activity
20242026
most citedSurvey of Large Multimodal Model Datasets, Application Categories and Taxonomy

2 citations · 4 across the 14 of their papers we have counts for

collaborators

19 papers

cs.AI2026

Auditing Disability Representation in Vision-Language Models

Srikant Panda, Sourabh Singh Yadav, Palkesh Malviya

Vision-language models (VLMs) are increasingly deployed in socially sensitive applications, yet their behavior with respect to disability remains underexplored. We study disability…

cs.CV2025

World in a Frame: Understanding Culture Mixing as a New Challenge for Vision-Language Models

Eunsu Kim, Junyeong Park, Na Min An +9

In a globalized world, cultural elements from diverse origins frequently appear together within a single visual scene. We refer to these as culture mixing scenarios, yet how Large…

cs.CL2025

Say It Differently: Linguistic Styles as Jailbreak Vectors

Srikant Panda, Avinash Rai

Large Language Models (LLMs) are commonly evaluated for robustness against paraphrased or semantically equivalent jailbreak prompts, yet little attention has been paid to linguisti…

cs.AI2025

FlexDoc: Parameterized Sampling for Diverse Multilingual Synthetic Documents for Training Document Understanding Models

Karan Dua, Hitesh Laxmichand Patel, Puneet Mittal +7

Developing document understanding models at enterprise scale requires large, diverse, and well-annotated datasets spanning a wide range of document types. However, collecting such…

cs.CL2025

Pushing on Multilingual Reasoning Models with Language-Mixed Chain-of-Thought

Guijin Son, Donghun Yang, Hitesh Laxmichand Patel +9

Recent frontier models employ long chain-of-thought reasoning to explore solution spaces in context and achieve stonger performance. While many works study distillation to build sm…

cs.CV2025

PCRI: Measuring Context Robustness in Multimodal Models for Enterprise Applications

Hitesh Laxmichand Patel, Amit Agarwal, Srikant Panda +6

The reliability of Multimodal Large Language Models (MLLMs) in real-world settings is often undermined by sensitivity to irrelevant or distracting visual context, an aspect not cap…