A curated list of papers on alignment, preference optimization, mechanistic interpretability, and reasoning from the two biggest ML conferences this cycle — with personal takes on the ones that matter most.
A deep look at Agent-Native Research Artifacts (Ara) — a proposed replacement for academic PDFs that packages research as machine-executable knowledge bundles. What it gets right, what it gets wrong, and why it matters for AI-assisted research.
SAEs went from niche interpretability tool to dominant research theme in one year. Where they’re being applied, what they reveal, and the fundamental limitations nobody has solved yet.
Books, papers, conferences, and researchers — a personal resource list for anyone going deep into LLMs, RL, and AI safety.