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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
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