We gave a population of simulated bots a way to pass traits to their offspring. The faster ones prospered, and their descendants took over.

Then, across tens of millions of simulation steps, this evolutionary drama turned fairly uneventful.

That plateau is the most interesting result so far from Biobot Tierra, an artificial-life experiment at Mind37. We set out to see how replicating biobot machines might accumulate useful changes across generations. What we found was a system very good at spending its initial inheritance, and with little evidence that it earned anything new.

A body that builds a body

The inspiration is the idea of xenobots from the incredible research of Michael Levin et al. out of Tufts University. Xenobots are tiny living constructs made from frog embryo cells, which swim using microscopic beating hairs called cilia. In 2021, researchers showed that swimming xenobots could sweep loose cells into piles that matured into new moving bodies, a process called kinematic self-replication. A body assembles another body from the material around it. That is an extraordinary thing to watch.

But a successful shape does not survive the handoff. The C-shaped, Pac-Man-like xenobots in those experiments replicated better than spheres, yet their offspring still rounded up into spheres. The parent's engineered geometry was lost between generations.

Why anyone wants living machines

Xenobots are interesting because they can do jobs that are hard for metal and plastic robots. They are built from ordinary cells, so they need no foreign materials, run on their own stored energy, and break down harmlessly when they die after a few days or weeks. Researchers have proposed using bots like these to:

  • collect pollutants, such as microplastics, from water
  • deliver drugs to specific places in the body
  • sense and record their surroundings
  • repair tissue, possibly using bots grown from a patient's own cells

The early lab demonstrations point in those directions. Xenobots have pushed particles into piles, and one version carried a protein that permanently changes color when exposed to a particular wavelength of light, a simple record of what it had been exposed to. In 2023, a related system called anthrobots, grown from human airway cells, helped a scratched layer of human neurons grow back across the gap in a dish.

These are early results in dishes, not products. They do share a property that is important for everything that follows: none of these bots pass their traits on.

That missing inheritance is consequential though. Darwinian evolution needs three things: variation, differences in how many offspring individuals leave, and heredity. Without heredity there is no natural selection. With Biobot Tierra we ask what would happen if something useful did carry over.

The dish

Our bots live in a small digital dish: a 128 × 128 lattice of loose cells. They move, push cells into piles, and after a fixed lifespan fall apart, returning their material to circulation. A pile that is large enough, compact, and left alone for a few simulated seconds hatches into a new bot.

Each bot carries five traits: mouth shape, size, speed, turning bias, and a color marker. Each trait is a whole number from 0 to 255, decoded into the body. Speed, for example, maps a gene g onto a velocity:

v = 40 + 60 · g / 255   (in 1/256 of a lattice site per step)

so the slowest bot covers about 3 sites per simulated second and the fastest about 8. The marker does nothing at all, which makes it useful. It lets us follow lineages, and because it has no effect, how far it drifts shows how much change chance alone produces.

Nobody stands above the dish handing out offspring to the winners. The movement and hatching rules are programmed, but reproduction follows from what bots actually do. A bot has to take part in the world for its traits to get another turn.

We built four versions of that world, differing only in how offspring inherit:

  • Control: offspring hatch as default spheres with random behavior, as in the lab.
  • Mold: offspring copy the shape of the bot that built most of their pile.
  • Imprint: offspring copy the behavior of the last bot to work on their pile.
  • Carry-over: offspring take the average traits of the cells their body is made from.

All four mechanisms are invented. None has been seen in xenobots. We are asking what their consequences would be in a model, not claiming to have found them in living tissue.

Whatever is inherited can also mutate. Each copied trait gets a small random nudge, then is clipped to the allowed range:

z_offspring = clip( z_parent + ε, 0, 255 ),   ε ≈ Normal(0, m²)

where m, the mutation size, was 4, 16 or 48. The nudge is built from four random bytes added together. By the central limit theorem their sum is already close to a bell curve, which keeps the model in exact integer arithmetic.

The first study

We ran 176 dishes for 200,000 steps each: 35.2 million simulation steps. Every dish started with eight founders and 40,000 loose cells. Every birth and death went into the record, and each dish was measured against its own founders. The model is exact and deterministic, so any run can be replayed birth for birth.

The basic measure of inheritance is heritability: fit a straight line through offspring traits plotted against parent traits, and take its slope.

b = Cov(z_offspring, z_parent) / Var(z_parent)

A slope of 1 is a perfect copy; 0 means nothing is passed on. Because our bots have a single parent, this slope estimates the heritability h² directly. The control is the sanity check: its slopes came out at 0.00–0.02, as they should.

Three lessons emerged.

Copying shape worked as copying, not as improvement. Mold passed shape down almost perfectly (a heritability above 0.8), but shape barely affected how many piles a bot built in this simulation. Faithful copies added only drift and harmful mutations, and mold dishes did worse than the control. That says as much about the model as about inheritance: our coarse lattice does not reproduce the advantage C-shaped bodies have in the lab. We searched 16 arena settings for one that would restore it. The best looked promising, then failed on fresh random seeds. It had been noise.

Blending removed variation, not information. Carry-over offspring closely tracked the average traits of their cells: the slope against the pile's average was 0.90–1.01 per trait at the smallest mutation size. But averages of mixed cells are all much alike. That is the familiar rule that averaging shrinks spread.

Among the founders, a trait's standard deviation was about 68. Among pile averages it was 7–15. Since what selection can do scales with variance, not standard deviation, that is a loss of roughly 20- to 90-fold (68²/15² ≈ 20, 68²/7² ≈ 94). The system kept transmitting information while erasing the differences that selection needs. Fleeming Jenkin raised this objection to Darwin in 1867.

Copying behavior was the promising case. Faster bots worked more piles, so their behavior was copied more often, and the population sped up. Most of that rise came in the first generation of offspring.

And then the line went flat.

Investigating the flat line

So what was the possible explanation of the plateau?

The first dishes had a problem: populations fell to about two bots in every version, including the control. We traced the material. A dead bot's cells formed a compact pile that hatched again as a larger bot, so more and more of the dish ended up in fewer and fewer bodies.

We changed the research version so a newborn took a fixed amount of material and left the rest loose. With 100,000 cells and longer lives, the dish held about 39 bots steadily.

The plateau survived.

We also went back to the first study's records to see what drove the early rise, using the Price equation. It splits the change in a trait's average from one generation to the next into two exact parts:

Δz̄ = Cov(w, z) / w̄  +  E(w · Δz) / w̄
       selection         transmission

Here z is a parent's trait, w is how many offspring it left, w̄ is the average of w, and Δz is how far its offspring's traits differ from its own. The first term is selection: does the trait go with having more offspring? The second is transmission: do offspring drift away from their parents, through mutation or imperfect copying?

For the founders' offspring under imprint (mutation size 16), the numbers were:

Δz̄ = +37.2 ± 15.5   =   selection +39.4 ± 14.6   +   transmission −2.2 ± 3.4

Almost all of the rise was selection: faster founders left more offspring. For every later group of births, the total change was −0.0 ± 0.5. It was essentially zero.

Who is the parent?

The next question sounds bureaucratic until it changes the result: who, exactly, is the parent?

The bot that builds a pile is often not the last one to touch it. In about two-thirds of births they were different bots, and under the original imprint rule it was the last toucher whose behavior was copied. Building the next generation and being copied into it were two separate accomplishments.

When offspring copied the builder instead, the response was stronger: newborn speed rose about 75 points above the founders (on a 0–255 scale), against about 45 under the last-toucher rule. Even our accounting of what caused the change depended on which bot we called the parent. Both terms of the Price equation are defined parent by parent. Read the same births through the builder instead of the last toucher, and the change shows up mostly as transmission rather than selection. The math is exact; the ancestry you feed it is a choice.

A real advantage, and a limited one

Was the change real? We put eight late-run bots into a fresh dish with eight bots resembling the founders. The late bots claimed about 60% of the offspring, and 58% in a rerun.

With no advantage, the share would be 0.5. The rerun's share of 0.585, with a standard error of 0.022, sits almost four standard errors above that:

t = (0.585 − 0.500) / 0.022 ≈ 3.9

We could also name the cause. When the founder-like bots were given the late bots' speed, the late bots' share fell to 0.46 ± 0.04, back to chance.

But the adapted bots had not made the dish more productive. A dish of adapted bots alone produced about as many offspring as a dish of founder-like bots alone. They had become better at claiming a share of reproduction without increasing the total.

The population had evolved a competitive advantage. The collective output had barely moved.

Turning off mutation

Then we removed mutation entirely.

The initial improvement still happened, about as large as with mutation, and in every dish one founder's speed took over. The population got to its eventual performance using only the variation it started with.

Selection was doing its job: useful starting variation spread. What we could not find was a continuing supply of new improvements built on top of it.

Classical quantitative genetics gives a simple way to see why. The breeder's equation says the response to selection in one generation is

R = h² · S

the heritability times the selection differential S, which is how much the parents who reproduce differ from the population average. S in turn is proportional to how much variation there is. Once one founder's speed has taken over, there is almost no variation left, so S, and with it R, falls toward zero. Mutation adds variation back, but random nudges to an already-favored speed rarely improve it.

At the largest mutation size, mutation also pushed speeds into the clipped ends of the 0–255 range, and selection and mutation settled into a balance instead of a steady climb.

Trying to break the conclusion

We fixed the tests, thresholds and conditions in advance, then ran 16 fresh random seeds, with eight more for replication. Every condition was paired seed by seed with a control dish, and a test passed only at t ≥ 2.6. That bar corresponds to about a one-sided p < 0.05 after a Bonferroni correction for four tests: dividing the 5% false-alarm budget among them.

The competitive advantage passed. The tests for continuing improvement did not. The slope of newborn speed from the second to the fifth tenth of the run, compared with the control, came out at +2.7 ± 3.4 and +2.8 ± 3.0 for the two copying rules. Both are smaller than their own uncertainty.

We then mapped mutation rates, cell supplies and a cost for speed across another 360 dishes: 72 million more simulation steps, before counting the competition trials. The cost shortened a bot's lifespan in proportion to its speed gene:

lifespan = L · (1 − c · g / 255)

where L is its normal lifespan and c is 0, 0.25, 0.5 or 0.75, so at the highest cost the fastest bots lived a quarter as long.

In every condition with a reasonable population and no more than a modest cost for speed, the early shift appeared, at about the same size. A cost of 0.5 halved it, and 0.75 reversed it. Sustained change passed in only one of 40 conditions. With eight seeds per condition, a test with no real effect still clears t ≥ 2.6 about 1.8% of the time, so about 0.7 false passes are expected across 40 conditions. A single success is about what luck delivers, not evidence of a general effect.

We also tried a back-of-the-envelope predictor, the product N × s × h: population size, the strength of selection on speed, and heritability. It tracked the cost of speed closely (correlation 0.95) but not mutation (−0.25). Mutation inflates the spread of speeds, and s is measured in units of that spread, so it hides how hard selection is really pushing.

The bots adapted. Over the roughly 75 generations we studied, we found no convincing evidence that improvement kept accumulating.

Why a plateau is the interesting result

I find that more compelling than an endlessly rising graph would have been.

It forces us to be precise about what an adaptive system is. A population can reproduce, inherit, undergo selection and become measurably better at competing, all while making no further progress once its initial advantages have spread. A rising curve can be the sorting of variation that was there from the start. Showing continuing innovation takes more evidence than that.

It also points to better questions. What happens when the environment changes? When one useful trait opens access to another? When improving a trait keeps paying off instead of running into a limit? When building, inheriting and reproducing are more tightly linked, or less?

Biobot Tierra makes those questions concrete. We can change a rule, replay a history, inspect every birth, and find out that the explanation we liked was wrong. Several times in this study, we did.

What this does not tell us

So there are limits to what we found. The bots are simple 2D agents on a lattice. Their inheritance mechanisms are hypothetical. Piles hatch in seconds rather than days, populations top out around 39 bots, and runs end after about 75 generations. A plateau here cannot show that real xenobots would stop evolving, or that an evolving biological system would be safe. Any biological work in this direction would need its own scientific and ethical assessment.

What we have is a working experiment at the boundary between replication and continuing adaptation, and a sharper question: what lets an adaptive system keep finding useful changes after its first advantages have spread?

We gave the bots inheritance. They made good use of it.

Now we want to understand what would give them somewhere else to go.

Explore Biobot Tierra · Read the methods and results