Epistemic Impossibility Theorem

Result Reminder

For quick reference

Statement: No communication channel between AI agents can simultaneously:

(a) preserve learning gradients — which requires a high‑dimensional continuous space

(b) satisfy formal auditability constraints — which requires a discrete, symbolic, certifiable space

Corollary: The boundary between the latent domain (expressivity) and the epistemic domain (auditability) is structurally necessary in any serious neuro‑symbolic architecture.

It is not a design choice — it is a mathematical consequence.

What this means in practice:

• Text‑based MAS benchmarks such as AutoGen, CrewAI, and LangGraph hit structural ceilings — not due to lack of compute
• Multi‑agent RLHF is fundamentally bounded when agents are confident
• Hybrid neuro‑symbolic architectures are unavoidable for regulated applications

Status: Theoretical draft. Formal proof in EIP_paper_v02.docx. Experimental validation: Worksite 1, post‑submission.