As autonomous agents increasingly act without human review, their communication protocols become a safety decision, not just an engineering one. Today's LLM agents talk in natural language or loosely structured formats like MCP, and a classical result in computer science (Rice's theorem) says once messages can express anything, no filter can reliably distinguish safe from adversarial inputs — prompt injection and OpenCLAW's security blunders are the predictable result. With my Sydney-based co-author Hugo O'Connor, I'd like to present CBCL (Common Business Communication Language), an agent communication framework whose safety properties are machine-checked by a theorem prover, and use it to ground a broader argument: formally constraining what agents can say to each other is necessary, but not sufficient, for meaningful human oversight of multi-agent systems. CBCL lets agents teach each other new vocabulary at runtime, so structural safety doesn't come at the cost of flexibility. The work is currently under review at NeurIPS, with further empirical results expected between now and July. Estimated duration: 10 minutes including Q&A, could also work as regular presentation.
Speakers
Aaron Snoswell
Senior Research Fellow, AI Accountability, Queensland University of Technology GenAI Lab
I'm a computer scientist working in AI Accountability, with a focus on responsible LLM evaluation. Current projects focus on Generative AI in public service media, quantifying the risks of "silicone sampling", care-ethics grounded AI Alignment, and multi-agent communication safety. I'm part of the ARC CoE in Automated Decision Making and Society (ADM+S) and the Machine Intelligence and Normative Theory (MINT) Lab.
Hugo O'Connor
R&D Software Engineer, Anuna Research
Hugo O'Connor is a founding member and technical researcher at Anuna Research, an Australian R&D organisation. With a background in applied cryptography and distributed systems, he develops free, open-source tools designed to nourish people, communities, and the planet.
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