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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": "2609.21109", "title": "Talk to Me, Jarvis: An Open-Source Edge-Deployable Voice Assistant Framework for Autonomous Racecars", "summary": "Replaces network-dependent, online-hosted large models for voice command intent classification with a locally deployed Mistral 7B fine-tuned on a domain-specific command dataset, removing network dependency and variable API latency. Costs one-time fine-tuning effort and enough local compute to serve a 7B model (roughly 5-16 GB depending on quantization), plus ongoing dataset maintenance as the command set evolves; latency of about 1.4 s end-to-end is acceptable for high-level commands but not for reflex-level control. 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