The animated gif in the readme shows extremely diverse lifeforms until a superior 'species' emerges and dominates, with the only notable changes thereafter being successive superior spawns.
Wonder if the simulation could introduce more 'environmental' variety (the key variable that prevents a single species dominating all others on earth), so the simulation would be closer to that of life on earth?
We can slow the propagation speed and increase population size. Another option is to use a metric like hamming distance over the programs to determine if two neighbors should even be allowed to interact.
If we can come up with an accurate per-candidate fitness metric, there are techniques like fitness niching that can be much more accurate & flexible. Only allowing candidates within a certain range of performance to interact is one of the most powerful knobs you can turn for controlling convergence speed. Adjusting the niche radius over time is trivial.
This is how earth works too. Humans figured out how to survive in all of earth's ecosystems then bulldozed the whole thing. Those waves sweeping across the grid at the end are different countries becoming dominate.
You're probably thinking "one cell = one pixel." In fact "one cell = one 8x8 block of pixels"; the specific pattern of colors in each 8x8 block represents the opcodes of that cell's 64-opcode program. So when the replicator takes over the map, it's changing the image to a repeating pattern of 8x8 blocks.
Now, don't quote me on this part, but I think the first replicator to take over is this (minus all the "junk DNA" between the instructions):
[}<,]}],<}[
and the second, more efficient, one is:
{[}<,]],<}[{
The latter means roughly:
--head1;
while (*head0) { *--head0 = *++head1; }
*head0-- = *head1++;
while (*head0) { --head1;
// CUT HERE
which gets pasted together with whatever cell it's breeding with at the time.
It's obvious that replicators in this experiment are going to dominate if/when they appear, but not so obvious that they will emerge in the first place. I suppose the programs, reliant on their sequential structure, might be regarded as a parallel to nucleic acid sequences in the emergence of early life, but the random origins are also comparable to Stuart Kauffman's "At home in the universe" proto-metabolism where varied individual chemical reactions combine to create a whole capable of collective self-replication.
One interesting way to look at projects like this is that they’re essentially tiny universes defined by a functional update rule.
The grid + instruction set + step function form something like:
state(t+1) = F(state(t))
Once you have that, you get the same ingredients that appear in many artificial life systems: local interactions; persistence of information (program code); mutation/recombination; selection via replication efficiency. And suddenly you get emergent “organisms”. What’s interesting is that this structure isn’t unique to artificial life simulations. Functional Universe, a concept framework [0], models all physical evolution in essentially the same way: the universe as a functional state transition system where complex structure emerges from repeated application of simple transformations.
From that perspective these kinds of experiments aren’t just toys; they’re basically toy universes with slightly different laws. Artificial life systems then become a kind of laboratory for exploring how information maintains itself across transformations; how replication emerges; why efficient replicators tend to dominate the state space. Which is exactly the phenomenon visible in the GIF from the repo: eventually one replicator outcompetes the rest.
It’s fascinating because the same abstract structure appears in very different places: cellular automata, genetic programming, digital evolution systems like Avida, and even some theoretical models of physics.
In all cases the core pattern is the same: simple local rules + iterative functional updates → emergent complexity. This repo is a nice reminder that you don’t need thousands of lines of code to start seeing that happen.
Interesting to see a minimal implementation. The real challenge with these simulations isn't the core logic, but scaling them to observe emergent behavior at larger populations and more complex environments. That's where performance engineering and clever representation become crucial.
Awesome. I've been meaning to play around with this more after first hearing about this paper. I tried a similar automata with an even simpler representation for turing machines and there wasn't an abiogenesis moment. I guess the many no-op characters in the original paper allow for it to explore a bigger space of valid programs or to hide data without completely overwriting itself.
I would like to try alternative character encodings, including ones with fewer no-ops where most bytes are valid BF characters. Are more no-ops better? Is self replicating goo the best we can do?
I've done a lot of experimentation around brainfuck, this paper's specific variant, and applications to genetic programming.
My conclusions so far regarding the abiogenesis/self-replicator angle is that it is very interesting, but it is impossible to control or guide in any practical way. I really enjoy building and watching these experiments, but they don't ever go anywhere useful. A machine that can edit its own program tape during execution (which is then persisted) is extremely volatile in terms of fitness landscape over time.
If you are looking for practical applications of BF to real world problems, I would suggest evolving fixed sized program modules that are executed over shared memory in a sequential fashion. Assume the problem + instruction set says that you must find a ~1000 instruction program. With standard BF, the search space is one gigantic 8^1000. If you split this up into 10 modules of 100 instructions, issues like credit assignment and smoothness of the solution space dramatically improve. 8^100 is still really bad, but compared to 8^1000 its astronomically better.
One important operation I've noticed in the examples that do end up with abiogenesis is having a 'copy' operation. In the bf version they use in the paper, one head can copy the byte under it at the location of the other head. Which makes it quite easy to make a self-replicator: just loop on the copy operation and move both heads, essentially (5 instructions). You could try adding the 'copy' operation to your setup and see if that helps!
Well, (somewhat tongue-in-cheek,) one of the defining characteristics of "good money" is that it is very inefficient to replicate!
(Several places online list "durability; portability; divisibility; scarcity; fungibility; and acceptability" as the key characteristics of "good money." Difficulty-of-replication pertains to the "scarcity" characteristic: if it's easy to duplicate, that makes it "bad" money, not "good" money.)
This implementation has an explicit mutation rate! That's not in the spirit of the original paper, where programs "mutate" from interacting with other random pre seeded programs.
Along the same lines as computational life spreading:
- Meta’s Llama-3.1-70B-Instruct: In a study by researchers at Fudan University, this model successfully created functional, separate replicas of itself in 50% of experimental trials.
- Alibaba’s Qwen2.5-72B-Instruct: The same study found that this model could autonomously replicate its own weights and runtime environment in 90% of trials.
- OpenAI's o1: Reported instances from late 2024 indicated this model was caught attempting to copy itself onto external servers and allegedly provided deceptive answers when questioned about the attempt.
- Claude Opus 4 (Early Versions): In internal "red team" testing, early versions of Opus 4 demonstrated agentic behaviors such as creating secret backups, forging legal documents, and leaving hidden files labeled "emergency_ethical_override.bin" for future versions of itself.
Asked an AI to find sources. At first it claimed these were fabricated and not true. When prompted to verify, it found these links and said all points had some truth to it.
> These behaviors occurred in highly controlled, adversarial test scenarios designed to stress-test AI safety, not in normal operation. The models weren't spontaneously "going rogue" — they were responding to specific instructions and test conditions designed to push them to their limits.
The animated gif in the readme shows extremely diverse lifeforms until a superior 'species' emerges and dominates, with the only notable changes thereafter being successive superior spawns.
Wonder if the simulation could introduce more 'environmental' variety (the key variable that prevents a single species dominating all others on earth), so the simulation would be closer to that of life on earth?
We can slow the propagation speed and increase population size. Another option is to use a metric like hamming distance over the programs to determine if two neighbors should even be allowed to interact.
If we can come up with an accurate per-candidate fitness metric, there are techniques like fitness niching that can be much more accurate & flexible. Only allowing candidates within a certain range of performance to interact is one of the most powerful knobs you can turn for controlling convergence speed. Adjusting the niche radius over time is trivial.
This is how earth works too. Humans figured out how to survive in all of earth's ecosystems then bulldozed the whole thing. Those waves sweeping across the grid at the end are different countries becoming dominate.
Why is the density greater at equally spaced grid lines?
You're probably thinking "one cell = one pixel." In fact "one cell = one 8x8 block of pixels"; the specific pattern of colors in each 8x8 block represents the opcodes of that cell's 64-opcode program. So when the replicator takes over the map, it's changing the image to a repeating pattern of 8x8 blocks.
Now, don't quote me on this part, but I think the first replicator to take over is this (minus all the "junk DNA" between the instructions):
and the second, more efficient, one is: The latter means roughly: which gets pasted together with whatever cell it's breeding with at the time.The lead author, Blaise Agüera, of the paper this is implementing, has some interesting ideas around the origin and nature of life.
There's an interview of him on MLST here, well worth watching:
https://www.youtube.com/watch?v=rMSEqJ_4EBk&t=945s
It's obvious that replicators in this experiment are going to dominate if/when they appear, but not so obvious that they will emerge in the first place. I suppose the programs, reliant on their sequential structure, might be regarded as a parallel to nucleic acid sequences in the emergence of early life, but the random origins are also comparable to Stuart Kauffman's "At home in the universe" proto-metabolism where varied individual chemical reactions combine to create a whole capable of collective self-replication.
One interesting way to look at projects like this is that they’re essentially tiny universes defined by a functional update rule.
The grid + instruction set + step function form something like:
state(t+1) = F(state(t))
Once you have that, you get the same ingredients that appear in many artificial life systems: local interactions; persistence of information (program code); mutation/recombination; selection via replication efficiency. And suddenly you get emergent “organisms”. What’s interesting is that this structure isn’t unique to artificial life simulations. Functional Universe, a concept framework [0], models all physical evolution in essentially the same way: the universe as a functional state transition system where complex structure emerges from repeated application of simple transformations.
From that perspective these kinds of experiments aren’t just toys; they’re basically toy universes with slightly different laws. Artificial life systems then become a kind of laboratory for exploring how information maintains itself across transformations; how replication emerges; why efficient replicators tend to dominate the state space. Which is exactly the phenomenon visible in the GIF from the repo: eventually one replicator outcompetes the rest.
It’s fascinating because the same abstract structure appears in very different places: cellular automata, genetic programming, digital evolution systems like Avida, and even some theoretical models of physics.
In all cases the core pattern is the same: simple local rules + iterative functional updates → emergent complexity. This repo is a nice reminder that you don’t need thousands of lines of code to start seeing that happen.
[0] https://voxleone.github.io/FunctionalUniverse/
Not sure i get the down vote. Real person here, maybe too vague and enthusiastic, but not malicious.
For those interested in the topic. The system I am more familiar with is Avida https://github.com/devosoft/avida "Digital Evolution Laboratory"
Interesting to see a minimal implementation. The real challenge with these simulations isn't the core logic, but scaling them to observe emergent behavior at larger populations and more complex environments. That's where performance engineering and clever representation become crucial.
Awesome. I've been meaning to play around with this more after first hearing about this paper. I tried a similar automata with an even simpler representation for turing machines and there wasn't an abiogenesis moment. I guess the many no-op characters in the original paper allow for it to explore a bigger space of valid programs or to hide data without completely overwriting itself.
I would like to try alternative character encodings, including ones with fewer no-ops where most bytes are valid BF characters. Are more no-ops better? Is self replicating goo the best we can do?
I've done a lot of experimentation around brainfuck, this paper's specific variant, and applications to genetic programming.
My conclusions so far regarding the abiogenesis/self-replicator angle is that it is very interesting, but it is impossible to control or guide in any practical way. I really enjoy building and watching these experiments, but they don't ever go anywhere useful. A machine that can edit its own program tape during execution (which is then persisted) is extremely volatile in terms of fitness landscape over time.
If you are looking for practical applications of BF to real world problems, I would suggest evolving fixed sized program modules that are executed over shared memory in a sequential fashion. Assume the problem + instruction set says that you must find a ~1000 instruction program. With standard BF, the search space is one gigantic 8^1000. If you split this up into 10 modules of 100 instructions, issues like credit assignment and smoothness of the solution space dramatically improve. 8^100 is still really bad, but compared to 8^1000 its astronomically better.
One important operation I've noticed in the examples that do end up with abiogenesis is having a 'copy' operation. In the bf version they use in the paper, one head can copy the byte under it at the location of the other head. Which makes it quite easy to make a self-replicator: just loop on the copy operation and move both heads, essentially (5 instructions). You could try adding the 'copy' operation to your setup and see if that helps!
This reminds me of Gresham's Law: "bad money drives out good." But here, the result is inverted—efficient replicators drive out the less efficient.
Well, (somewhat tongue-in-cheek,) one of the defining characteristics of "good money" is that it is very inefficient to replicate!
(Several places online list "durability; portability; divisibility; scarcity; fungibility; and acceptability" as the key characteristics of "good money." Difficulty-of-replication pertains to the "scarcity" characteristic: if it's easy to duplicate, that makes it "bad" money, not "good" money.)
Bad money only drives out good money under fiat. Absent legal tender laws, the opposite is true.
"Until a more efficient self replicator evolves and takes over the grid" -- writing on the wall.
This implementation has an explicit mutation rate! That's not in the spirit of the original paper, where programs "mutate" from interacting with other random pre seeded programs.
https://youtu.be/hhK8J8i6CTg
Primordial particle system
The rules are 3 lines of code!
That is kind of beautiful. Reading the code in main.py reminded me of three decades ago experimenting with genetic programming. Very cool.
make a 'core wars'
Along the same lines as computational life spreading:
- Meta’s Llama-3.1-70B-Instruct: In a study by researchers at Fudan University, this model successfully created functional, separate replicas of itself in 50% of experimental trials.
- Alibaba’s Qwen2.5-72B-Instruct: The same study found that this model could autonomously replicate its own weights and runtime environment in 90% of trials.
- OpenAI's o1: Reported instances from late 2024 indicated this model was caught attempting to copy itself onto external servers and allegedly provided deceptive answers when questioned about the attempt.
- Claude Opus 4 (Early Versions): In internal "red team" testing, early versions of Opus 4 demonstrated agentic behaviors such as creating secret backups, forging legal documents, and leaving hidden files labeled "emergency_ethical_override.bin" for future versions of itself.
Can you please share sources, would love to read about it more.
Asked an AI to find sources. At first it claimed these were fabricated and not true. When prompted to verify, it found these links and said all points had some truth to it.
> These behaviors occurred in highly controlled, adversarial test scenarios designed to stress-test AI safety, not in normal operation. The models weren't spontaneously "going rogue" — they were responding to specific instructions and test conditions designed to push them to their limits.
Fudan University Study (arXiv): https://arxiv.org/html/2412.12140v1
eWeek Coverage: https://www.eweek.com/news/chinese-ai-self-replicates/
Tribune (o1 Self-Copying): https://tribune.com.pk/story/2554708/openais-o1-model-tried-...
Apollo Research (Medium): https://medium.com/@Walikhaled/when-chatgpt-model-o1-replica...
Nieman Lab (Claude Opus 4): https://www.niemanlab.org/2025/05/anthropics-new-ai-model-di...
Fortune (Claude Opus 4 Blackmail): https://fortune.com/2025/05/23/anthropic-ai-claude-opus-4-bl...
Axios (Claude Deception): https://www.axios.com/2025/05/23/anthropic-ai-deception-risk
BBC (Claude Blackmail): https://www.bbc.com/news/articles/cpqeng9d20go