Varun Gandhi
I'm an MS Computer Science student at UMass Amherst, interested in AI research and engineering, with a focus on LLM post-training. I have experience across both research and industry. Outside of research, I write notes on things I'm learning, inspired by the idea of learning in public.
- 2026
This summer, I'm interning as a Software Engineer at Motional. - 2026
I was an AI Research Extern at Adobe (Jan–May 2026) through the UMass CS 698DS Industry Mentorship Practicum in AI, working on LLM post-training, agents, and retrieval-augmented generation for document Q&A — mentored by Zichao (Jack) Wang at Adobe and Jaewook (Jake) Lee at UMass. Paper under review. - 2025
I'm pursuing my MS in Computer Science at UMass Amherst. In Fall 2025, I was the instructor for four sections of undergraduate CS First Year Seminars. - 2025
I interned as a Machine Learning Engineer with the Vision-Language Models team at Sarvam AI, where I contributed to their efforts in building India's sovereign language model. - 2024 Awarded the Bay State Fellowship by Manning CICS — a merit-based award granted to around ten students each year, with a teaching assistantship and full tuition waiver.
News
- Jun 2026 Our paper "Hierarchical Experimentalist Agents" (HExA) was accepted to the Third Reinforcement Learning Beyond Rewards Workshop at the Reinforcement Learning Conference (RLC).
- Summer 2026 Interning at Motional as a Software Engineer Intern.
- Sep 2025 Instructor for four sections of undergraduate CS First Year Seminars at UMass this fall.
- Summer 2025 Interned as a Machine Learning Engineer with the Vision-Language Models team at Sarvam AI.
- Jan 2025 Began my MS in Computer Science at UMass Amherst.
- Oct 2024 Awarded the Bay State Fellowship by Manning CICS.
Research
Hierarchical Experimentalist Agents
arXiv preprint, 2026
HExA is a training-free framework that lets LLM agents improve through active experimentation — learning reusable skills and integrating evidence to solve novel tasks without external supervision — evaluated on Interphyre, a benchmark built on the PHYRE 2D physics environment.
Chain-of-Code Collapse: Reasoning Failures in LLMs via Adversarial Prompting in Code Generation
arXiv preprint, 2025
We show that LLMs are fragile under semantically faithful but adversarially structured prompt variations in code generation — performance swings on surface-level formatting changes rather than genuine reasoning ability.