{
  "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": "2609.24417",
  "title": "ARM: Attention with Routed-Memory for Learnable Sparse Control",
  "summary": "ARM replaces fixed or eviction-based KV caches with a differentiable, fixed-size hierarchical memory that uses Gumbel-Softmax slot selection and sigmoid-gated soft updates instead of hard token eviction. The cost is architectural modification and training of the routing and memory-update policy, which requires retraining or fine-tuning the model rather than plug-in deployment on an existing checkpoint. It can fail through routing instability from Gumbel-Softmax sampling, information loss if the fixed memory size is too small for hard long-context inputs, and absence of compatibility with off-the-shelf pretrained models.",
  "rationale": "The approach requires training a differentiable memory system into the model, which fits neither instant adoption on constrained infrastructure nor the reader's API-based workflows, so small-scale fine-tuning experiments are the appropriate next step.",
  "technique": "Routed learnable sparse KV memory (ARM)",
  "family": "cache_kv",
  "recommendation": "testar",
  "published": "2026-09-21",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.24417",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
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      "dimension": "security",
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  "risks": [],
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  "report_available": false,
  "validation_tier": "unknown",
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  "equations_status": "not_fetched",
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