Research

I build long-horizon agents that improve through expert iteration in context space, adapting a frozen model across many rounds by updating persistent state from verified reward signals rather than model weights.

Using this approach, my agents have discovered 150+ vulnerabilities in widely used OSS projects including Next.js, pnpm, MetaMask, and LiteLLM; improved the upper bound for an open math problem; and topped the Spider 2.0 dbt benchmark for data science tasks. Earlier, my master’s thesis work at MIT focused on model robustness under distribution shift.

My papers are below, and also on my Google Scholar profile.

Test Time Scaling

AutoFyn interface running a long-horizon security audit

AutoFyn Technical Report: Non-Parametric Expert Iteration for Long-Horizon Agents

Adib Hasan, Daniel Schaffield, Akashnil Dutta, Tarik Adnan Moon

Preprint, 2026

paper / code / blog post

Robustness Against Adverse Distribution Shift

Overview of VITA variational pretraining and crop-yield fine-tuning

VITA: Variational Pretraining of Transformers for Climate-Robust Crop Yield Forecasting

Adib Hasan, Mardavij Roozbehani, Munther Dahleh

AAAI Conference on Artificial Intelligence (AAAI), 2026 — Oral (Top 5%)

paper / code / blog post / slides

Refusal rates of pruned and unpruned language models

Pruning for Protection: Increasing Jailbreak Resistance in Aligned LLMs Without Fine-Tuning

Adib Hasan, Ileana Rugina, Alex Wang

BlackboxNLP Workshop at EMNLP, 2024

paper / dataset

Algorithms and Data Structures

Learned binary heap architecture and performance results

Towards Learned Binary Heaps

Adib Hasan, Angelos Pelecanos

MIT 6.890: Learning-Augmented Algorithms, 2019

paper / blog post

Graph and bit-vector representations used by Graphettes

Graphettes: Constant-time determination of graphlet and orbit identity including (possibly disconnected) graphlets up to size 8

Adib Hasan, Po-Chien Chung, Wayne Hayes

PLoS ONE, 2017