AutumnBench Public Benchmark
Bibliographic record
Abstract
AutumnBench Dataset Release This dataset accompanies the paper “Benchmarking World-Model Learning with Environment-Level Queries.” It contains the benchmark release for AutumnBench, including task prompts, ground-truth answers, and environment programs used for evaluation. The release includes a `prompts/` folder containing the task prompts for each AutumnBench environment and challenge task. Files are named {env_name}_{task_name}.json, where task_name is one of mfp, cd, or planning. It also includes an `answers/` folder containing the ground-truth answers for each task. For mfp, each file gives the index of the correct answer option (0-indexed). For cd, each file gives the condition after which the change occurs. For planning, each file gives the optimal number of steps required to reach the target. The `programs/` folder contains the Autumn programs for the original environments, as well as the modified environments used in the change-detection task. The Autumn interpreter for running the programs is available in the GitHub repository: https://github.com/BasisResearch/Autumn.cpp. Finally, the release includes a shared `color_dict.yaml` file that maps numeric identifiers to color names. This release is intended to support reproducibility and further analysis of the AutumnBench benchmark described in “Benchmarking World-Model Learning with Environment-Level Queries.”
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.121 | 0.021 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".