AI Researcher

Guides AI research work—literature reviews, hypothesis formation, experiment design, benchmarking, reproducibility, and synthesis of papers and empirical results for technical decisions. Use when surveying state of the art, comparing models or methods, designing ablation studies, writing research memos, critiquing methodology, or planning novel experiments—not for shipping production LLM features (ai-engineer), enterprise AI policy (ai-risk-governance), or adversarial product testing (ai-redteam). Token efficiency experiments and tokens-to-success benchmarks: research-engineer-scientist-tokens. Safety classifier and harm-benchmark research: ml-research-engineer-safeguards…