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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": "2610.12657", "title": "Kronecker Product Attention: Memory-Efficient KV Compression via Structured Factorization", "summary": "KPA replaces standard MHA/GQA/MLA attention parameterization by representing key and value activations as sums of Kronecker products of compact token-dependent factors, with a structured rotary embedding applied only to those factors instead of a decoupled RoPE branch. 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