We extend the Governance Physics framework from output-level attestation to attention-level computational provenance. By recognizing the structural isomorphism between the softmax normalization constraint (Σⱼ attentionᵢⱼ = 1) and the trust conservation law (Σᵢ ωᵢ = K), we demonstrate that transformer attention mechanisms already encode the mathematical structure required for governance verification. We introduce attention fingerprinting—cryptographically hashing attention matrices at each layer into Merkle chains—enabling tamper-evident provenance of the computational path that produced a decision. Experimental evaluation across GPT-2 variants (124M–1.5B parameters) demonstrates 8–28% latency overhead with 100% conservation verification accuracy. This is Paper VI in the Governance Physics series.