activity
20242026
collaborators

8 papers

cs.CV2026

CarePilot: A Multi-Agent Framework for Long-Horizon Computer Task Automation in Healthcare

Akash Ghosh, Tajamul Ashraf, Rishu Kumar Singh +4

Multimodal agentic pipelines are transforming human-computer interaction by enabling efficient and accessible automation of complex, real-world tasks. However, recent efforts have…

cs.AI2026

CURE-Med: Curriculum-Informed Reinforcement Learning for Multilingual Medical Reasoning

Eric Onyame, Akash Ghosh, Subhadip Baidya +3

While large language models (LLMs) have shown to perform well on monolingual mathematical and commonsense reasoning, they remain unreliable for multilingual medical reasoning appli…

cs.CL2025

CLINIC: Evaluating Multilingual Trustworthiness in Language Models for Healthcare

Akash Ghosh, Srivarshinee Sridhar, Raghav Kaushik Ravi +3

Integrating language models (LMs) in healthcare systems holds great promise for improving medical workflows and decision-making. However, a critical barrier to their real-world ado…

cs.CV2025

Ask Me Again Differently: GRAS for Measuring Bias in Vision Language Models on Gender, Race, Age, and Skin Tone

Shaivi Malik, Hasnat Md Abdullah, Sriparna Saha +1

As Vision Language Models (VLMs) become integral to real-world applications, understanding their demographic biases is critical. We introduce GRAS, a benchmark for uncovering demog…

cs.CL2025

Infogen: Generating Complex Statistical Infographics from Documents

Akash Ghosh, Aparna Garimella, Pritika Ramu +2

Statistical infographics are powerful tools that simplify complex data into visually engaging and easy-to-understand formats. Despite advancements in AI, particularly with LLMs, ex…

cs.CL2025

Relic: Enhancing Reward Model Generalization for Low-Resource Indic Languages with Few-Shot Examples

Soumya Suvra Ghosal, Vaibhav Singh, Akash Ghosh +4

Reward models are essential for aligning large language models (LLMs) with human preferences. However, most open-source multilingual reward models are primarily trained on preferen…