Artificial Intelligence
Frontier machine learning: large language models, reasoning, alignment, interpretability, agents, generative models, efficient inference and AI for science.
LLM Architectures & Pretraining
How frontier language models are built: transformer variants, mixture-of-experts, state-space and hybrid models, long-context attention, tokenization and pretraining recipes.
Scaling Laws & Compute-Optimal Training
Empirical laws relating loss and capability to parameters, data and compute; data-constrained and synthetic-data scaling; emergent abilities and their measurement.
Reasoning Models & Test-Time Compute
Chain-of-thought, reinforcement learning with verifiable rewards, process reward models and test-time scaling behind modern reasoning models.
AI Alignment & Safety
RLHF and its successors, constitutional and preference optimization methods, scalable oversight, dangerous-capability evaluations, reward hacking and deceptive behavior.
Mechanistic Interpretability
Reverse-engineering neural networks: superposition, sparse autoencoders and transcoders, circuits and attribution graphs, probing and activation steering.
LLM Agents & Tool Use
Agents that plan and act: tool and function calling, computer-use and coding agents, multi-agent systems, agent benchmarks and agent protocols.
Generative & Multimodal Models
Diffusion and flow-matching models, vision-language models, video generation and learned world models.
Efficient Training & Inference
Quantization, distillation, sparsity, speculative decoding, KV-cache compression and IO-aware kernels that make frontier models cheaper to train and serve.
AI for Science
Machine learning that accelerates discovery: protein structure, machine-learned force fields, materials discovery, weather and autonomous laboratories.
Robot Learning & Embodied AI
Vision-language-action models, large-scale imitation learning, sim-to-real transfer, dexterous manipulation and humanoid control.