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Review regressions before expanding the test." ], "measure": "Held-out quality, peak memory, training time, and full compute cost to the target quality.", "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation." }, "builder_review": null, "arxiv_id": "2610.26802", "title": "Scaling Parameter-Efficient Fine-Tuning for Brain Encoding", "summary": "The method replaces fitting fixed readouts on frozen pretrained backbones (or costly full fine-tuning) with LoRA adapters plus a lightweight readout on large multimodal backbones, exploiting causal structure to predict multiple timepoints in one pass instead of sliding windows. 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