{ "schema_version": 1, "builder_plan": { "scenario": "Long customer conversations exhaust memory or limit concurrent requests.", "change": "Change only the attention cache policy, after checking model and runtime support.", "baseline": "The same model with its ordinary attention cache.", "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": "Peak cache memory, long-context answer quality, first-token latency, and requests completed.", "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": "2609.39329", "title": "PatchKV: Weight-Space Compensation of KV Cache", "summary": "PatchKV does not replace KV cache compression; it augments an existing compressed cache (eviction or approximation based) with a context-specific weight patch computed once via closed-form ridge regression that aligns activations of reference queries between the full and compressed cache. Costs are a one-time per-context patch computation at context-loading time, storage of one patch per served context (or re-merge per context switch), and the engineering complexity of merging and unmerging weight deltas in the serving stack; per-query inference cost is claimed unchanged only in the single-context multi-query setting. 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