By chrisliu298
Minimal autonomous ML research: scout ideas, run experiments, write papers.
Experiment loop: edit, commit, run, measure, keep or revert. Faithful to Karpathy's autoresearch. Use for 'nanoresearch:loop', 'experiment loop', 'autoresearch'.
Full autonomous ML research pipeline: scout ideas, run experiments, write a paper, survive peer review. Use when user says 'nanoresearch', 'autonomous research', or wants end-to-end ML research. One command, wake up to a paper.
Review phase: simulated peer review with 4 reviewers, rebuttal, area chair decision. Use for 'nanoresearch:review'.
Scout phase: survey literature, generate ideas, verify novelty, produce experiment spec. Use for 'nanoresearch:scout', 'find ideas', 'research direction'.
Write phase: turn experiment results into a paper or research memo. Use for 'nanoresearch:write', 'write paper', 'draft paper'.
Area chair agent for nanoresearch peer review. Reads all reviewer reports, author rebuttal, and the paper. Writes a meta-review and makes the accept/reject decision following NeurIPS/ICLR/ICML guidelines.
Parameterized ML venue reviewer agent. Spawned with a persona (methodologist, empiricist, novelty-critic, or skeptic) to review a paper independently. Read-only: critiques but never edits the paper.
Parameterized writing critic for nanoresearch write phase. Reviews sections or full papers under a specified lens. Read-only: critiques but never edits.
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Autonomous ML research in one command. Scout ideas, run experiments, write a paper, survive peer review — overnight.
/nanoresearch "efficient attention mechanisms for long-context LMs"
Wake up to an accepted paper or an honest rejection analysis.
nanoresearch is the irreducible core of ARIS, rebuilt from first principles following Karpathy's autoresearch and the nanorepl philosophy: strip everything that isn't the core algorithm, then layer complexity back one concept at a time.
The algorithm of research is propose → evaluate → keep/discard, applied at three levels:
| Level | Phase | What it does |
|---|---|---|
| Ideas | Scout | Survey literature, generate ideas, verify novelty, produce experiment spec |
| Code | Loop | Edit → commit → run → measure → keep or revert (autoresearch) |
| Writing | Write | Draft section → review → fix (×6 sections), then revise → panel review → fix (×2 passes) |
| Papers | Review | 4 reviewers score → rebuttal → area chair decides → revise if rejected |
/nanoresearch "topic"
┌────────┐ ┌─────────────────────┐ ┌──────────────────────────┐ ┌──────────────┐ ┌──────────┐
│ SCOUT │────▶│ LOOP │────▶│ WRITE │────▶│ REVIEW │────▶│ DONE │
│ │ │ │ │ │ │ │ │ │
│ survey │ │ ┌────────────────┐ │ │ ┌─────────────────────┐ │ │ 4 reviewers │ │ paper.pdf│
│ ideate │ │ │ edit → commit │ │ │ │ draft → review → fix│ │ │ + rebuttal │ │ + reviews│
│ verify │ │ │ → run → measure│ │ │ └──┬────────────┬────┘ │ │ + area chair │ │ │
│ spec │ │ └──┬──────────┬──┘ │ │ next section all done │ │ │ │ │
│ │ │ revert keep │ │ └──────┬──────┘ │ └──────┬───────┘ └──────────┘
│ │ │ └─────┬────┘ │ │ ┌─────────────────────┐ │ │
│ │ │ budget exceeded? │ │ │ 4+4 panel → fix │ │ rejected?
│ │ │ yes ──▶ exit │ │ └──┬────────────┬─────┘ │ │
└────────┘ └─────────────────────┘ │ next pass 2 passes │ │
▲ │ └──────┬─────┘ │ │
│ │ (or memo if weak) │ │
│ └──────────────────────────┘ │
└───────────── REVISE: update spec & resubmit ◀──────────── yes
# 1. Clone the repo
git clone https://github.com/chrisliu298/nanoresearch.git ~/.nanoresearch
# 2. Register the marketplace — add this to ~/.claude/settings.json:
# "extraKnownMarketplaces": {
# "nanoresearch": {
# "source": { "source": "directory", "path": "~/.nanoresearch" }
# }
# }
# 3. Install the plugin
claude plugin install nanoresearch@nanoresearch
# Required: Codex MCP (for GPT-5.4 reviewers and brainstorming)
npm install -g @openai/codex
codex setup # set model to gpt-5.4
claude mcp add codex -s user -- codex mcp-server
# Required for paper writing: LaTeX
# macOS: brew install --cask mactex && brew install poppler
# Ubuntu: sudo apt install texlive-full latexmk poppler-utils
# Full pipeline — one command, go to sleep
/nanoresearch "factorized attention for efficient long-context transformers"
# Individual phases
/nanoresearch:scout "topic" # just scout
/nanoresearch:loop # just experiment loop
/nanoresearch:write # just paper writing
/nanoresearch:review # just peer review
# Override defaults
/nanoresearch "topic" — budget: 8h # longer experiment budget
/nanoresearch "topic" — loop: 50 # bound to 50 iterations
/nanoresearch "topic" — venue: NeurIPS # target venue
/nanoresearch "topic" — skip-scout # skip idea discovery
/nanoresearch "topic" — skip-to-write # skip to paper writing
The write phase is a two-sub-phase loop, not a one-shot draft:
Sub-phase 1: Section-by-section drafting. Sections are written in dependency order (method → experiments → related work → introduction → conclusion → abstract). Each section gets a GPT-5.4 xhigh review gate before the next begins — catching issues early before they propagate.
npx claudepluginhub chrisliu298/nanoresearch --plugin nanoresearchOh My Paper research harness: memory system, Codex delegation, and pipeline commands for academic research projects.
Academic paper writing skills for ML conferences (NeurIPS, ICML, ICLR, AAAI)
Three AI models, one synthesis — multi-model research workflow for scientific domains
Production-grade academic research pipeline for Claude Code: research → write → review → revise → finalize. 4 skills, 27 modes, 39-agent ensemble, v3.7.3 + v3.8 L3 claim-faithfulness gate, v3.9.0 cross-index triangulation, v3.10 triangulation policy layer, v3.11 deterministic citation verification gate (#182).
Research-team agents for Claude Code: supervisor, analysis-implementer, paper-writer, figure-descriptor, reviewer, literature-curator.
Semi-automated research assistant for academic research and software development, with skills for literature review, experiments, analysis, writing, and project knowledge management