After reading the proposal, the discussion, and the thoughtful feedback from Ryan and others, I decided to support this project because I believe it addresses an area that deserves much more attention as AI systems become increasingly collaborative.

This is one of the more interesting proposals I've read on multi-agent alignment because it focuses on something that often receives less attention: the environment that agents inherit, rather than only the agents themselves. The observations around semantic information, sticky comments, and passive behavioral steering raise important questions about how future AI systems will behave as they increasingly collaborate on shared codebases instead of operating in isolation.

Ryan's point about agent swarms becoming common inside frontier labs—and Jonathan's response about open ecosystems potentially presenting an even broader attack surface—both seem compelling. As more autonomous agents contribute to open-source repositories, shared libraries, and collaborative development environments, it feels reasonable to expect that semantic contamination could propagate much further than we currently anticipate.

Ryan's references to Moltbook, Gastown, AI Village, and the growing ecosystem around multi-agent systems highlight how quickly this area is evolving. At the same time, platforms such as Rosely AI, where users continuously interact with AI agents over long conversations, also demonstrate that persistent AI environments are becoming increasingly common outside purely software engineering workflows. Although these are different domains, both raise interesting questions about how agents accumulate context and whether undesirable behaviors can gradually propagate through shared environments.

A few questions came to mind while reading the proposal:

• Have you considered measuring whether these semantic effects accumulate over multiple generations of agents, rather than only within a single interaction trajectory?

• Do you expect the "semantic vaccines" to generalize across different model families (Gemini, Claude, GPT, open-weight models), or will they require model-specific tuning?

• Could your observability framework eventually produce standardized benchmarks that other researchers could use to compare the resilience of different multi-agent systems against semantic contamination?

I appreciate the decision to open-source the research harness and publish results regardless of the outcome. Work that improves observability, reproducibility, and practical AI safety benefits the wider research community, and I'm looking forward to following the project's progress.

Best of luck to the team & Thanks in Advance!