---
pretty_name: MuxaBench
language:
- en
license: other
license_name: muxa2
license_link: LICENSE
size_categories:
- n<1K
task_categories:
- question-answering
- other
tags:
- benchmark
- evaluation
- code
- systems-programming
- zig
- coding-benchmark
- llm-benchmark
- rubric
configs:
- config_name: default
  data_files:
  - split: test
    path: MuxaBench.jsonl
dataset_info:
  features:
  - name: id_aa
    dtype: string
  - name: title
    dtype: string
  - name: category
    dtype: string
  - name: prompt
    dtype: string
  - name: system_prompt
    dtype: string
  - name: rubric
    dtype: string
  - name: expected_deliverables
    dtype: string
  - name: reference_files
    dtype: string
  splits:
  - name: test
    num_bytes: 270174
    num_examples: 26
  download_size: 270174
  dataset_size: 270174
---

# MuxaBench: Systems Programming Benchmark

MuxaBench is an evaluation benchmark designed to assess LLMs and coding agents on 26 staff/principal-level systems programming tasks in pure **Zig 0.15.1**.

## Benchmark Overview

The benchmark contains 26 tasks divided into 5 core engineering domains:
1. **Rendering and content pipelines** (6 tasks): ReSTIR DI on BVH, perceptual 8 bpp texture codec, analytic-AA vector rasteriser, 10 km² city + navmesh, HZB occlusion culling.
2. **Physics and motion simulation** (6 tasks): GJK/EPA CCD rigid body, XPBD rope/cloth, active ragdoll, full-body IK, 128³ Eulerian multigrid smoke solver, quadcopter simulator.
3. **Numerical and ML kernels** (5 tasks): Fused transformer step, Int8 GQA engine with paged KV-cache, runtime SPIR-V shader generator, FlashAttention causal attention, spatial audio synthesizer.
4. **Systems infrastructure** (5 tasks): Crash-safe WAL KV engine, deterministic matching engine (10M ops/s), x86_64 microkernel slice (4-level VMM/page allocator), Raft under 30% packet loss, JIT compiler with generational GC.
5. **Security and cryptography** (4 tasks): Constant-time ML-KEM-768, ML-DSA-65 signatures, R1CS SHA-256 prover, static PE heuristic analyzer.

## Evaluation Methodology (Score Against Rubric)

Each task in MuxaBench is scored **against a multi-criteria rubric** with discrete scoring levels (0 to 2 or 3) across ~6 explicit criteria per task.
Models are evaluated on:
- Strict correctness and mathematical formulation
- Enforcing zero dynamic allocation in hot loops after initialization
- Deterministic hashing across runs and threads
- metric_provenance: verifying that reported metrics trace directly to executable test harness code