{
  "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": "2609.35750",
  "title": "KV-streams for Efficient Compaction in Agentic Reinforcement Learning",
  "summary": "KV-streams replaces the standard compaction loop in which the KV cache is flushed and the context is reprefilled after every compaction step, instead streaming the existing cache forward so compaction becomes cheap and differentiable-friendly during post-training. The cost is mainly engineering complexity: the cache must be managed as a persistent recurrent state across compaction boundaries, and the approach only pays off in pipelines that actually perform repeated compaction during long-horizon RL rollouts. It can fail if the streamed cache drifts from what a fresh prefill would produce, if the chosen compaction strategy interacts poorly with stale cache entries, or if the workload's traces are short enough that prefill overhead was never the bottleneck.",
  "rationale": "The method is plug-and-play and targets exactly the prefill-repetition bottleneck that a single-GPU post-training setup with long agentic traces would hit, but its benefit depends on the reader actually running compaction-heavy RL training, so it warrants a small-scale validation before production adoption.",
  "technique": "KV-streams: streamed KV cache for trainable context compaction",
  "family": "cache_kv",
  "recommendation": "testar",
  "published": "2026-09-28",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.35750",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
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      "dimension": "compatibility",
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