Umakanta Maharana

@misc{maharana2025rightpredictionwrongreasoning,
  title = {Right Prediction, Wrong Reasoning: Uncovering LLM Misalignment in RA Disease Diagnosis},
  author = {Maharana, Umakanta and Verma, Sarthak and Agarwal, Avarna and Mruthyunjaya, Prakashini and Mahapatra, Dwarikanath and Ahmed, Sakir and Mandal, Murari},
  year = {2025},
  eprint = {2504.06581},
  archiveprefix = {arXiv},
  primaryclass = {cs.AI},
  url = {https://arxiv.org/abs/2504.06581},
  note = {Under review. Investigating the implications of LLM misalignment and reasoning faithfulness in medical diagnostics.},
  keywords = {LLM misalignment, RA disease diagnosis, medical diagnostics, AI ethics, machine learning},
  journal = {arXiv preprint arXiv:2504.06581},
}

@inproceedings{sanyal2025orgaccess,
  title = {OrgAccess: A Benchmark for Role-Based Access Control in Organization-Scale LLMs},
  author = {Sanyal, Debdeep and Maharana, Umakanta and Tan, Hong Ming and Sinha, Yash and Karande, Shirish and Kankanhalli, Mohan and Kumar, Dhruv},
  booktitle = {Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year = {2025},
  eprint = {2505.19165},
  archiveprefix = {arXiv},
  primaryclass = {cs.AI},
  url = {https://arxiv.org/abs/2505.19165},
  note = {Accepted at EMNLP 2025. A benchmark evaluating whether models can reliably respect organizational roles, permissions, and access boundaries.},
  keywords = {Large Language Models, Role-Based Access Control, AI Safety, Benchmark, Enterprise AI},
}

@inproceedings{sanyal2025pedagogical,
  title = {Investigating Pedagogical Teacher and Student LLM Agents: Genetic Adaptation Meets Retrieval Augmented Generation Across Learning Style},
  author = {Sanyal, Debdeep and Maiti, Agniva and Maharana, Umakanta and Kumar, Dhruv and Mali, Ankur and Giles, C. Lee and Mandal, Murari},
  booktitle = {Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year = {2025},
  eprint = {2505.19173},
  archiveprefix = {arXiv},
  primaryclass = {cs.AI},
  url = {https://arxiv.org/abs/2505.19173},
  note = {Accepted at EMNLP 2025. An adaptive framework integrating heterogeneous student agents with genetic teacher policy evolution.},
  keywords = {Pedagogical Agents, LLM, Multi-Agent Simulation, Retrieval-Augmented Generation, Genetic Algorithm},
}