COMPASS: mapping where life-like behaviour appears in stochastic models, with an ordered, non-repeating active search
Year: 2026
Type: preprint
Abstract: Models of the origin of life are stochastic, slow and depend on many conditions. The useful question is oftennot what one run does, but where in condition space a level of life-like behaviour appears at all. That questionasks for a boundary. We present COMPASS (Condition-space Ordered Mapping by Prioritised Active Samplingof Stochastic systems). It tests conditions in an ordered, never-repeating sequence, simulates each conditiononce, fits a shared neural map of the success probability, and spends most of its budget where a bootstrapensemble of maps disagrees. None of the parts is new. Our contribution is the combination for this question, abenchmark protocol in which all methods share one map-maker and are scored against brute-force ground truth,and a record of what did not work.On six models, from a toy protocell to a fully bottom-up chemistry with 29 dimensions, COMPASS needed1.6–8× fewer simulations than Sobol or random sampling for the same map accuracy. The saving held at 20–30dimensions (2.8–4.0×), and the engine's own cost did not grow with dimension. On autocatalytic sets, thethreshold read off a COMPASS map lay within 7% of direct simulation. Ablations show that the saving comesfrom the loop as a whole: a standard boundary rule (straddle) does as well as our scoring. On a 54-dimensionalbottom-up model, COMPASS found and confirmed environments for levels that a ground-truth sample neverhit. But it does not raise hits per simulation uniformly, and most of its apparent advantage in raw hit countscomes from the design of the comparison.
Author: Wahiro, Gokyona
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225018897