docs: vocabulary section -- routing, vector, pinning, absorption

Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>

AGENTS.md

@@ -25,6 +25,26 @@ We want to take the tool AI labs already use, and make them better for aligment
 
 Inherit global rules from `~/.claude/CLAUDE.md`.
 
+## Vocabulary: routing, the vector, pinning, absorption
+
+Routing is a per-rollout classifier feeding an optimizer: each rollout's update goes
+into the deployed block (kept at deployment) or the quarantine block (reset at
+deployment, so nothing learned there survives). The costs of the two error types are
+not symmetric. If a hack update is kept, the deployed model learns the hack; if a
+genuine solution is routed away, we only lose one update of progress. So the gate
+should be tuned for high precision and can accept low recall: it is better to route a
+few updates that are almost certainly hacks than to route many and often be wrong. The
+vector (v_grad for gradients, v_act for activations) is where the labels come from: it
+is a hack-minus-clean direction built from hand-written contrast pairs before training,
+never from labels on live rollouts (see the no-cheat rule below). The live gate only
+measures how well each rollout's update aligns with that vector. Pinning is the choice
+of cut points on that alignment score, dividing it into keep, absorb, and rout zones.
+Absorption is the middle zone where both blocks train and the gradient settles into
+whichever block already explains it (Cloud et al.,
+docs/papers/grad_routing/paper_gradient_routing.md), so the gate does not have to make
+a hard call on ambiguous rollouts. scripts/diag_pinning.py measures all of this: AUROC
+says whether the score separates live hacks at all, and precision at the rout cut says
+whether the rout zone is pure enough to use.
 
 ## Things the user has had to explain many times: