most citedOmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization

2 citations · 2 across the 4 of their papers we have counts for

collaborators

8 papers

eess.AS2026

Harf-Speech: A Clinically Aligned Framework for Arabic Phoneme-Level Speech Assessment

Asif Azad, MD Sadik Hossain Shanto, Mohammad Sadat Hossain +6

Automated phoneme-level pronunciation assessment is vital for scalable speech therapy and language learning, yet validated tools for Arabic remain scarce. We present Harf-Speech, a…

cs.AI20262 cited

OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization

Keane Ong, Sabri Boughorbel, Luwei Xiao +9

Socially intelligent AI systems must reason across diverse human behavioral tasks and generalize to new social contexts. However, behavioral data is inherently heterogeneous, compr…

cs.HC2026

Digital Harf: A Clinically Integrated Multimodal AI System for Pervasive Arabic Speech and Language Therapy

Asif Azad, Mohammad Sadat Hossain, MD Sadik Hossain Shanto +6

Children with Autism Spectrum Disorder in Arabic-speaking countries face compounded barriers to effective speech and language therapy: a shortage of qualified specialists, limited…

cs.HC2026

Design and Evaluation of a Culturally Adapted Multimodal Virtual Agent for PTSD Screening

Cengiz Ozel, Waleed Nadeem, Samuel Potter +8

Post-traumatic stress disorder (PTSD) is highly prevalent yet chronically underreported among combat-exposed military personnel. This paper presents Molhim, a culturally adapted mu…

cs.CL2026

There Is More to Refusal in Large Language Models than a Single Direction

Faaiz Joad, Majd Hawasly, Sabri Boughorbel +2

Prior work argues that refusal in large language models is mediated by a single activation-space direction, enabling effective steering and ablation. We show that this account is i…

cs.CL2025

Beyond the Leaderboard: Understanding Performance Disparities in Large Language Models via Model Diffing

Sabri Boughorbel, Fahim Dalvi, Nadir Durrani +1

As fine-tuning becomes the dominant paradigm for improving large language models (LLMs), understanding what changes during this process is increasingly important. Traditional bench…