A curated, verified map of LLM theory — expressivity, scaling laws, ICL, alignment, interpretability, and open problems
awesome-llm-theory — a curated, theory-focused reading map, current as of 2026
I curated a theory-focused awesome list, intended as a reading map for graduate students and researchers entering the area.
Coverage
- Representational expressivity / circuit-complexity bounds
- Optimization & training dynamics (μP, signal propagation, NTK for transformers)
- Generalization & sample complexity
- Scaling laws (theory side)
- In-context learning theory
- Chain-of-thought expressivity bounds
- Alignment / RLHF theory
- Knowledge & memory theory
- The formal side of interpretability
- A curated Open Problems section
How it differs from existing lists
It's not trying to replace awesome-language-model-analysis, which is a great exhaustive paper database. This list differs in three ways:
- Tighter — curation is the value, not link count.
- Annotated — every entry has a one-sentence "why it matters" note.
- Verified — every entry is web-verified for authors / year / venue, with a verification URL stored in the YAML source. Weekly CI link-check.
It also includes six-paper reading paths and a curated open-problems section.
Link: https://github.com/bettyguo/awesome-llm-theory
PRs and "wanted entry" issues welcome. The bar for an entry is one verification URL.
u/FunCulture9983 — 9 days ago