Equation recovery

Area: definition. Apply the shared measurement protocol.

Estimated progress toward AI-managed physics

0% 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 0 + 0 + 0 + 0 + 0 = 0/100, 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: The quantified equation-recovery benchmark predates 2022, so it anchors the starting point. This outcome's baseline does not establish a comparable post-anchor increment. The dated baseline below supplies the findings and source links used for this assessment. A 0% score here means no qualifying post-2022 advance is established by this file's evidence under the rubric; it is not proof that the capability is absent or that the entire field is at 0%.

What would move the score: Freeze fresh systems, noise and missing-variable levels; compare equivalent equations under identical time/compute budgets and report all failures. 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

Correct models / all held-out benchmark systems, with equivalent forms recognized and noise, missing variables, and budgets specified.

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: Known-law recovery benchmark. 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

April 15, 2020 revised preprint: AI Feynman recovered all 100 equations in its Feynman benchmark, versus 71 for the comparison method. AI Feynman.

Interpretation and tracking value

This historical benchmark is not a current universal success rate under realistic missing variables and noise. Algebraic equivalence and benchmark familiarity matter.

Next comparable measurement

Freeze fresh systems, noise and missing-variable levels; compare equivalent equations under identical time/compute budgets and report all failures. Preserve this dated observation; append future results with their model/system version, denominator, budget and evidence level rather than overwriting it.