7 papers
Learning When Not to Decide: A Framework for Overcoming Factual Presumptuousness in AI Adjudication
Mohamed Afane, Emily Robitschek, Derek Ouyang +1
A well-known limitation of AI systems is presumptuousness: the tendency of AI systems to provide confident answers when information may be lacking. This challenge is particularly a…
Quantum-Audit: Evaluating the Reasoning Limits of LLMs on Quantum Computing
Mohamed Afane, Kayla Laufer, Wenqi Wei +4
Language models have become practical tools for quantum computing education and research, from summarizing technical papers to explaining theoretical concepts and answering questio…
Benchmarking Legal RAG: The Promise and Limits of AI Statutory Surveys
Mohamed Afane, Emaan Hariri, Derek Ouyang +1
Retrieval-augmented generation (RAG) offers significant potential for legal AI, yet systematic benchmarks are sparse. Prior work introduced LaborBench to benchmark RAG models based…
Differentiable Architecture Search for Adversarially Robust Quantum Computer Vision
Mohamed Afane, Quanjiang Long, Haoting Shen +4
Current quantum neural networks suffer from extreme sensitivity to both adversarial perturbations and hardware noise, creating a significant barrier to real-world deployment. Exist…
SCOUT: A Defense Against Data Poisoning Attacks in Fine-Tuned Language Models
Mohamed Afane, Abhishek Satyam, Ke Chen +3
Backdoor attacks create significant security threats to language models by embedding hidden triggers that manipulate model behavior during inference, presenting critical risks for…
Can LLMs Help Allocate Public Health Resources? A Case Study on Childhood Lead Testing
Mohamed Afane, Ying Wang, Juntao Chen
Public health agencies face critical challenges in identifying high-risk neighborhoods for childhood lead exposure with limited resources for outreach and intervention programs. To…