| --- |
| license: apache-2.0 |
| pretty_name: TB-MathModel |
| task_categories: |
| - text-generation |
| - question-answering |
| language: |
| - en |
| - zh |
| tags: |
| - math-modeling |
| - terminal-bench |
| - agent-benchmark |
| - cumcm |
| - mcm |
| - lean4 |
| - multi-oracle-evaluation |
| size_categories: |
| - n<1K |
| --- |
| |
| # Terminal-Bench Math Modeling |
|
|
| **TB-MathModel** (short: `tb-mathmodeling`) is a Terminal-Bench-style benchmark for |
| end-to-end mathematical-modeling workflows. It contains **18 tasks**: CUMCM and MCM |
| problems A, B, and C from 2023, 2024, and 2025. |
|
|
| Code, verifiers, oracles, scoring scripts, and leaderboard live in the GitHub repository: |
| [wxj630/terminal-bench-math-modeling](https://github.com/wxj630/terminal-bench-math-modeling). |
| This dataset mirrors its `tasks/` directory so the tasks can be downloaded without cloning. |
|
|
| Paper: [arXiv:2611.08237](https://arxiv.org/abs/2611.08237) (cs.CL), doi:[10.48550/arXiv.2611.08237](https://doi.org/10.48550/arXiv.2611.08237) |
|
|
| ```bibtex |
| @misc{wu2026terminalstylemathematicalmodelingbench, |
| title={Terminal-style Mathematical Modeling Bench: The First Agentic and Verifiable Benchmark for Mathematical Modeling}, |
| author={Xiaojun Wu and Cehao Yang and Xuhui Jiang and Chengjin Xu and Jia Li and Jian Guo}, |
| year={2026}, |
| eprint={2611.08237}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CL}, |
| url={https://arxiv.org/abs/2611.08237}, |
| } |
| ``` |
|
|
| ## What is in this dataset |
|
|
| ``` |
| tasks/ |
| ├── CUMCM/ # 9 tasks: 2023 A/B/C, 2024 A/B/C, 2025 A/B/C |
| └── MCM/ # 9 tasks: 2023 A/B/C, 2024 A/B/C, 2025 A/B/C |
| ``` |
|
|
| Each task directory is self-contained: |
|
|
| | Path | Contents | |
| |---|---| |
| | `instruction.md` | The original contest statement plus the benchmark's final-answer requirement | |
| | `environment/` | Dockerfile, mirrored official attachments, and `output_schema.json` | |
| | `tests/` | Verifier: `score_config.json`, `score_result.py`, `test_outputs.py`, `expected_result.json` | |
| | `solution/` | Oracle reproduction and `solve.sh` | |
| | `task.toml` | Harbor task metadata: timeouts, resources, scoring scope | |
| | `scoring_table.md` | Human-readable scoring panel for the task | |
|
|
| The required artifact for every task is `/root/results/<task-slug>_result.json`. |
|
|
| ## Scoring |
|
|
| Real modeling problems rarely have one exact answer, so each task is scored on a single |
| core scalar for the final question — an optimized objective, a recommended policy value, |
| or a binary decision — rather than on every number in a report. |
|
|
| Two reference constructs are used: |
|
|
| - **B-Eval** (default): `raw_panel_score - baseline_panel_score`, i.e. how much the |
| submission improves over a baseline method. May be negative. |
| - **BO-Eval**: the baseline-to-outstanding normalized score, using explicit |
| `baseline_value` / `outstanding_value` / `normalization_direction` per metric. |
|
|
| The released board in the GitHub repository additionally reports a **Multi-O Eval**, which |
| averages each task over every O-award human paper that reports the task's scored field, and |
| a **Multi-BO index** anchored at DeepSeek V4.1 Flash = 1.000. |
|
|
| ## Leaderboard |
|
|
| | Rank | Model | Multi-O Eval | Multi-BO index (Flash = 1.000) | |
| |---:|---|---:|---:| |
| | 1 | claude-code + Fable 5.1 | **64.62%** | 1.1637 | |
| | 2 | codex + GPT-6 Astra | **61.47%** | 1.1200 | |
| | 3 | opencode + DeepSeek V4.1 Flash | **59.81%** | 1.0000 | |
| | 4 | codex + GPT-5.5 xhigh | **59.75%** | 1.0061 | |
| | 5 | opencode + DeepSeek V4.1 Flash (max) | **48.11%** | 0.8636 | |
|
|
| Single runs; across 3 seeds the per-model deviation is ±1.3–2.9pp. |
|
|
| ## Quickstart |
|
|
| ```bash |
| harbor run -p tasks/CUMCM/cumcm-2024-a-dragon-dance -a oracle |
| harbor run -p tasks/MCM/mcm-2024-c-tennis-momentum -a codex -m openai/gpt-5 |
| ``` |
|
|
