A language-model agent that simultaneously modifies task-specific scaffolding and model weights. Achieves 25.1% over prior SOTA on LawBench and 12.4% faster GPU kernels.
ICML 2026
4 papers accepted at ICML 2026, held in Seoul, South Korea - including SIA, our self-improving AI system.
At the ICML 2026 venue, Seoul, South Korea
View all photosCurrent Research Focus
I study how to make small language models (SLMs) capable enough for useful work on the hardware people already have. The goal is to make more AI tasks practical on a phone or laptop, without requiring an expensive GPU.
- Efficient inference on everyday hardware: Quantization, efficient decoding, and lower memory use to make capable models practical on phones and laptops.
- Making small models more capable: Distillation, better training data, and lightweight adaptation to help a small model do more with limited compute.
Why small models are a research problem
A smaller model uses less memory and compute, but has less capacity to handle difficult tasks. The research challenge is to improve what it can do within those limits, through better data, training, and inference, so useful work can run on everyday hardware.
Research
View all papers →Autonomous self-refinement via evolving agent structure and test-time reinforcement learning. +16pp on LawBench, -19% GPU kernel runtime.
Addresses the cost/reliability tradeoff in proxy evaluations. MLEvolve achieved SOTA MAE of 0.1354 on MLE-bench within 12 hours.
A two-agent system pairing a Scientist with an advisor that can only ask questions. Improved Kaggle test scores on 4/5 MLE-bench tasks with a mean increase of ~56%.
A benchmark for evaluating whether an AI agent can modify another agent to improve it, covering meta-improvement and self-improvement scenarios.
Experience
Hexo LabsSenior Research Scientist
Leading AI research and high-impact programs end-to-end, from architecture and novel research to production delivery.
- 4 papers @ ICML 2026
- Co-authored & co-built SIA
- Led tech across multiple AI projects
- Owned end-to-end delivery for client programs
AI CallerAI / Backend Engineer
Built a real-time AI calling system before audio-to-audio models existed. Engineered low-latency voice pipelines from scratch and took it from zero to revenue.
- $2,000+ MRR within 2 months
- Pre audio-to-audio era
- Ultra-low latency voice AI
- Sole engineer on critical infrastructure
Who I've Worked With
Organizations I've worked with across AI research, engineering, and product development.
DPIIT
IP India
Bito
Soliton
Atomicwork
Dashtoon