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.

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?

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.

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.

Can you please share sources, would love to read about it more.

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.

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.

Can you please share sources, would love to read about it more.

I've done a lot of experimentation around brainfuck, this paper's specific variant, and applications to genetic programming.

artificial-life

A simple (300 lines of code) reproduction of Computational Life: How Well-formed, Self-replicating Programs Emerge from Simple Interaction.

Program description

A 240x135 grid of 64 instruction-length Brainfuck-like programs are randomly initialized. Every iteration, neighboring programs are randomly paired, have their instruction tapes concattenated together, and are run for a maximum of $2^{13}$ steps. Once execution completes, the tapes are split back apart. The instructions are such that they can loop and mutate the instruction tapes (programs) themselves. As found in the paper, self-replicating programs that copy themselves over their neighbor's tape often spontaneously emerge, which soon spread to take over the entire grid.

Example simulation

Every pixel is an instruction; each instruction has a unique color, while black represents a value on the tape that is raw data storage / not an instruction. Every 8x8 section of pixels represents a single program.

uv run main.py --seed 1

In this run, a self-replicator emerges relatively early on into the run and soon takes over most of the grid, until a more efficient self-replicator evolves and takes over everything.

Example

Hacker Times

Hacker Times

Artificial-life: A simple (300 lines of code) reproduction of Computational Life

Discussion

Discussion

artificial-life

Program description

Example simulation