{
  "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": "2610.07940",
  "title": "Hybrid Latent Attention for Looped Language Models",
  "summary": "HLA replaces full per-loop key-value storage in looped language models (which multiply the KV cache by the loop count T) with exact keys/values in a sliding window plus compact latents for older tokens, read directly without reconstruction. It costs an uptraining phase per base model, added latent-attention parameters and implementation complexity, and a small accuracy loss (up to roughly 3% on some benchmarks). It can fail on tasks sensitive to information outside the sliding window at longer contexts than evaluated, and its gains vanish on standard non-looped architectures where the cache is not multiplied by T.",
  "rationale": "The technique targets looped models such as Ouro, which are not widely deployed, so the method is relevant to KV-cache strategy but cannot be adopted directly without a compatible base model and an uptraining run.",
  "technique": "Hybrid latent KV cache for looped models",
  "family": "cache_kv",
  "recommendation": "observar",
  "published": "2026-10-06",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.07940",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 121,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
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      "source_url": "https://arxiv.org/abs/2610.07940",
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      "claim_id": "claim-02",
      "statement": "Per-token KV cache shrinks 10.7x, fitting 4.0-8.8x more concurrent sequences per GPU; decoding throughput improves 2.5x at 1K tokens and up to 7.4x at 16K, while retaining over 97% of baseline accuracy.",
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  ],
  "exposure_map": [
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      "dimension": "compute",
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    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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    },
    {
      "dimension": "security",
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      "finding": "",
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    },
    {
      "dimension": "data_and_training",
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    },
    {
      "dimension": "reproducibility",
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  ],
  "risks": [],
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
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}