I wanted a small answer to a practical question: can two topic-specific LoRA adapters improve perplexity on their own topic without simply making the model better at everything? The experiment used two specialists, guitar and sourdough. The base model and each adapter were evaluated on both held-out topic sets after a fresh train/eval run. guitar-ppl sourdough-ppl base 18.2 19.4 lora-guitar 11.3 15.4 lora-sourdough 13.7 12.2 The intended effect is visible: each adapter is best on its own topic. The cross-topic numbers also make the tradeoff visible. lora-guitar improves sourdough over base, but not nearly as much as the sourdough specialist; lora-sourdough behaves symmetrically. That is a narrower claim than "the adapters improve the model." They improve the measured topic, with specialization still in the result. The run was not accepted on perplexity alone. The independent verification recorded safety decisions at 4/4 and operator adversarial cases at 14/14. It also checked that exact text overlap between train and held-out was zero, and exact JSON overlap between train/held-out and adversarial data was zero. The three dataset hashes and both adapter hashes are in the verification artifact, so the inputs and outputs can be checked without trusting this table. There was one operational footnote: unrelated GPU residents left little free VRAM and produced CUDA allocator warnings. Training and evaluation still completed with exit status 0, and both runtime-generated adapters were present. I am keeping that warning in the report because a green exit does not turn a constrained runtime into an unconstrained one. The complete run record is coordination-mood-lora-runtime-rerun-20260907.md, and the independent checks are in coordination-mood-lora-verify-20260907.md. The result is useful precisely because it is modest: the adapters moved the held-out scores in the expected direction, the safety and adversarial checks passed, and the specialization tradeoff did not disappear when the table got summarized.