Adam Kamel

University of Waterloo logo

Software Engineering University of Waterloo

I'm a Software Engineering student at UWaterloo working across ML research and applied AI. I've published on interpretability and context compression, and I build reinforcement learning agents, including a Minecraft combat bot that beats 99% of human players. Right now I'm co-leading WorldFold at Wat.AI, teaching a dual-arm robot to fold cloth with world models. Open to new opportunities and collaborations.

Experience

b(x) Theory logo

AI Engineer at b(x) Theory

May 2026 – Aug 2026

Built the document intelligence and financial RAG platform behind a restructuring analytics product

Wat.AI logo

Technical Project Manager at Wat.AIDesign Team

May 2026 – Present

Leading WorldFold, a project exploring world models and reinforcement learning for robotic cloth manipulation

WatStreet logo

Machine Learning Engineer at WatStreetDesign Team

Dec 2025 – Aug 2026

Trained statistical and deep learning classifiers to identify market regimes in noisy financial time series

Workbench logo

Software Engineering Intern at Workbench

May 2025 – Sep 2025

Developed AI agent integrations and improved natural language processing systems

Papers

Emergent World Beliefs: Exploring Transformers in Stochastic Games

Adam Kamel et al.

NeurIPS 2025 MechInterp Workshop · EMNLP 2025 BlackboxNLP Workshop

Explores whether transformers build internal world models of games they can't fully observe. Generated 3M+ simulated poker games to train a GPT-2-style model on hand data, then probed its internal activations, recovering hand strength with 98% accuracy and showing the model encodes its own win probability (r = 0.59), information it was never shown during training.

RSCE: Training-Free Residual Stream Encoding for Persistent Context Amortization

Adam Kamel, Eric Xu

KnowFM Workshop @ ACL 2026

A training-free method that compresses long documents into reusable vector representations. Benchmarked against leading compression baselines on QA and code tasks across 7B–70B models, RSCE retains 60–81% of answer quality while using 99% fewer tokens, and the paper identifies why quality drops when it does.

Featured Projects

Minecraft RL Combat Agent

Self-play RL agent that beats 99% of human players in Minecraft sword duels

  • C++
  • PyTorch
  • Java
  • Reinforcement Learning
Pufferfish

Pufferfish

Chess engine that placed 3rd overall out of 75 teams at ChessHacks

  • Python
  • Pytorch
  • Huggingface