Datasets:

myhapro
/

MuxaBenchGold

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

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