Mayank Agarwal

Researcher, International Business Machines

Joined

September 2018

Names

Mayank Agarwal (Preferred)

Emails

****@ibm.com (Confirmed)

****@gmail.com (Confirmed)

Personal Links

DBLP

ACL Anthology

Career & Education History

Researcher

International Business Machines (ibm.com)

2018 – Present

MS student

University of Massachusetts, Amherst (umass.edu)

2016 – 2018

Advisors, Relations & Conflicts

No relations added

Expertise

No areas of expertise listed

Publications

ToolRM: Outcome Reward Models for Tool-Calling Large Language Models
Mayank Agarwal, Ibrahim Abdelaziz, Kinjal Basu, Merve Unuvar, Luis A. Lastras, Yara Rizk, Pavan Kapanipathi
- ICLR 2026 Conference Withdrawn Submission
- Readers: Everyone
LongFuncEval: Measuring the effectiveness of long context models for function calling
Kiran Kate, Tejaswini Pedapati, Kinjal Basu, Yara Rizk, Vijil Chenthamarakshan, Subhajit Chaudhury, Mayank Agarwal, Ibrahim Abdelaziz
- CoRR 2025
- Readers: Everyone
Granite-Function Calling Model: Introducing Function Calling Abilities via Multi-task Learning of Granular Tasks
Ibrahim Abdelaziz, Kinjal Basu, Mayank Agarwal, Sadhana Kumaravel, Matthew Stallone, Rameswar Panda, Yara Rizk, G. P. Shrivatsa Bhargav, Maxwell Crouse, Chulaka Gunasekara, Shajith Ikbal, Sachindra Joshi, Hima Karanam, Vineet Kumar, Asim Munawar, Sumit Neelam, Dinesh Raghu, Udit Sharma, Adriana Meza Soria, Dheeraj Sreedhar et al. (6 additional authors not shown)
- EMNLP (Industry Track) 2024
- Readers: Everyone
Granite Code Models: A Family of Open Foundation Models for Code Intelligence
Mayank Mishra, Matt Stallone, Gaoyuan Zhang, Yikang Shen, Aditya Prasad, Adriana Meza Soria, Michele Merler, Parameswaran Selvam, Saptha Surendran, Shivdeep Singh, Manish Sethi, Xuan-Hong Dang, Pengyuan Li, Kun-Lung Wu, Syed Zawad, Andrew Coleman, Matthew White, Mark Lewis, Raju Pavuluri, Yan Koyfman et al. (26 additional authors not shown)
- CoRR 2024
- Readers: Everyone
Granite-Function Calling Model: Introducing Function Calling Abilities via Multi-task Learning of Granular Tasks
Ibrahim Abdelaziz, Kinjal Basu, Mayank Agarwal, Sadhana Kumaravel, Matthew Stallone, Rameswar Panda, Yara Rizk, GP Bhargav, Maxwell Crouse, Chulaka Gunasekara, Shajith Ikbal, Sachin Joshi, Hima Karanam, Vineet Kumar, Asim Munawar, Sumit Neelam, Dinesh Raghu, Udit Sharma, Adriana Meza Soria, Dheeraj Sreedhar et al. (6 additional authors not shown)
- CoRR 2024
- Readers: Everyone
Structured Code Representations Enable Data-Efficient Adaptation of Code Language Models
Mayank Agarwal, Yikang Shen, Bailin Wang, Yoon Kim, Jie Chen
- CoRR 2024
- Readers: Everyone
An Investigation of Representation and Allocation Harms in Contrastive Learning
Subha Maity, Mayank Agarwal, Mikhail Yurochkin, Yuekai Sun
- ICLR 2024
- Readers: Everyone
Aligners: Decoupling LLMs and Alignment
Lilian Ngweta, Mayank Agarwal, Subha Maity, Alex Gittens, Yuekai Sun, Mikhail Yurochkin
- EMNLP (Findings) 2024
- Readers: Everyone
Aligners: Decoupling LLMs and Alignment
Lilian Ngweta, Mayank Agarwal, Subha Maity, Alex Gittens, Yuekai Sun, Mikhail Yurochkin
- Tiny Papers @ ICLR 2024 Notable
- Readers: Everyone
Structured Fine-Tuning Enables Data-Efficient Adaptation of Code Language Models
Mayank Agarwal, Yikang Shen, Bailin Wang, Yoon Kim, Jie Chen
- ICLR 2024 Conference Withdrawn Submission
- Readers: Everyone

View all 38 publications

Co-Authors

View all 144 co-authors