Papers by Subhadip Baidya
CURE-Med: Curriculum-Informed Reinforcement Learning for Multilingual Medical Reasoning (2026.acl-long)
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| Challenge: | Recent advances in large language models (LLMs) have produced strong performance in mathematical reasoning and code generation, but medical reasoning remains challenging because it requires domain knowledge. |
| Approach: | They propose a multilingual medical reasoning dataset with open-ended reasoning queries with a single verifiable answer that spans thirteen languages. |
| Outcome: | The proposed framework outperforms baselines and scales effectively across thirteen languages. |
When Background Matters: Breaking Medical Vision Language Models by Transferable Attack (2026.acl-long)
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| Challenge: | Existing medical attacks focus on secondary objectives such as model stealing or adversarial fine-tuning, while transferable attacks from natural images introduce visible distortions that clinicians can easily detect. Existing transferable adversarials are less effective in the medical domain. |
| Approach: | They propose a highly transferable black-box multimodal attack that induces incorrect yet clinically plausible diagnoses while keeping perturbations imperceptible. |
| Outcome: | The proposed method induces incorrect yet clinically plausible diagnoses while keeping perturbations imperceptible. |
RELIC: Enhancing Reward Model Generalization for Low-Resource Indic Languages with Few-Shot Examples (2025.findings-emnlp)
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Soumya Suvra Ghosal, Vaibhav Singh, Akash Ghosh, Soumyabrata Pal, Subhadip Baidya, Sriparna Saha, Dinesh Manocha
| Challenge: | a new reward model for low-resource Indic languages is proposed . a preference-based training approach is prohibitively expensive, authors say . |
| Approach: | a new in-context learning framework is proposed to train a retriever to select in-constext examples from low-resource Indic languages. |
| Outcome: | a new in-context learning framework for reward modeling in low-resource Indic languages is developed . the proposed framework outperforms existing examples on three preference datasets . |