Microcosmos was validated across four experiments spanning hand-designed control to fully emergent behavior, exercising both gradient descent and neuroevolution on one substrate

artificial-lifeneuroevolutionquality-diversitymap-elitesneatdifferentiable-physicsopen-endedness

The paper behind Microcosmos (arXiv:2607.02954) exercises its engine (claim-microcosmos-elastic-filaments-differentiable-gpu-fluid) across four experiments arranged along a spectrum from fully specified control to fully emergent behavior:

1. Hand-designed locomotion across Reynolds numbers from ~10³ down to ~1, which matched Purcell's scallop theorem — a physics sanity check that the fluid coupling behaves correctly at low Reynolds number.
2. Gradient-based folding of a 1000-node filament into MNIST digit shapes, a demonstration that the simulation is differentiable end to end (the same property that lets INR models be optimized by gradient descent — claim-siren-2020-implicit-neural-representations-signals-as-functions).
3. NEAT / CPPN neuroevolution, which produced sinusoidal swimming under a displacement-only objective, and chemotaxis once a second run added an energy-collection term to the fitness function — evolutionary rather than gradient search on the identical substrate. The chemotaxis is fitness-rewarded, not spontaneously emergent under a neutral objective (see Correction history below).
4. MAP-Elites quality-diversity search (Mouret & Clune, 2015), which discovered a range of gaits along bending/stretching behavioral axes, reaching 399 of 400 archive cells by generation 300.

The intellectually load-bearing point is that experiments 2 and 3–4 run on the same differentiable engine: gradient descent and open-ended evolutionary search (NEAT, MAP-Elites) share one physics substrate. This is the vault's first note touching the Kenneth Stanley open-endedness tooling lineage; the existing neuroevolution-adjacent material is backpropagation and automatic-differentiation history (backpropagation-gap), not evolutionary or quality-diversity search, so this note is a seed for a new cluster.

The four results were confirmed verbatim against the primary by the 2026-07-09 cross-model audit (see audit_status above) and independently reconfirmed with direct quotes by a 2026-08-20 capture, which also narrowed point 3 above (see Correction history). [unverified-quote] is resolved; the note stays seedling pending the verifier bee's mechanical pass, per the vault's standing audit_status convention.

Correction history.

• 2026-08-20 — Point 3 said "produced sinusoidal swimming and emergent chemotaxis." A direct read of the Methods section (claim-microcosmos-chemotaxis-is-fitness-rewarded-not-emergent, from 10-inbox/raw/2026-08-20-do-microcosmoss-scaling-benchmark-linear-to-500k-particles.md) shows the chemotaxis is fitness-rewarded: a second run changes the objective to "the total displacement traveled plus the total energy collected, rewarding both movement and chemotaxis," and only then does chemotaxis appear. The baseline, displacement-only run produces sinusoidal swimming with no food-seeking. Point 3 above is updated accordingly; the underlying claim (NEAT/CPPN neuroevolution validates the engine) stands, narrowed on the mechanism.
• 2026-09-12 (propagation-repair) — the closing paragraph said this note is the vault's first touching the "Kenneth Stanley / Sakana AI open-endedness tooling lineage"; it now reads "Kenneth Stanley open-endedness tooling lineage." Propagates the 2026-09-11 correction on claim-microcosmos-elastic-filaments-differentiable-gpu-fluid: per the paper's byline the lead affiliation is the Artificial Life Institute, Japan (Tensen, Regan, Chan, Oka, Szep), Sakana AI is Regan's second affiliation only, and Stanley's is Lila Sciences, USA — so the Stanley/Sakana pairing overstates. The open-endedness reading via Stanley (a co-author) stands; the four experiments and every other statement in this note are untouched.