behaviors (RRB), combined into a single score with thresholds, improving predictive value. The ADOS was originally effective in classifying children with ASD, but had lower specificity and sensitivity for milder cases. The revised algorithm aims to address these issues by creating more homogeneous diagnostic groups based on age, language level, and developmental stage. The new algorithm uses items that best differentiate between diagnoses and is based on item-response analysis. The revised algorithm also includes RRB items, which may contribute to diagnostic stability. The goal of this research is to improve the sensitivity and specificity of the ADOS algorithms for Modules 1–3 and to test the feasibility of using similar items across all three modules for easier case comparisons. The revised algorithm is part of a larger project aiming to generate a calibrated severity metric for autism, independent of language levels. The new algorithm includes fewer items, chosen for their diagnostic value, and is designed to remain consistent across developmental stages while maintaining or improving classification performance. The revised algorithm addresses concerns about floor and ceiling effects in the original ADOS totals. The study concludes that the revised algorithm improves the ADOS's ability to quantify social-communicative deficits and make more accurate diagnostic distinctions between ASD and other disorders.The Autism Diagnostic Observation Schedule (ADOS) is a semi-structured assessment used for diagnosing Autism Spectrum Disorder (ASD). This paper presents revised algorithms for the ADOS Modules 1–3 to improve diagnostic validity. The original ADOS had limitations in distinguishing between ASD and non-spectrum disorders, particularly for milder cases. The revised algorithm includes two new domains: Social Affect and Restricted, Repetitive Behaviors (RRB), combined into a single score with thresholds, improving predictive value. The ADOS was originally effective in classifying children with ASD, but had lower specificity and sensitivity for milder cases. The revised algorithm aims to address these issues by creating more homogeneous diagnostic groups based on age, language level, and developmental stage. The new algorithm uses items that best differentiate between diagnoses and is based on item-response analysis. The revised algorithm also includes RRB items, which may contribute to diagnostic stability. The goal of this research is to improve the sensitivity and specificity of the ADOS algorithms for Modules 1–3 and to test the feasibility of using similar items across all three modules for easier case comparisons. The revised algorithm is part of a larger project aiming to generate a calibrated severity metric for autism, independent of language levels. The new algorithm includes fewer items, chosen for their diagnostic value, and is designed to remain consistent across developmental stages while maintaining or improving classification performance. The revised algorithm addresses concerns about floor and ceiling effects in the original ADOS totals. The study concludes that the revised algorithm improves the ADOS's ability to quantify social-communicative deficits and make more accurate diagnostic distinctions between ASD and other disorders.