An LLM agent that iteratively self-corrects its outputs

A Python framework for building LLM agents with built-in self-correction loops, enabling more reliable and accurate outputs through iterative refinement.

Multi-objective NAS for efficiency under real deployment constraints

A research project on multi-objective neural architecture search (NAS) that explicitly targets efficiency (parameters/energy) while keeping forecasting accuracy competitive.

Evidence that width should grow faster than depth for transformers

A research project studying architecture-conditioned scaling laws and identifying a critical-depth regime where adding layers can hurt performance.