{
  "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": "2609.37590",
  "title": "FOCUS: Training-Free Decision-Preserving Context Compression for LLM Agents",
  "summary": "Replaces offline-trained context compressors (guideline optimization, distilled compressors, learned compression policies) with a test-time, training-free layer that selects which past interaction units causally shape future decisions, attachable to closed-API models. Costs additional inference-time computation per compression step to identify decision-relevant units, plus integration complexity in the agent loop; no fine-tuning or extra hardware required. Can fail if the causal relevance scoring misidentifies which history units matter, silently dropping information needed later in long trajectories, and the reported gains may not transfer to the reader's specific tools, tasks, or base model.",
  "rationale": "It fits constrained infrastructure since it requires no training and wraps existing API models, but the workload-specific risk of discarding causally important history demands validation on the reader's own agent tasks before production adoption.",
  "technique": "Training-free causal decision-preserving context compression",
  "family": "memoria_e_contexto",
  "recommendation": "testar",
  "published": "2026-09-29",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.37590",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 131,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "Replaces offline-trained context compressors (guideline optimization, distilled compressors, learned compression policies) with a test-time, training-free layer that selects which past interaction units causally shape future decisions, attachable to closed-API models. Costs additional inference-time computation per compression step to identify decision-relevant units, plus integration complexity in the agent loop; no fine-tuning or extra hardware required. Can fail if the causal relevance scoring misidentifies which history units matter, silently dropping information needed later in long trajectories, and the reported gains may not transfer to the reader's specific tools, tasks, or base model.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.37590",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Cuts peak context by up to 48% and dependency by 73% while improving task success by up to 8.9 percentage points over uncompressed execution.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.37590",
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    }
  ],
  "exposure_map": [
    {
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      "finding": "",
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      "next_check": "",
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    },
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      "dimension": "compute",
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    },
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      "dimension": "latency",
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    }
  ],
  "risks": [],
  "minimum_test": [],
  "open_questions": [],
  "independent_tests": [],
  "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": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}