{
  "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": "2609.39185",
  "title": "Low-Discrepancy Dither for Quantized Recurrent State Caches",
  "summary": "The method replaces stochastic rounding (and round-to-nearest) used when writing a low-precision Mamba-style or hybrid recurrent state back to cache with a deterministic golden-ratio Weyl dithering rule. It costs nothing: no random number generation, no extra memory or latency, only a deterministic offset added before rounding, though implementations must match the documented pitfalls to retain the benefit. It can fail if the reader's stack uses only transformer attention with a KV cache (the technique applies to recurrent state caches), if the implementation subtly misapplies the dither sequence, or if evaluations are too short to reveal the error accumulation it prevents.",
  "rationale": "For any deployment that stores Mamba-style recurrent states in low precision, this is a zero-cost, drop-in replacement for stochastic rounding with strictly better long-horizon fidelity, provided the team actually runs such models.",
  "technique": "Golden-ratio Weyl dither for quantized recurrent state",
  "family": "cache_kv",
  "recommendation": "adotar",
  "published": "2026-09-30",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.39185",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
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    {
      "dimension": "compatibility",
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      "dimension": "security",
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  "report_available": false,
  "validation_tier": "unknown",
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  "equations_status": "not_fetched",
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