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From the 1 of 10 linked papers with an AI index.

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10 papers

cs.IR2026

Can Argus Judge Them All? Comparing VLMs Across Domains

Harsh Joshi, Gautam Siddharth Kashyap, Rafiq Ali +5

The paper introduces ARGUS-EVAL, a framework that assesses vision-language models on both capability and reliability across domains, and uses it to compare several VLMs on retrieva…

cs.CL2026

ChildGuard: A Specialized Dataset for Combatting Child-Targeted Hate Speech

Gautam Siddharth Kashyap, Mohammad Anas Azeez, Rafiq Ali +3

Mental health industry faces growing concerns regarding hate speech directed at children's on social media, as exposure to such content can contribute to adverse psychological outc…

cs.CL2026

Truth, Trust, and Trouble: Medical AI on the Edge

Mohammad Anas Azeez, Rafiq Ali, Ebad Shabbir +4

Large Language Models (LLMs) hold significant promise for transforming digital health by enabling automated medical question answering. However, ensuring these models meet critical…

cs.CL2026

Are Large Language Models Economically Viable for Industry Deployment?

Abdullah Mohammad, Sushant Kumar Ray, Pushkar Arora +5

Generative AI-powered by Large Language Models (LLMs)-is increasingly deployed in industry across healthcare decision support, financial analytics, enterprise retrieval, and conver…

cs.CV2026

See Fair, Speak Truth: Equitable Attention Improves Grounding and Reduces Hallucination in Vision-Language Alignment

Mohammad Anas Azeez, Ankan Deria, Zohaib Hasan Siddiqui +5

Multimodal large language models (MLLMs) frequently hallucinate objects that are absent from the visual input, often because attention during decoding is disproportionately drawn t…

cs.CL2026

Are Aligned Large Language Models Still Misaligned?

Usman Naseem, Gautam Siddharth Kashyap, Rafiq Ali +4

Misalignment in Large Language Models (LLMs) arises when model behavior diverges from human expectations and fails to simultaneously satisfy safety, value, and cultural dimensions,…