Novel prediction

Area: definition. Apply the shared measurement protocol.

Estimated progress toward AI-managed physics

20% 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 + 0 = 20/100, 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: Simulation-based hypothesis generation is a precursor, but predictions novel to physics and registered before new physical observations are not established. 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: Timestamp discriminating quantitative predictions before collection; identify strongest alternative models and preregister rejection thresholds. 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

Quantitative predictions registered before data collection that distinguish the proposed model from the strongest established alternatives.

Evidence requirements

Release equations, assumptions, units, code, and data needed to reproduce the result. Verify dimensional consistency and applicable physical constraints. Test outside the fitted range when scientifically appropriate. Use blind analysis and correction for multiple hypothesis searches. Mathematical derivations can use formal proof where practical, but formal correctness does not substitute for empirical validation of physical assumptions.

Related milestones

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

Baseline as of November 7 2026

Status: Hypothesis generation in simulation; prospective novelty baseline 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

September 29, 2025 preprint; 2026 journal publication: SciExplorer demonstrates exploration of systems initially unknown to the agent. SciExplorer.

Interpretation and tracking value

Unknown to an agent does not mean unknown to physics. A model fitted to simulator outputs does not establish a registered new prediction tested on future physical data.

Next comparable measurement

Timestamp discriminating quantitative predictions before collection; identify strongest alternative models and preregister rejection thresholds. Preserve this dated observation; append future results with their model/system version, denominator, budget and evidence level rather than overwriting it.