| --- |
| pretty_name: MuxaBenchGold |
| language: |
| - en |
| license: mit |
| size_categories: |
| - n<1K |
| task_categories: |
| - text-generation |
| tags: |
| - benchmark |
| - agentic-coding |
| - code-generation |
| - zig |
| - physics |
| - biomechanics |
| - systems |
| - security |
| - code |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train.jsonl |
| --- |
| |
| # MuxaBenchGold |
|
|
| Agentic-coding benchmark in pure **Zig**: 55 tasks from easy to expert. |
|
|
| - 26 base tasks: rendering, physics, ML kernels, systems, crypto |
| - 29 hardest expand tasks: physics + biological mechanics + Zig applications hitting AI weak spots (delay instability, friction flips, fluid-structure coupling, fatigue/history, topology change + determinism, energy drift, zero-alloc) |
| - Each task: human-style English `prompt` + ONE `expected_result` + simple score `0-10`, `pass_score 7` |
| - Every task requires: build with your local `zig version` (no fixed version), report `zig_version`, deterministic rerun hashes, zero allocations after init proven by counters |
|
|
| ## Load |
|
|
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("muxapro/MuxaBenchGold") |
| print(ds["train"][0].keys()) |
| # id, title, category, difficulty, prompt, expected_result, checks, score_max, pass_score, scoring |
| ``` |
|
|
| ## Score |
|
|
| - Each `checks` entry is yes/no with numbers |
| - Sum to `score_max=10`, pass if `>=pass_score=7` |
| - Human + LLM-judge friendly: one result, one score |
|
|
| ## Files |
|
|
| - `data/train.jsonl` — 55 lines, one task per line (runner) |
| - `MuxaBench-Gold.json` — pretty 922-line version for reading in editor |
| - Source of truth: local `Desktop/Muxa/MuxaBench-Gold.json` |
|
|
| ## License |
|
|
| MIT |
|
|
