Multi-Probe Bayesian Spam Gating: Filtering Junk Before Spending Compute
In a B2B lead generation pipeline, every email that arrives costs compute. Scoring it for buyer intent, extracting entities, predicting reply probability, matching it against your ideal customer profile — each module is a DeBERTa forward pass. If 40% of inbound email is template spam, AI-generated slop, or mass-sent campaigns, you are burning 40% of your GPU budget on garbage.
The solution is a gating module: a spam classifier that sits at stage 2 of the pipeline and filters junk before anything else runs. But a binary spam/not-spam classifier is too blunt. You need to know why something is spam (template? AI-generated? role account?), how confident you are (is it ambiguous, or have you never seen this pattern before?), and which provider will block it (Gmail is stricter than Yahoo on link density).
This article documents a hierarchical Bayesian spam gating system with 4 aspect-specific attention probes, information-theoretic AI detection features, uncertainty decomposition, and a full Rust distillation path. The Python model trains on DeBERTa-v3-base. The Rust classifier runs at batch speed with 24 features and zero ML dependencies.