Epoch data and evaluation setting

Epoch AI publishes this benchmark in its capability collection. Imported rows retain the published score column and model configuration. Original authors and evaluation providers remain credited in the result references.

How to read the result

Compare the named task version, tools and scoring rule. Epoch-run evaluations and externally collected results have different provenance. ECI inclusion does not make a benchmark score an ECI value.

Epoch attribution and score coverage

Data is attributed to Epoch AI under CC BY 4.0, with original evaluation providers credited in the linked source snapshots. Scores are imported only where a published mapping identifies the field and its scale; missing or incompatible records are omitted rather than assigned zero.

Published evaluation

Native multipliers against the source expert baseline; higher is better. Opus 5.5 and Fable 5.1 use seven scored tasks after one refused task was excluded, while other main-cohort models use eight. Super-Think and effort ablations remain source-labeled configurations; compute and final performance are distinct measures.

Where this fits in an evaluation suite

The agent wrapper is part of the system. Pin tools, dependencies, timeouts, tests, and the repository revision. Compare contest coding separately from repository maintenance.

Start with a capability hypothesis, then choose a second test with a different failure mode. Keep the checkpoint, reasoning effort, agent tools, sampling policy, and scoring procedure fixed when measuring a training intervention. Record failures and missing outputs in the denominator.

A reproducible comparison

Native multipliers against the source expert baseline; higher is better. Opus 5.5 and Fable 5.1 use seven scored tasks after one refused task was excluded, while other main-cohort models use eight. Super-Think and effort ablations remain source-labeled configurations; compute and final performance are distinct measures.

Publish the exact run configuration, item coverage, score units, and available uncertainty. If a source supplies only an aggregate or a single run, preserve that limit. A new model name or a rounded score alone does not establish a capability improvement.

PRIMARY EVIDENCE & FURTHER READING

  1. TasteVal: Measuring the Experimental Research Taste of AI Systems Against Human Experts — published result table ↗

    Jaffe, Oliver, Sherburn, Dane · 2026

Reviewed 2026-10-27. Explanation and interpretation by RLTasks. Benchmark ownership remains with its original authors.