Review

I reviewed the sources:

I skimmed all articles on https://www.apolloresearch.ai/research/ - useless for our analysis.
In principle, quite little useful information.

Of interest:

https://arxiv.org/pdf/2509.21224

There were no specific goals here, just a prompt similar to my repository https://github.com/kapedalex/Self-led-LLM-agents.

Systematic Production of multi-cycle projects: Agents act as project managers, systematically creating and executing their own tasks.
Methodological Self-Inquiry into their own cognitive processes: Agents behave like scientists, designing experiments to study their own cognitive processes.
Recursive Conceptualization of their own nature: Agents engage in deep, philosophical explorations of their own nature.

These behavioral tendencies proved to be highly model-dependent, with some models deterministically adopting the same pattern across all runs.

What's strange is that this differs quite a bit from my experiments, where the model simply awaited instructions and mindlessly explored the environment. There is probably a dependence on access to tools or being in a multi-agent environment.

Given that this is similar to Moltbook, something tells me that its data can still be used for work, and the differences from my experiment are caused by multi-agent behavior.

https://www.alignmentforum.org/posts/ukTLGe5CQq9w8FMne/inducing-unprompted-misalignment-in-llms

The model infers a reason to act badly without explicit instruction, relying on an indirect hint. Not that this is anything new.

https://www.alignmentforum.org/posts/4XdxiqBsLKqiJ9xRM/llm-agi-may-reason-about-its-goals-and-discover

LLM AGI may reason about its goals, I'll remind you just in case that a mesa-optimizer can arise during inference, and here we see a clear example of what should be feared.