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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
```
|