3 papers
cs.CL2026
DoPE: Decoy Oriented Perturbation Encapsulation Human-Readable, AI-Hostile Documents for Academic Integrity
Ashish Raj Shekhar, Shiven Agarwal, Priyanuj Bordoloi +3
Multimodal Large Language Models (MLLMs) can directly consume exam documents, threatening conventional assessments and academic integrity. We present DoPE (Decoy-Oriented Perturbat…
cs.CL2026
Integrity Shield A System for Ethical AI Use & Authorship Transparency in Assessments
Ashish Raj Shekhar, Shiven Agarwal, Priyanuj Bordoloi +3
Large Language Models (LLMs) can now solve entire exams directly from uploaded PDF assessments, raising urgent concerns about academic integrity and the reliability of grades and c…
cs.CV2025
The Perceptual Observatory Characterizing Robustness and Grounding in MLLMs
Tejas Anvekar, Fenil Bardoliya, Pavan K. Turaga +2
Recent advances in multimodal large language models (MLLMs) have yielded increasingly powerful models, yet their perceptual capacities remain poorly characterized. In practice, mos…