| --- |
| language: |
| - en |
| license: mit |
| pretty_name: Klein-Code-Bench |
| task_categories: |
| - text-generation |
| tags: |
| - code |
| - benchmark |
| - project-generation |
| - small-language-models |
| - local-models |
| - python |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: default |
| default: true |
| data_files: |
| - split: test |
| path: klein_code_bench/data/*.jsonl |
| - config_name: easy |
| data_files: |
| - split: test |
| path: klein_code_bench/data/easy.jsonl |
| - config_name: intermediate |
| data_files: |
| - split: test |
| path: klein_code_bench/data/intermediate.jsonl |
| - config_name: hard |
| data_files: |
| - split: test |
| path: klein_code_bench/data/hard.jsonl |
| - config_name: max |
| data_files: |
| - split: test |
| path: klein_code_bench/data/max.jsonl |
| --- |
| |
| <p align="center"> |
| <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/69ae829a8408eeb0d7dd5491/943kN5YZ59lFCUN1jiKz5.png" width="180" alt="KleinML"> |
| </p> |
|
|
|
|
|
|
| # Klein-Code-Bench |
|
|
| Can a model build a working program from scratch? Klein-Code-Bench gives a coding model a specification and an |
| empty directory, and checks whether the finished Python project passes every acceptance test. |
|
|
| - **260 tasks** in four tiers of 65 |
| - **Pass or fail per project:** a task counts only if all of its tests pass |
| - **Runs locally:** standard-library Python, no Docker, no cloud model, no extra dependencies |
|
|
| | Tier | Intended model size | What the model builds | Tasks | |
| | --- | --- | --- | ---: | |
| | easy | 2–8B | A JSONL command-line tool with a domain algorithm and error recovery | 65 | |
| | intermediate | 8–16B | A stateful document service: CRUD, listing, domain evaluation | 65 | |
| | hard | 16–32B | A service with atomic batches, revision checks and nested transactions | 65 | |
| | max | 60–200B | A durable service that survives being force-killed, with idempotency, history and pagination | 65 | |
|
|
| The model sizes are design targets. Version 0.1.0 has not been calibrated against real models yet, and there is no |
| leaderboard. Any model can be run on any tier. |
|
|
| ## How a task works |
|
|
| 1. The model gets `PROMPT.md`: the full specification plus two examples. |
| 2. It writes `main.py` and any other files into an empty `project/` directory. There's no starter code and no |
| existing repository to fix. |
| 3. The evaluator runs the project as a subprocess, sends JSONL requests and checks every response and state change. |
| In the max tier, the process is force-killed right after its last acknowledged response, restarted on the same |
| state directory, and must continue correctly. |
|
|
| The 65 tasks per tier come from the same 65 domain families: text processing, CSV/JSON tools, calendars, numerical |
| utilities, graphs, scheduling, ledgers, geometry, parsing, simulation and more. Each tier adds service behavior on |
| top of the one below. Full list: [task catalog](docs/CATALOG.md). |
|
|
| Because the tiers share families, results across tiers are correlated. Report each tier separately, and don't |
| train on one tier and then present another as an uncontaminated evaluation. |
|
|
| ## Quick start |
|
|
| Requires Python 3.11+ on Linux or macOS. |
|
|
| ```bash |
| git clone https://huggingface.co/datasets/klein-ml/klein-code-bench |
| cd klein-code-bench |
| python -m pip install -e . |
| klein-bench list --subset easy |
| |
| # Create a workspace: PROMPT.md, plus an empty project/ directory for the model |
| klein-bench prepare kcb-easy-001-word-frequency --output workspaces/first |
| |
| # Give workspaces/first/PROMPT.md to your model and let it write into workspaces/first/project/, then: |
| klein-bench evaluate kcb-easy-001-word-frequency \ |
| --solution workspaces/first/project --output results/first.json |
| klein-bench score results/first.json |
| ``` |
|
|
| - `python -m klein_code_bench` works the same as `klein-bench`. |
| - Exit code 1 means a failed submission, 2 an invalid invocation. |
| - `--public-only` runs just the two examples as a smoke test. Those results can't be scored. |
|
|
| Bring your own agent or local API endpoint, or use the [banana-harness integration](docs/BANANA_HARNESS.md). |
| Record the model's inference settings with your results. |
|
|
| ## Loading the dataset |
|
|
| ```python |
| from datasets import load_dataset |
| |
| easy = load_dataset("klein-ml/klein-code-bench", "easy", split="test") # 65 tasks |
| all_tasks = load_dataset("klein-ml/klein-code-bench", split="test") # 260 tasks |
| ``` |
|
|
| Each tier is its own config with a single `test` split; the default config contains all four. There is no |
| training split, and no loading script or `trust_remote_code` is needed. Pin `revision` to a commit when you |
| report results. |
|
|
| | Field | Content | |
| | --- | --- | |
| | `task_id` | Stable unique ID | |
| | `subset`, `family`, `category`, `title` | Tier and task description | |
| | `target_model_parameters` | Intended model-size band (not calibrated) | |
| | `language`, `entrypoint`, `protocol_version` | Python, `main.py`, protocol version | |
| | `prompt` | The full specification the model sees | |
| | `public_tests` | Example requests and responses (JSON string) | |
| | `acceptance_tests` | Evaluator scenarios: sessions, requests, expected responses (JSON string) | |
| | `num_scenarios`, `num_assertions`, `timeout_seconds` | Test counts and the per-session time limit | |
|
|
| The test fields are stored as JSON strings so that all tasks share one schema; decode them with `json.loads`. |
|
|
| **Never put `acceptance_tests`, the reference implementations or the evaluator source into the model's prompt, |
| context or workspace.** They're public for reproducibility, so the benchmark depends on you keeping them away from the |
| model. |
| |
| ## Scoring |
| |
| The metric is **resolve@1**: one project per task, and it counts only if every test passes. |
| |
| - **Per tier:** resolved tasks / 65. **Overall:** resolved tasks / 260. |
| - Missing submissions count as failures. The report also shows the rate among submitted tasks and flags incomplete runs. |
| - Partial assertion success is reported for diagnosis, but it never counts as resolved. |
| |
| Each scenario starts from a fresh copy of the submission and an empty state directory; sessions within a max-tier |
| scenario share that directory. JSON comparison keeps array order and tells booleans apart from numbers. Floats use |
| an absolute and relative tolerance of 1e-9; integers are compared exactly. Default limits are 10 seconds per session |
| and 1 MiB of stdout. Details: [evaluation protocol](docs/EVALUATION.md). |
| |
| | Tier | Assertions | |
| | --- | ---: | |
| | easy | 3,075 | |
| | intermediate | 5,415 | |
| | hard | 9,705 | |
| | max | 15,490 | |
| | **total** | **33,685** | |
| |
| The tests cover: |
| |
| - 65 hand-written golden outputs |
| - 24 generated inputs per family (seed 1729, shared across tiers) |
| - boundary cases and malformed requests |
| - failure atomicity, nested rollback and revision conflicts |
| - retry and restart sequences |
| |
| All expected outputs are computed before grading, never inside the candidate's process. |
| |
| ### Verifying the benchmark itself |
| |
| ```bash |
| python -m unittest discover -s tests -v # unit and property tests for the benchmark and runner |
| klein-bench build # regenerate data and manifest from source |
| klein-bench validate --output results/validation.json # every reference must pass, every empty project must fail |
| ``` |
| |
| The reference implementations exist to maintain the evaluator. Export one for auditing with |
| `klein-bench reference TASK_ID --output DIR`. The published `validation-summary.json` documents the dataset |
| manifest and validation totals; it is not a model score. |
| |
| ## Security |
| |
| The runner is **not a sandbox**. It does the following: |
| |
| - removes inherited credentials from the environment |
| - runs each submission from a temporary copy |
| - rejects symlinks |
| - limits time and output |
| - kills the whole process group when it's done |
| |
| Generated code can still reach your filesystem and network. Run it under a separate unprivileged user, on a machine |
| you can afford to lose. Timing depends on the host, so record your hardware with your results. |
| |
| ## Provenance and limitations |
| |
| - **Origin:** every task was written for this benchmark. None were copied from SWE-bench, LiveCodeBench or other |
| benchmarks. That covers how the tasks were authored; it doesn't guarantee that standard algorithms in the easy tier |
| never appear in a model's training data. |
| - **Scope:** Python only. The tests measure behavioral correctness, not code quality, architecture or UI. They don't |
| cover concurrent writers, disk corruption or power loss during a write. |
| - **Coverage:** the reference implementations and independent golden checks make the dataset internally consistent, |
| but the tests can't prove that no specification or expected output contains an error. |
| - **License:** MIT for prompts, test data and reference implementations. |
|
|
| ## Contributing |
|
|
| A new task needs: |
|
|
| - a contract (the specification) |
| - meaningful boundary tests |
| - an independent check of the expected outputs |
| - a reference implementation that passes |
|
|
| Changes to a contract or expected behavior require a new dataset version. Task IDs stay the same only for |
| backward-compatible fixes. |
