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Turns "is this component earning its parameters?" into a one-line config flip for each of the three pluggable network components: - router.type = "none" (NoneRouter, giant/model/routers.py): still builds n_experts expert trunks via RoutedTrunk, but replaces the learned gate with a uniform 1/n_experts weight for every row — no centers/embeddings/classifier. Distinct from router.enabled=false (which drops routing/mixing entirely): this isolates whether the *learned routing signal* specifically is earning its parameters, holding expert count fixed. - stage2_model.autoregressive.history = "none" (NoHistory, giant/model/history.py): ignores feat/has_prev entirely and always returns zeros, ablating whether the AR decoder's history conditioning earns its parameters. Already validated for free by gitea #35's generic HISTORY_REGISTRY membership check. - trunk.type = "linear" (LinearTrunk, giant/model/trunks.py): a bare nn.Linear(in_dim + cond_dim, out_dim) body, no ResBlock stack. Per gitea #33's design, this composes for free with router.enabled=true ("mixture of trivial linear experts"). Both blocking issues (#33 trunk registry, #35 pluggable history encoder) are closed, so this was unblocked.