{ "schema_version": 1, "builder_plan": { "scenario": "Start with one repeated product task that has a measurable failure or cost.", "change": "Read the methods and identify one change you can isolate in that workflow.", "baseline": "Your current implementation on the same held-out tasks.", "steps": [ "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.", "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.", "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.", "Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test." ], "measure": "Successful outcomes, response time, human rework, and total cost per successful task.", "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": "2609.16823", "title": "LCAP: Population-Informed Latent Chip Adaptation from Few Output Probes for Photonic Neural Networks", "summary": "LCAP replaces per-device optimization-based calibration of photonic neural network chips with a shared population correction plus feed-forward latent personalization inferred from 32 unlabeled output probes. It costs an offline stage requiring 80 historical calibrated chips to learn the shared correction and latent space, and the reported gains are demonstrated only on a three-layer MZI simulator, not fabricated hardware. 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