AI EVALUATION, IN CONTEXT.BY EQUALITY MACHINE

Claire Radar

AutoWorldModel-Bench

AutoWorldModel-Bench evaluates coding agents on autonomously improving a base world model under fixed compute budget across eight game environments. Uses structured-state representation, with held-out test split and closed-loop iteration.

Evidence available for this benchmark

Numeric results have not been collected for this source record. Use the paper, code or dataset below to check the task and experiments; this does not imply that the original work contains no results.

Start with these resources

Cited documents and dates (1)

Catalog collected on 2026-10-14; resources and fields reflect that saved source.

Recorded attention

Observed on: 2026-10-11

GitHub stars
4
Hugging Face paper upvotes
14

electronicarts/autoworldmodelbench · View source snapshot

Dated attention observations, not live counts or benchmark scores.

Evaluation context and evidence

Source
Claire Radar
Publisher
Not recorded
Benchmark release
2026-07-20
Collected
2026-10-14
Modality
Not recorded

These are source-reported observations, not independent reruns by this site. Check the task version, evaluation protocol, unit and reporting date before comparing models. Results from different setups are not a universal ranking.

Reported results 0

This source record has no numeric results in our collection. That does not mean a score of zero or that no public results exist. Consult the original resources above.