Datasets:

YuhaoZhan
/

PACE-Bench

pretty_name: PACE-Bench
language:
  - en
license: mit
size_categories:
  - n<1K
task_categories:
  - text-generation
tags:
  - benchmark
  - agents
  - agent-evaluation
  - self-evolving-agents
  - physics
  - physical-reasoning
  - code
  - code-generation
  - executable-design
  - simulation
  - box2d
  - dynamic-environments
citation: |
  @misc{zhan2026pacebenchbenchmarkingphysicsadaptation,
        title={PACE-Bench: Benchmarking Physics Adaptation via Code Evolution in Dynamic Environments},
        author={Yuhao Zhan and Bingxiang He and Zecong Tang and Chaojun Xiao},
        year={2026},
        eprint={2608.14441},
        archivePrefix={arXiv},
        primaryClass={cs.AI},
        url={https://arxiv.org/abs/2608.14441},
  }
source_datasets: []

Task mirror only. This dataset repository contains the 36 executable task definitions. The CLI, shared runtime, evaluation engine, self-evolving methods, reporting code, and coding-agent sandbox live in the PACE-Bench GitHub repository.

Mirror provenance

Source: src/pace_bench/tasks/categories
Included: 36 base tasks and primitives_api.json
Excluded: shared runtime, evaluation methods, generated results, caches, and local artifacts

PACE-Bench

Self-evolving agents improve future behavior from interaction experience, yet existing evaluations typically keep execution conditions fixed. PACE-Bench tests whether an agent can adapt a previously successful code-driven design after an environment shift causes it to fail.

What is PACE-Bench?

PACE-Bench contains 144 source-to-target adaptation pairs across six physics domains. Each pair keeps the goal and interface fixed:

A code-driven design succeeds in the source environment.
The same design fails in a mutated target environment.
The agent uses diagnostic sandbox feedback to revise the design.
The adapted design must succeed under the target physics.

Benchmark scale	Count
Physics domains	6
Base tasks	36
Environments per task	5
Evaluation environments	180
Source-to-target pairs	144

What is included here?

Domain	Prefix	Tasks
Statics / Equilibrium	`S`	6
Kinematics / Linkages	`K`	6
Dynamics / Energy	`D`	6
Granular / Fluid Interaction	`F`	6
Cybernetics / Control	`C`	6
Exotic Physics	`E`	6

tasks/
├── Category1_Statics_Equilibrium/S_01 ... S_06/
├── Category2_Kinematics_Linkages/K_01 ... K_06/
├── Category3_Dynamics_Energy/D_01 ... D_06/
├── Category4_Granular_FluidInteraction/F_01 ... F_06/
├── Category5_Cybernetics_Control/C_01 ... C_06/
├── Category6_ExoticPhysics/E_01 ... E_06/
└── primitives_api.json

Each task package contains:

File	Role
`agent.py`	Source and four target reference solutions
`environment.py`	Box2D world, primitives, and mutable physics
`evaluator.py`	Success criteria, score, constraints, and raw metrics
`feedback.py`	Diagnostic feedback derived from measured metrics
`prompt.py`	Task description, exposed values, and primitive API
`renderer.py`	Evaluation-neutral visualization
`stages.py`	Four target mutations and prompt updates

These are executable benchmark definitions, not a conventional row-based dataset. The Dataset Viewer is therefore not the primary interface.

Download the task mirror

from huggingface_hub import snapshot_download

path = snapshot_download(
    repo_id="YuhaoZhan/PACE-Bench",
    repo_type="dataset",
    allow_patterns=["tasks/**"],
)
print(path)

Or clone it directly:

git clone https://huggingface.co/datasets/YuhaoZhan/PACE-Bench

Use this mirror when you need to inspect, archive, or distribute the task definitions without the full evaluation stack.

Run the benchmark

The Hugging Face mirror is not standalone. For evaluation, install the complete GitHub repository with uv:

git clone https://github.com/thunlp/PACE-Bench.git
cd PACE-Bench

uv venv .venv --python 3.10
source .venv/bin/activate  # Windows: .venv\Scripts\activate
uv pip install -r requirements.txt

pace-bench list --task S_01
pace-bench validate --task S_01

The GitHub checkout already contains the same task definitions. You do not need to download this mirror separately to run PACE-Bench.

Intended use

Evaluate adaptation after controlled physical environment changes
Study feedback-driven code evolution and self-evolving agents
Compare context-, memory-, search-, and parameter-based methods
Analyze physical reasoning, redesign, exploration, and convergence failures
Inspect or extend executable task definitions

Scope and limitations

Physics: 2D rigid-body systems in Box2D
Language: English prompts and diagnostic feedback
Not covered: 3D/deformable physics, full fluids, perception, navigation, and multi-agent coordination
Execution safety: run generated code on a dedicated evaluator host without unrelated credentials
Version note: these tasks include an additional difficulty-escalation pass beyond the paper version, so new scores may differ slightly while its conclusions remain unchanged

Issues and contributions

The Hugging Face repository is a distribution mirror. Please open task issues, fixes, and pull requests in the GitHub repository.

License

PACE-Bench is released under the MIT License.

Citation

@misc{zhan2026pacebenchbenchmarkingphysicsadaptation,
      title={PACE-Bench: Benchmarking Physics Adaptation via Code Evolution in Dynamic Environments},
      author={Yuhao Zhan and Bingxiang He and Zecong Tang and Chaojun Xiao},
      year={2026},
      eprint={2608.14441},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2608.14441},
}