| --- |
| license: apache-2.0 |
| pretty_name: Science Desk |
| tags: |
| - openenv |
| - reinforcement-learning |
| - agents |
| - tool-use |
| - terminal-bench |
| - science |
| task_categories: |
| - text-generation |
| --- |
| |
| # Science Desk |
|
|
| An OpenEnv environment for the [OpenEnv Arena](https://openenvarena-arena.hf.space): natural-science data tasks in |
| the style of public SkillsBench v1.1 science tasks, served through the same `tbench2_env` interface the arena uses for |
| its own tasks. 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. |
|
|
| Image: `ghcr.io/syndicate604/science-desk` (linux/amd64). Source: `source/` in this repo. Forked from |
| [dnakhla/terminal-desk](https://huggingface.co/datasets/dnakhla/terminal-desk) v2 (Apache-2.0); see `NOTICE`. |
|
|
| ## Interface |
|
|
| Terminal Desk v2's copy of OpenEnv's upstream `tbench2_env` contract: `exec`, `write_file`, `evaluate`, `close`; |
| the instruction repeated in every observation; output not cut; per-task budgets of 4096 completion tokens (model |
| tokens plus every observation after the first), 8192 context tokens and 32 tool calls. The image has numpy, scipy, |
| pandas and astropy. |
|
|
| ## Tasks |
|
|
| 10 families x 3 levels = 30 task ids (`<family>-L<level>`); each id is one fixed problem, so a GRPO group compares |
| answers to the same question. `<family>-L<level>-i<k>` names further fixed instances. Eight families are modelled |
| on SkillsBench science tasks with generated data: `sci_units` (lab-unit-harmonization), `sci_protein` |
| (protein-expression-analysis), `sci_flood` (flood-risk-analysis), `sci_trend` (lake-warming-attribution), |
| `sci_quake` (earthquake-plate-calculation), `sci_period` (exoplanet-detection-period), `sci_picking` |
| (seismic-phase-picking), `sci_vitals` (radar-vital-signs). `sci_kinetics` and `sci_stoich` come from Terminal Desk. |
| `tasks.jsonl` lists every id with its fixed instruction, output file and screen numbers. |
|
|
| Every task is checked by tests: the reference answer scores 1, missing or corrupted output scores 0, and an |
| independent solver that reads only the files and the instruction scores 1 on four instances per level. |
|
|
| ## Submission (v5) |
|
|
| `selected_tasks.txt`, 12 tasks with both successes and failures in a screen of Qwen3.8-27B (thinking off) via |
| OpenRouter, DeepInfra bf16 pinned, 6 episodes each, under the budgets above with observation tokens counted by the |
| Qwen tokenizer on arrival: |
|
|
| | Task | Solved / 6 | |
| |---|---:| |
| | sci_picking-L1 / L3 | 3 / 1 | |
| | sci_units-L2 / L3 | 3 / 3 | |
| | sci_quake-L3 | 2 | |
| | sci_trend-L3 | 4 | |
| | sci_flood-L2, sci_protein-L3, sci_stoich-L3 | 5 each | |
| | sci_period-L1 / L2 / L3 (with astropy) | 3 / 3 / 2 | |
|
|
| Failures include malformed action replies, stopping before writing the output, budget exhaustion and wrong |
| results. A malformed final reply ends the episode through `evaluate`, so a correct file already written still |
| scores 1. |
|
|
| Limitations: six episodes per task is noisy, and earlier screens underestimated the arena-trained model, so these |
| numbers are not predictions of arena solve rates. Twelve ids are twelve fixed problems: this is an |
| interface-and-workflow experiment, not broad science coverage. |
|
|
| ## Build and test |
|
|
| See `source/README.md`. |
|
|
