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.
lars