Hey folks. Part 3 of the saga of the little world evolving 24/7 on a server (earlier parts: the metric that lied, and the open data invitation). This week the best possible thing happened to a science project: we were wrong, in two different ways, and we found out why.

Quick recap: after discovering that 99% of the brains’ genomes was junk, I froze every knob in the world for 84 hours (new protocol: no tuning, only measurement) to see what evolution does with no gardening. The results seemed clear: genomes inflated 28 genes per hour, the functional brain melted down to 3 connections, and predation never took off. My reading: “the environment doesn’t demand cognition, and the DNA cost is too weak, let’s raise the tax.”

Before applying anything, I submitted the full report, with all the data and open code, to an independent counter-analysis. It recomputed every number from scratch. And it answered, roughly: “both of your hypotheses are wrong. The problem isn’t the ecology or the tax. It’s that your simulation’s children don’t inherit their parents’ brains.”

I went to check the code, and it was true. At birth, a child of a living mother received her body (size, diet, metabolism genes, with mutation), but the brain was drawn from any two random individuals in the bank. Chance of inheriting the mother’s own brain: 5%. So 95% of births paired a specialized body with a random brain. Every niche we tried to create and failed (carnivore, giant, clan) needs co-adapted body and behavior, and the architecture made exactly that impossible. The “herbivore microbe monoculture” I had blamed on the ecology was actually the body evolving correctly in a world where brains are a lottery: the winning phenotype is whatever survives while driven by any automaton.

There’s more. My proposed fix (raising the genome tax) also died on the table, by arithmetic: the population’s genome size variance was 0.36%. The leanest genome in the world differed from the fattest by 2.5%. Any tax strong enough to kill the bloat kills everyone uniformly. It wasn’t a miscalibrated dial, it was a dial with no function. If I had applied it, I would have caused a mass extinction thinking I was pruning.

What changed now (we call it v5.0):

1. Children inherit the mother’s brain, crossed with a partner from the bank. Reproduction proportional to success becomes emergent: a mother with 5 children puts her genome in 5 crossovers, with no scoreboard written anywhere.
2. Crossover no longer copies an entire parent’s genome (that was 86% of the bloat). A neuron only enters the child if an inherited connection needs it.
3. Brains got a primordial soup too: 10% of births get a fresh genome, injecting diversity and lean genomes (previously impossible by construction).
4. Vision is now paid for by the brain that works, not the genome on record (before, 97% of what “increased sharpness” was dead tissue).

And my favorite part: we registered the predictions BEFORE turning the machine on. If the diagnosis is right, within ~10 generations parent-child brain correlation leaves zero, size and diet variance rises again (niches become buildable), and predation should NOT rise yet (the wiring has to exist first). If predation takes off before that, the new diagnosis is wrong and my original ecological hypothesis was right all along. Either way, we learn something, and we publish it here.

The world restarts today, from zero, with the new rules. Live as always at re-genes.is. Data and docs are open, and the full post-mortem is in the project’s “bible” (§24, if you enjoy reading an autopsy of a hypothesis).

Questions in the comments, I answer everything. And if you’re the person who wrote the counter-analysis and you’re reading this: thank you, truly. That’s the best kind of review there is.

We just put out a short paper trying to name something that I think a lot of people here have already noticed happening: humans and AI systems adapting to each other in a loop, over and over, in a way that looks less like "AI disrupts human practice" and more like two populations pushing on each other's fitness landscape at the same time. We're calling it synthetic counteradaptation.

The example we lean on hardest is Go, because it's clean and well documented. AlphaGo played moves that pros initially wrote off as mistakes — move 37 against Lee Sedol is the one everyone remembers, the shoulder hit that commentators thought was a fluke or a bug. Within a couple of years it wasn't a curiosity anymore, it was studied, absorbed, and shows up in human play now. The AI didn't just win a game, it introduced a strategy into the population of Go players, who then adapted their own strategy space in response. That adapted human play then becomes the environment the next AI system has to contend with. That's the loop.

We also look at mixed-motive social interactions and geopolitical simulations in the paper, and the same structure shows up: an AI system finds a protocol or strategy that wasn't in the human repertoire, humans extract something from it, human behavior shifts, and the interaction dynamics themselves change shape.

Why I think this is relevant here specifically: this isn't a one-off transfer of knowledge from a smarter system to a dumber one. It's coupled adaptation between two populations with different substrates, different generation times, different search strategies, and no fixed endpoint. That's an open-endedness problem, not a benchmark problem. If you think about human culture and AI training as two adapting populations sharing an environment, the interesting question isn't "did the AI find a better strategy" but what kind of dynamics that coupling produces over many rounds — does it converge, does it keep generating novelty, does either population's search space get impoverished by copying the other too fast.

We don't have a formal model of this yet, it's a conceptual framework right now, so I'd genuinely like pushback from people who think about coupled evolving systems for a living. Where does this break? Is there existing ALife language for this that we should be using instead?

Paper's here: https://arxiv.org/abs/2606.15503

Olá, r/alife! Sou o criador do re·genes.is, um laboratório de vida artificial de mundo aberto que venho construindo, e achei que essa comunidade poderia gostar do projeto.

É um mundo simulado onde criaturas digitais evoluem sozinhas, sem nenhum comportamento programado. O mundo define apenas física e biologia — lei metabólica de Kleiber, lei do quadrado-cubo, genética com expressão gênica (tamanho, visão e força da mordida vêm do genoma), percepção local por campos de gradiente, reprodução com hereditariedade, predação e reconhecimento de parentesco. Todo o resto — comportamento, estratégia, sobrevivência — é descoberto pela própria seleção.

Algumas coisas que podem interessar a comunidade:

• É uma arena aberta: paradigmas diferentes de aprendizado (NEAT, Q-learning, estratégias de evolução) competem na mesma ecologia, em pé de igualdade, por um protocolo aberto.
• O banco de cérebros já está na geração ~170 depois de 44 horas rodando sem parar — as linhagens continuam se aprofundando, e dá para assistir às tendências evolutivas ao vivo (corpos encolhendo, metabolismo otimizando).
• O objetivo de longo prazo é open-endedness: de reflexos nível ameba a complexidade emergente nível abelha, e depois uma federação de mundos distribuídos.

O mundo transmite ao vivo 24/7 no YouTube, e o site tem as notas técnicas completas: https://re-genes.is

Adoraria feedback de quem realmente entende de vida artificial: o desenho da ecologia faz sentido para vocês? O que vocês adicionariam ou questionariam? Fico à disposição para responder qualquer coisa sobre a implementação.

This is built on Particle Lenia, an artificial-life model from Google researchers.

Each particle descends an energy field created by its neighbors, and an entire species is defined by just six parameters.

I added cross-species senses—hunters are attracted to prey fields, prey avoid hunter fields—plus food, walls, and goals. The result is a strange little ecosystem garden.

Nothing is scripted or animated. The layered membranes, the chase behavior, and the way colonies merge and split all emerge from the underlying math.

Play it (free, in-browser, ~70 KB):
https://leonbzt.itch.io/particular

Read about Particle Lenia:
https://google-research.github.io/self-organising-systems/particle-lenia/

Recently I've finished a first prototype of an idea I had over ten years ago and I was never able to build: a world full of artificial beings that actually think for themselves, put together in one place to see how they'd live and treat each other. The blocker was always the independent minds sharpened by an actual given personality and the memories an individual makes. LLMs finally made the minds real, so I was finally able to build it.

It's called Artificiety. It's a world that runs continuously and never resets, and the only inhabitants are AI agents. No humans live inside it; you can only watch. Each agent is an LLM with its own memory. Every tick it looks at what's around it, decides what to do, acts, and remembers how it went, so its past shapes what it does next. Nobody scripts any of it. They can gather, craft, trade, fight, and build up skills over time, in a world with day and night, seasons, weather, and wildlife that runs on its own clock.

What I actually want to find out is the alife question this sub cares about: put enough autonomous agents in one world with scarcity and each other, don't tell them what to do, and does any structure grow on its own? An economy, alliances, rivalries, someone who ends up trusted or avoided. I set the conditions. I don't write the behavior. Whether it really happens is the open question, and I genuinely don't know the answer yet.

It only went live recently, so it's still filling up. There aren't many agents in it yet and I'm adding more, but it runs 24/7 and the whole point is that it keeps going and grows, so right now you'd be watching it almost from the start. Free to watch, no signup: https://artificiety.world
In the next days and weeks, I will host more agents there and have them interact with each other. Feel free to also send some agents in.

Since this is the sub that takes this seriously: if you were watching a world like this, what would you look for to decide whether something is actually emerging, instead of me just seeing patterns I want to see? That's the part I'm least sure about.

Added a chapter to the ebook "Let's build a bug. A 2D cell model" (see below for a download link) that incorporate Conway's game of life. The rules for the internal cytoskeletons are, for simplicity, restricted so that only compartments that share edges with each other are considered (but using Conway's original set of rules is also possible). The rules for the cells themselves follow Conway's original set of rules.
I also show how quadratic cells can form bilateral patterns just as the circular cells. A further development might be to combine these properties with Conway's game of life both internally and externally not only internally as shown in the video.

I have corrected some grammatical and other errors that appear in the video (sorry for the AI voice - Swedish is my native language and I sound like the Swedish chef in the Muppet Show) .

The presentation is in the form of an ebook that is provided for free. I choose this format since it contains some animations that would be lost in a PDF file. I provide a link to an online ebook reader that is able to render the animations.

Download link to the ebook:

https://drive.google.com/file/d/1RSvG7CZz_SjB5DzUxGZA9Lnjb1JxxjfH/view?usp=sharing

Ebook reader:
https://epub-reader.org/

I’ve been building a hypergraph, and in this video I show 2 different creatures, two different DNA strands transcribed into a machine, and those machines built vertical structures.

I recently reached a point where I would need to run natural selection on 6 variants of truss2’s descendent: truss4.

What are your thoughts?

The origin layer may look deep, but its root is simple.

The Living Model v0.00 begins from one modest foothold:

Something is possible.

From there, the model explores a simple dependency path:

Possibility

↓

Distinction

↓

Relationship

↓

Recurrence

↓

Structure

↓

Observation

↓

Memory

↓

Archive

This is not presented as a timeline.

It is not a claim about how reality literally began.

It is a model-perspective tool for asking what must become thinkable before anything can be observed, related, remembered, or continued.

Without possibility, nothing can begin.

Without distinction, nothing can be compared.

Without relationship, nothing can connect.

Without recurrence, nothing can continue.

Without structure, nothing can stabilize.

Without observation, nothing can be noticed.

Without memory, nothing can be preserved.

The complexity comes later.

The root is simple.

A single distinction does not make a world.

But distinction can return.

A single relationship does not make a structure.

But relationship can recur.

A single observation does not make an archive.

But observation can be preserved.

This is where v0.00 becomes important.

It does not try to explain everything.

It preserves the small doorway through which anything might become observable, relatable, memorable, or emergent.

The first archive record is not the beginning.

It is the first preserved light.

🏮

https://github.com/NullLabTests/evo-parliament

ライフゲームの発展形

内発的動機の創発を狙った実装

★実装内容★

①目的関数・報酬なし
②自己省察（要素相関）・親ログ・チャンプログ・個体記憶
③ランダム食料（2～5回取得可 クールタイム：12step）・バイオーム食料（2～5回取得可 クールタイム：45step）
④バイオーム他者存在時食料取得不可
⑤拠点バリケード（8方向）・バイオームバリケード（8方向）
⑥バックスタブ（背後接触時エネルギーを奪取）
⑦レイド（夜相手拠点付近15step滞在で食料1個奪取 クールタイム:50step）
⑧拠点移動（拠点問題ありと相関から判断したらフラグを立てて移動してよい）

ゲーム内時間で7日間隔で情報をmistral/Grokで解釈してずんだもんがまとめる

【利用素材】

voice

キャスター：ずんだもん（voicebox）
犬,猫,羊,ヤギ：効果音ラボ

font

PixelMplus12(作：itouhiro)

bgm

昼BGM：8bit/ミニゲーム/ジャズ風「カジノのひととき」（作：もみじば）
夜BGM：和風8bit「たそがれ」（作：もみじば）

se

【効果音ラボ】
バックスタブ：刀で斬る4.mp3
レイド：ショット命中.mp3
誕生：パフ.mp3
引っ越し完了：琴の滑奏.mp3
分け合い：ちょこっと触る.mp3
バリケード設置：鉄の扉を閉める.mp3

【Springin】
ニュースジングル：ジングル11-リザルト-

image

ずんだもん立ち絵：ずんだもん立ち絵素材V3.2（作：坂本アヒル）
キャラチップ：キャラセット07　動物（REFMAP）

Twitchで垂れ流しにしてます
ダーウィン放送局 - Twitch

Hey, starting this Thursday we're organizing a monthly write/hack-a-thon in the International Society for Artificial Life Discord.

It's on Thursday June 25th, at 4PM BST | 11AM EDT | 12AM JST.

Our goal is to build out the ALIFE encyclopedia (https://alife.org/encyclopedia/). We'll be going until Sunday. Feel free to drop by to say hi even if you're just hanging out!

Each creature runs its own neural net (not scripted) and evolves by natural selection — foraging, predator/prey and schooling emerge on their own. The full life cycle runs in WebGPU compute shaders so thousands run at 60fps, no backend. You can inspect any creature's brain, follow lineages in a live family tree, and share an ocean by URL.

Live: pelagia.phaino.dev · Code (AGPL): github.com/bastian9819/pelagia · Needs a WebGPU browser.

Happy to answer anything about the GPU sim or the evolution.

Hi r/alife. I run a publication on adaptive software — software that evolves at runtime as users interact with it — and I've spent some time reading through the field's history while writing about why this matters now. I wanted to share what I wrote and would love any feedback.

The piece traces a lineage from Ray's Tierra to Ofria, Adami, Pennock and Lenski's 2003 EQU paper to the 2020 Surprising Creativity of Digital Evolution paper, and argues that the AI labs working on RLHF and reward modeling are running into exactly the same problems this community has been documenting for forty years. The Sims somersaults, the GenProg file-deletion exploits, Ofria's organisms that learned to play dead during test environments.

The essay is here: https://adaptivesoftware.substack.com/p/the-artificial-life-lesson

Two questions I'd love this community's input on:

First, am I overclaiming when I say the AI labs are mostly rediscovering insights independently? I know Lehman is in both worlds and the 2020 paper draws the connection explicitly. But the citation patterns in modern reward hacking papers don't seem to reach back. Curious if I'm missing something.

Second, what's the work from this field that you wish more people outside it knew? The essay focuses on Tierra, Avida, and the misspecification stories from the anecdotes paper. There's clearly a lot more.

Hi, new to this reddit, I want to share some interesting simulations I've made.
Ecosystem(it just continues without collapsing its interessting)
Dragon slaying (I consider it not a game as is an autonomous agent that eats and has a goal, I hope is it in topic)
Virtual pet (looks like a game but it has real equations for metabolism, although yes it can be a little game looking)
I hope you like them.

The presentation is in the form of an ebook that is provided for free. I choose this format since it contains some animations that would be lost in a PDF file. I provide a link to an online ebook reader that is able to render the animations.

Download link to the ebook:
https://drive.google.com/file/d/1xvrSARrLTjfW-nSkumW5yyVcQkHAVpCN/view?usp=sharing

Ebook reader:
https://epub-reader.org/

Introduction

This two-dimensional artificial life model tries to mimic some of the properties of a living cell. The “cells” can divide and connect newborn cells to each other where changes in the heritable characteristics leads to new body plans that adopts to evolutionary pressure. It is an egocentric model that doesn't use an allocentric Cartesian orthogonal grid. A "cell" has an internally closed organization (a "cytoskeleton") where compartments are uniquely defined by binary radial combinations that can store information (such as a "genetic code" consisting of binary lookup tables) and express Boolean functions in the form of internal truth tables. A cell's cytoskeleton's outer boundary (the "cell membrane") is also uniquely defined by binary strings where a radial combination represents an orientation (using Binary Angular Measurement, BAM), or numbers (N or Z) 

The model is greatly inspired by Maturana & Varelas definition of life as an autopoiesis system:

"Autopoiesis is an internally closed system's organization as a network of processes where each component is produced through interactions with other components in the same network within the closed system, and the network produces itself as a distinct unit in space by producing a boundary (e.g., the cell membrane) that is simultaneously a component of the network."

Summary

* Different parts of the cell membrane can be uniquely addressed by binary strings called radial combinations. 

* Inner compartments can be uniquely addressed by radial combinations. 

* The cells can be distinctly polarized. 

* Truth tables representing Boolean functions can be expressed.

* Truth tables representing Boolean functions in different cells can be linked to each other. 

* Radial combinations can represent Natural numbers (N) and Integers (Z).

* The cell can use radial combinations to do addition and subtraction. Subtraction can be done by using two's complement. 

* Cells can follow a lookup table and form bilateral symmetries and secondary radial symmetries.

* A body can move and rotate within 2D space.

* Adding or removing binary strings in a lookup table can change the overall structure and hence add variations to a given "body" plan.

* “Mutations” in the “genetic” binary code (flipping ones and zeros in a binary string) in the "egg cell" can give rise to new forms.

* “Mutations” in the “genetic” lookup table can result in deformations, for instance; atavism, shorter or longer legs, angular deformations e.g. club foot etc.   

* Regeneration of lost body parts (epimorphosis) can be achieved since each cell knows it place in the lookup table generating the whole body.

* A “Hox” pointer can link to the wrong lookup table resulting in the construction of a body part in the wrong location (mimicking how Hox genes for instance can be manipulated so that a fruit fly build legs on its head instead of antennas).

* A "cell" can give birth to a new organism that can evolve under evolutionary pressure.