Datasets:
Science Desk
An OpenEnv environment for the OpenEnv Arena, forked from
dnakhla's Terminal Desk v2 (Apache-2.0, see NOTICE). Each episode is a workspace under /app with a short
instruction. The agent runs commands (exec), writes files (write_file) and sends evaluate; a hidden
verifier checks the one output file the task names and returns 1 or 0.
The interface is Terminal Desk's copy of OpenEnv's upstream tbench2_env wrapper, which the arena operator
uses for its own tasks: same action and observation schema, the instruction repeated in every observation,
output not cut, the operator's per-task budgets. The image includes numpy, scipy and pandas.
Tasks
10 natural-science families at three levels (30 task ids, <family>-L<level>). Higher levels add volume and
a caveat the agent has to find and apply. The eight new families are modelled on public SkillsBench v1.1
science tasks, with their own generated data.
| Family | Modelled on | Output file |
|---|---|---|
sci_kinetics |
(Terminal Desk) | fit.json: first-order rate constant, half-life; Arrhenius at L3 |
sci_stoich |
(Terminal Desk) | batch_report.json: limiting reagent, molar mass, isolated grams |
sci_units |
lab-unit-harmonization | harmonized.csv: canonical units; L2 messy spellings, L3 re-runs and a QC drop |
sci_protein |
protein-expression-analysis | top_hits.csv: top log2 fold changes; L2 missing values, L3 sample sheet and Welch t filter |
sci_flood |
flood-risk-analysis | flood_results.csv: flood days per gauge; L2 missing codes, L3 m/ft thresholds |
sci_trend |
lake-warming-attribution | trend.json: OLS and Sen slopes; L2 monthly data, L3 Mann-Kendall Z |
sci_quake |
earthquake-plate-calculation | answer.json: event furthest from a boundary; L2/L3 filters, L3 lon/lat order |
sci_period |
exoplanet-detection-period | period.json: Lomb-Scargle period; L2 quality flags, L3 two sectors |
sci_picking |
seismic-phase-picking | picks.json / picks.csv: P (and S) arrival samples; L3 three stations |
sci_vitals |
radar-vital-signs | vitals.json / vitals.csv: breathing and heart rate; L3 three recordings |
sci_period, sci_picking and sci_vitals inputs are 9k-33k tokens: printing them overruns the context
budget, so the agent has to process them with a script.
Reward
Binary, on evaluate: 1.0 when the output file exists and every check passes (exact key set or header, every
row, stated tolerances), else 0.0. close ends the episode without a reward. After 32 calls the environment
evaluates on its own. No partial credit.
Instances and isolation
Each task id builds one fixed instance (seeded from the id), so a GRPO group of rollouts compares answers to
the same problem; <family>-L<level>-i<k> names further fixed instances. The server runs as root outside
/app; exec and write_file run as nobody with CPU, memory, file-size and process limits, cannot read
the server tree, and every nobody process is killed after each action and before verification. Expected
outputs live only in server memory; the verifier refuses symlinks, FIFOs and oversized files.
Build and test
python3 -m venv .venv && .venv/bin/pip install vendor/openenv-0.6.1.dev0-py3-none-any.whl pytest websockets httpx numpy scipy
PYTHONPATH=. .venv/bin/pytest -q tests # oracle = 1, corrupt = 0, schema, independent solvers
PYTHONPATH=. .venv/bin/python tools/check_family.py # per family: oracle 1, corrupt 0, empty 0
DOCKER_DEFAULT_PLATFORM=linux/amd64 docker build -t ghcr.io/syndicate604/science-desk:v5 .
bash tools/pool.sh up ghcr.io/syndicate604/science-desk:v5 8
.venv/bin/openenv validate --url http://localhost:18001
.venv/bin/python tools/replay.py http://localhost:18001 # every task over WebSocket
tools/calibrate.py plays Qwen3.8-27B through OpenRouter under the arena's token budgets, with a hard spend
cap (--budget). Screens have underestimated the arena model, so treat them as sanity checks only.