Autoresearch is a self-contained autonomous ML experimentation framework created by Andrej Karpathy. Its core premise is that an AI agent (e.g., Claude or Codex) modifies a single training script (`train.py`), runs a fixed 5-minute training experiment on a single GPU, evaluates validation bits-per-byte (BPB), and keeps or discards changes based on whether the metric improved — all without human intervention. The human programs the agent via `program.md`, which serves as a lightweight instruction set for the experiment loop. The codebase has two fixed files and one editable file. `prepare.py` handles data downloading (parquet shards from HuggingFace), BPE tokenizer training (via rustbpe), a custom BOS-aligned packed dataloader with best-fit document packing (zero padding), and a fixed BPB evaluation harness. `train.py` contains a GPT model implementation with RoPE, sliding window attention (SSSL pattern), ResFormer value embeddings, RMS normalization, and a hybrid Muon+AdamW optimizer with per-parameter-type learning rate scheduling. The model uses `torch.compile` and runs in bf16 with Flash Attention 3. The `analysis.ipynb` notebook provides post-hoc visualization of experiment results from `results.tsv`. The project is explicitly designed for a single NVIDIA GPU (H100 tested), requires no distributed training infrastructure, and pins all dependencies including PyTorch 2.9.1 with CUDA 12.8. The architecture is deliberately minimal — the entire research loop runs in two Python files with roughly 1,600 lines of non-comment code total.