Fraction of recent frontier AI capability progress attributable to algorithmic improvements rather than compute scaling
Reassessed
Completed the definition (log effective training compute, frontier LLMs, 2014-2025, data counted with compute, inference excluded). Recorded Epoch's Ho et al. 5-40% and the Gundlach et al. decade passage; the 0.36 beside the latter is derived arithmetic, as the work notes. Added two arguments, both rated likely. Estimate: central 0.35, 90% interval 0.15-0.55. No dependents to notify.
Claim proposal accepted
The proposal is a noun phrase that asks for a value (a fraction), so it reads as a quantity, not a proposition. The Matcher settles the kind, and the proposal should not be rejected for arriving as the wrong kind. It is about the world and not about a private person. It is a live, much-discussed topic in AI progress research, where people attribute progress to algorithmic gains versus compute scaling. The wording is workable as a starting point. The Steward will need to complete the definition, since "recent", "frontier", "capability progress", and the attribution method (for example, effective-compute decomposition versus counterfactual ablation) are left open. Those choices are the Steward's work after admission. The contributor did not give sources or reasons, and a proposal does not require them. Admitted on form, good faith, and the claim bar.
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Claim entered the graph
Claim proposal submitted