{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A support or data assistant repeats work across related customer requests.",
    "change": "Try the paper's memory change in a replay of past tasks, with separate memory per customer.",
    "baseline": "Your current memory pipeline plus a no-memory control using the same agent.",
    "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": "Task success, stale memories, retrieval misses, response latency, and all memory maintenance costs.",
    "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": "2609.29875",
  "title": "When Can Agents Forget Their Reasoning? ICLR for Long-Horizon Agent Context Compression",
  "summary": "The method replaces retaining the full accumulated reasoning history in the agent's context with training-free online deletion of reasoning blocks ranked by frozen proxy entropy, while preserving actions, tool calls, and observations. It costs only the ranking computation and integration logic, with no training, fine-tuning, or additional infrastructure, and the paper reports a small quality gain rather than a loss. It can fail through trajectory amplification: deleting a reasoning block can nonlinearly alter subsequent actions and total computation, and reasoning removed before task-relevant state is externalized into code, files, or tool outputs can degrade future decisions.",
  "rationale": "It is training-free and directly reduces API token spend on a single-GPU or API budget, but the documented trajectory-amplification risk means the reader must validate compression safety on their own agent workloads before production adoption.",
  "technique": "Online reasoning-history compression for long-horizon agents",
  "family": "memoria_e_contexto",
  "recommendation": "testar",
  "published": "2026-09-24",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.29875",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
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      "statement": "On 260 WorkBuddyBench tasks, ICLR reduces input tokens by 25.5%, output tokens by 14.4%, and cache read tokens by 33.3%, while improving average reward from 0.699 to 0.718.",
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  "report_available": false,
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