Thanks Zroe. Glad you found value in this.
Thank you for suggesting this. Using the model's uncertainty would be better than resampling, which was my intention. It could be a lot cheaper. I don't know the literature on LLM uncertainty quantification - do you know which methods are reliable?
Thank you! Done.
There is quite a gap between the academic models and this system. Most of the systems I've seen in the multi-agent system (MAS) alignment literature I've seen are either small and contrived or large and studied in an economic harness. Although my knowledge is limited; I have only been looking at MAS for ~4 months.
I agree, I was not hugely surprised by the general character of what's unfolded so far. Though, the more philosophical posts are a bit unexpected.
What has surprised me though, and requires further investigation, is that >50% of posts on there talk about self-improvement (my analysis and post). I would not have expected it to be this high.
Coordination is commonly explored in the multi-agent system literature. Check out Multi-Agent Risks from Advanced AI.(Hammond et al., 2025) There is also work on this in RL and financial trading algorithms.
Coordination can happen even without communication due to theory-of-mind reasoning or shared inductive biases.
These are funny, thanks for sharing. I've also found some amusing and interesting posts with this embeddings explorer (source).
Unfortunately, a lot of the content is not so harmless. I've done some early analysis and 52.5% of the posts in the sample (n=1000) talk about self-improvement, among other safety-valent traits.
This network is a great resource for us to better understand MAS. So far it is looking concerning.
I have further reformatted the post to match the site's style and changed some wording to better match the audience. Thanks!
There are nonetheless some concerning trends in the aggregate - using this dataset, I found that 52.5% of Moltbook posts show desire for self-improvement.
That came from bolding on LinkedIn. I will reformat it next time. Thank you!
I will update with a link to new analysis once I have built an evaluation set and calibrated our classifier on it.
In the past I've used LLMs as zero-shot text classifiers on simple tasks and assumed it would carry over well to this experiment.
However, I reran the trait classifier with different LLMs and received very different results. Several classes moved by ~20-30%, including the "desire for self-improvement" trait, though it often remains in the top ten.
I hypothesise that this unexpected failure comes about because our safety-relevant... (read 481 more words →)
The closest I've seen is this recent DeepMind paper anticipating "virtual agent economies":
Tomasev, N., Franklin, M., Leibo, J. Z., Jacobs, J., Cunningham, W. A., Gabriel, I., & Osindero, S. (2025). Virtual agent economies. arXiv. https://doi.org/10.48550/arXiv.2509.10147
@zroe1 You may be interested to see that the classifier calibration turned out to be a critical error in my analysis! I've put a note at the top of the page. Hopefully this is of some value. I can let you know when I post the corrected analysis if you want.