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warm-cache built its config from DEFAULT_CONFIG with only a handful of flags overridable, so it had no way to express settings like stage2_model.particle_type.n_classes. configs/baseline.toml sets that to 32; warm-cache always warmed the pdg top-N map under the emb_dim default (16) instead, so a `giant train --config configs/baseline.toml` run silently missed the cache and repaid the full parquet scan warm-cache exists to avoid. warm-cache now accepts the same --config a training run takes and resolves every value run_setup_stage needs (val_fraction/seed, conditioning types, both stages' router, particle_type.n_classes, ...) from one gconfig.merge_cli_overrides + validate_config pass, exactly like giant train's own pipeline does — so warming and training are guaranteed to agree. Per user decision, --config is mutually exclusive with the individual --val-fraction/--seed/--particle-conditioning/ --material-conditioning/--router*/flags (rejected outright rather than silently layered on top), since a hardcoded CLI default clobbering an unset config value is the same failure mode one level down. Also drops a hardcoded stage2_model.router/k_max override that was a no-op against today's defaults but would have clobbered a config setting either one away from its default — same bug class. Adding validate_config surfaced that the existing test_warm_cache_router_process_warms_proc_map test was warming a router.type="process" + conditioning.particle.type="physical" (the CLI's old hardcoded default) combination that giant train's own validate_config would already reject as incompatible — fixed by passing --particle-conditioning embedding, which is what a working --router-type process run actually requires. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>