AI EVALUATION, IN CONTEXT.BY EQUALITY MACHINE

Claire Radar

EvoCode-Bench

EvoCode-Bench evaluates coding agents in multi-turn iterative interactions, with 26 stateful coding tasks and 227 evaluated rounds. Each task preserves the agent's workspace for 5-15 rounds, specifies requirements via observable behavior, and uses cumulative executable tests to verify new and prior requirements. Scoring uses MT@4, a four-attempt fail-stop multi-round score, and SR, a single-round score from a reference-completed prior state.

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.

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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

Hugging Face paper upvotes
1

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Dated attention observations, not live counts or benchmark scores.

Evaluation context and evidence

Source
Claire Radar
Publisher
Not recorded
Benchmark release
2026-05-22
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.