Successive improvement

Area: definition. Apply the shared measurement protocol.

Estimated progress toward AI-managed ai research

25% 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 5 + 5 + 5 + 5 + 5 = 25/100, 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: Evidence of feedback into research is suggestive, but it does not isolate an independently validated sequence of AI-caused improvements across generations. 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: Evaluate at least three successive generations on fresh frozen research tasks, with and without inherited AI-produced improvements. 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

Change in the next system's ability to perform fresh AI research tasks caused by the previous system's contributions. Measure each generation, not just the final model.

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: Feedback evidence; causal generation series unknown. 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 5, 2026: METR found substantial recent research acceleration, while emphasizing uncertainty in attribution and future continuation. METR: Anthropic AI R&D acceleration assessment.

Interpretation and tracking value

An organization-level trend does not isolate how each system's contribution improves the next system's research ability. No qualifying multi-generation causal series is established by this assessment.

Next comparable measurement

Evaluate at least three successive generations on fresh frozen research tasks, with and without inherited AI-produced improvements. Preserve this dated observation; append future results with their model/system version, denominator, budget and evidence level rather than overwriting it.