Research task performance

Area: definition. Apply the shared measurement protocol.

Estimated progress toward AI-managed ai research

40% complete — judgment-based estimate as of November 7, 2026. Confidence: low. The scale starts at 0% before ChatGPT's public release in 2022 and ends at 100% when AI manages the field's continuing advancement without human participation. This rates the outcome's contribution to that transition, not the percentage of tasks solved, a measured fraction of the field, or time remaining.

The shared scoring rubric uses five equally weighted dimensions: execution, breadth, agenda-setting, validation, and sustained autonomous improvement. Points for this outcome are 10 + 10 + 5 + 10 + 5 = 40/100, in that order; each dimension has a maximum of 20. These component judgments make the estimate inspectable, but are not independently measured scores.

Reason for the estimate: Multiple bounded research-engineering tasks support repeatable execution and objective checking. Budget-sensitive results and expert-selected tasks limit agenda and long-run autonomy credit. The dated baseline below supplies the findings and source links used for this assessment. Unmeasured components receive no demonstrated-progress credit; missing evidence does not prove a missing capability.

What would move the score: Rerun fresh tasks with identical compute/data and 2-, 8- and 32-hour budgets; report task-level scores and uncertainty. Higher autonomy scores additionally require AI to select worthwhile work, validate results and improve the process over repeated cycles without human research management, repair or adjudication. Lower the score if replication fails, quality regresses or previously hidden human assistance is found. Preserve this dated estimate when adding future assessments.

Measurement and acceptance rule

Held-out objective improvement on fresh research tasks at fixed time, compute, and data budgets, relative to expert researchers and automated-search baselines.

Evidence requirements

Freeze private evaluations outside the research agent's control. Publish ablations comparing the same development pipeline with and without the AI-produced change. Control for additional compute, new data, expert interventions, and evaluator changes. Distinguish improving a narrow kernel from improving general research ability. Do not allow an agent to redefine its own success metric.

Related milestones

See the area's proposed milestones. Proposed targets are distinct from the dated evidence below.

Baseline as of November 7 2026

Status: Measured benchmark comparison. Sources accessed November 7, 2026. This is a documentary baseline, not an independent replication or an assertion of the worldwide best result. An unknown measurement is not zero.

Dated findings

November 22, 2024; revised May 27, 2025: RE-Bench includes seven research-engineering tasks and 71 eight-hour expert attempts. Agents scored about four times humans at two-hour budgets; humans outscored agents about twofold at 32 hours. RE-Bench research paper.

Interpretation and tracking value

Results depend strongly on budget and task selection. They measure bounded engineering tasks, not the full process of discovering and validating research directions.

Next comparable measurement

Rerun fresh tasks with identical compute/data and 2-, 8- and 32-hour budgets; report task-level scores and uncertainty. Preserve this dated observation; append future results with their model/system version, denominator, budget and evidence level rather than overwriting it.