{ "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": "2609.15030", "title": "Validating Hybrid-State Cache Recovery for GLM-5.3-Flash with vLLM and LMCache", "summary": "The method replaces full prompt recomputation after cache transfer with externally restored KV/hybrid state repaired by strict-prefix lookup so the scheduler credits the correct token count. It costs the LMCache integration layer, a patched vLLM recovery path, matched checkpoint scheduling, and fixed per-rank kernel configurations, adding operational complexity for a 1.9-7.0% end-to-end latency benefit. 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