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"Imagination is more important than knowledge" - Albert Einstein

1. Improving reasoning ability of LLMs and reducing reward hacking in reasoning LLMs.
2. Reasoning in diffusion LLMs.
3. Weak-to-strong generalization and self-improvement.
4. LLM/VML alignment, reasoning and reducing hallucinations. 
5. Reinforcement Learning and Multi-Agent Systems, Uncertainty Estimation.
   
   

UPDATES:

• VisAlign and EnsemW2S accepted at ACL, 2026.

• March 2nd: OC-PRM accepted as Poster at ICLR's AFAA Workshop'26. (Received a recommendation of Oral from AC!)

• March 2nd: VisAlign accepted as Poster at ICLR's MM Intelligence Workshop'26. 

• Dec 17: Gave me Prelim Exam. My talk titled : Towards Reliable Reasoning and Alignment in Large Models is available here. I am officially a PhD Candidate. Please reach out for full-time roles and summer internships!

• 1 paper accepted at EMNLP'25 on unceratiny aware Answer Selection for multiple LLMs.

• 1 paper accepted at Neurips'25.

• Competed Fall'24-Spring'25 internship at Capital One working on reward hacking in reasoning LLMs. Stay Tuned for paper.

• Completed Summer'24 internship at Dolby where I worked on reducing hallucinations in Video LLMs!

• EnsemW2S accepted at SafeGenAI workshop at Neurips'24. Paper is now on arxiv! 

• Robustness to Multi-Modal Environment Uncertainty in MARL accepted at Neurips'23 Multi-Agent Security workshop.

• Amazon Internship paper accepted at Interspeech'23.

RESEARCH PUBLICATIONS

REASONING LLMs | Alignment | SuperAlignment in LLMs

1. Cut the Overcredit: Precision First Process Rewards for Reasoning LLMs.
   Aakriti Agrawal, Souradip Chakraborty, Armin Saghafian, Nihal Sharma, Rizal Fathony, Nam H Nguyen, C. Bayan Bruss, Amrit Singh Bedi, Furong Huang.
   In-review at ICML, 2026

2. VGS-GRPO: Scalable and Efficient Process Supervision in GRPO.
   Aakriti Agrawal, Minghui Liu, Furong Huang.
   In-review @International Conference on Machine Learning (ICML), 2026.

3. Scheduling Thoughts: Learning the Order of Thought in Diffusion Language Models. 
   J. Xu*, M. Liu*, Aakriti Agrawal, Y. Chen, Furong Huang.
   In-review @International Conference on Machine Learning (ICML), 2026.

4. Uncertainty-Aware Answer Selection for Improved Reasoning in Multi-LLM Systems.
   Aakriti Agrawal, Rohith Aralikatti, Anirudh Satheesh, Amrit Singh Bedi, Furong Huang.
   Published 30th Empirical Methods in Natural Language Processing (EMNLP), China , 2025

5. EnsemW2S: Can an Ensemble of SoTA LLMs be Leveraged to Obtain a Stronger LLM?
   Aakriti Agrawal, Mucong Ding, Zora Che, Chenghao Deng,  Anirudh Satheesh, John Langford, Furong Huang.
   Published The 64th Annual Meeting of the Association for Computational Linguistics (ACL), San Diego, 2026 | SafeGenAI @Neurips, 2024.

6. Easy2Hard-Bench: Standardized Difficulty Labels for Profiling LLM Performance and Generalization.
   Mucong Ding*, Chenghao Deng*, Jocelyn Choo, Zichu Wu, Aakriti Agrawal, Avi Schwarzschild, Tianyi Zhou, Tom Goldstein, John Langford, Anima Anandkumar, Furong Huang.
   Published at Neurips Dataset Track, 2024

Vision-LLMs

1. Towards Mitigating Hallucinations in Large Vision-Language Models by Refining Textual Embeddings.
   Aakriti Agrawal, Gouthaman KV, Rohith Aralikatti, Gauri Jagatap, Jiaxin Yuan, Vijay Kamarshi, Andrea Fanelli, Furong Huang.
   Published The 64th Annual Meeting of the Association for Computational Linguistics (ACL), San Diego 2026 | MM Intelligence Workshop @ICLR, 2026.

Robustness

1. WAVES: Benchmarking the Robustness of Image Watermarks.
   Bang An*, Mucong Ding*, Tahseen Rabbani*, Aakriti Agrawal, Yuancheng Xu, Chenghao Deng, Sicheng Zhu, Abdirisak Mohamed, Yuxin Wen, Tom Goldstein, Furong Huang.
   Published ICML 2024 | Paper 

2. PoisonedParrot: Subtle Data Poisoning Attacks to Elicit Copyright-Infringing Content from Large Language Models.
   Michael-Andrei Panaitescu-Liess, Pankayaraj Pathmanathan, Yigitcan Kaya, Zora Che, Bang An, Sicheng Zhu, Aakriti Agrawal, Furong Huang.
   Published at NAACL'25 | SafeGenAI @Neurips'24

3. Robustness to Multi-Modal Environment Uncertainty in MARL using Curriculum Learning.
   Aakriti Agrawal,  Rohith Aralikatti, Yanchao Sun, Furong Huang.
   Published MASEC@NeurIPS 2023  | Paper | Code 

Speech Processing

Learning when to trust which teacher for weakly supervised ASR.

 Aakriti Agrawal, Milind Rao, Anit Kumar Sahu, Gopinath (Nath) Chennupati, Andreas Stolcke.

Published Interspeech 2023 | Paper 

Multi-Agent Reinforcement Learning

1. Revisiting Parameter Sharing in Multi-Agent Deep Reinforcement Learning.
   J K Terry, Nathaniel Grammel, Sanghyun Son, Benjamin J Black, Aakriti Agrawal.
   Paper

2.  RTAW: An Attention Inspired Reinforcement Learning Method for Multi-Robot Task Allocation in Warehouse Environments.
   Aakriti Agrawal,  Amrit Singh Bedi,  Dinesh Manocha.
   ICRA 2023 | Paper | Code 

3. DC-MRTA: Decentralized Multi-Robot Task Allocation and Navigation in Complex Environments.
   Aakriti Agrawal,  Senthil Arul Hariharan,  Amrit Singh Bedi,  Dinesh Manocha.
   IROS 2022 | Paper  

Robotics

1. Accurate Estimation of 3D-Repetitive-Trajectories using Kalman Filter, Machine Learning and Curve-Fitting Method for High-speed Target Interception.
   Aakriti Agrawal, Aashay Bhise, Rohitkumar Arasanipalai, Lima Agnel Tony, Shuvrangshu Jana, Debasish Ghose.
   Book Chapter in : "Artificial Intelligence for Robotics and Autonomous Systems Applications" | Paper 

2. Mid-flight propeller failure detection and control of propeller-deficient quadcopter using reinforcement learning.
   Rohitkumar Arasanipalai*, Aakriti Agrawal*, Debasish Ghose | Paper

3. A Comparative Study of Noise Cancellation Using LMS Adaptive Filter and RNN Filter.
   Aakriti Agrawal, Rohitkumar Arasanipalai, B Sainath.
   ICEPE 2018 conference | Paper