Essam Sleiman
I run Canvas Labs (YC), where we work on self-improving AI.
Previously, I did research on continual learning, language-model
interpretability, and vision-language models at Amazon Science and
as a grad student at Harvard, with papers at ECCV and CVPR.
In my free time, I enjoy basketball, coffee, and hiking around SF.
Selected Work
Canvas Labs
AutoHarnessBench
A long-horizon benchmark evaluating harness-driven agent
self-improvement.
Benchmark, 2026
site
Canvas Labs
Privileged Self-Distillation
A post-training method distilling privileged corrections to
failed rollouts.
Research, Harvard
Goldfish: Vision-Language Understanding of Arbitrarily Long
Videos
A SOTA vision-language model for question answering on
hour-long videos.
Research, UC Davis
SlowFormer: Adversarial Robustness for Efficient Vision
Transformers
Adversarial robustness for vision transformers.
Research Engineer at Amazon Science
Continual Self-Supervised Learning with Knowledge
Distillation
A Twitch video embedding model that continually learns from
video streams.
Preprint, 2023
arxiv
Research, Harvard & MIT
Do Function Vectors Factor Task and Distribution?
An interpretability study of how LLMs represent tasks during
in-context learning.