{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A workflow needs specialist review, but extra agents may add more cost than useful work.",
    "change": "Test the proposed division of work against a single-agent implementation.",
    "baseline": "One agent with the same tools and an equal total token and time budget.",
    "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": "Accepted outputs, coordination failures, wall-clock time, and total cost across all agents.",
    "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": "2610.11458",
  "title": "Generative Adversarial Loops",
  "summary": "GAL replaces human-driven benchmark creation and method discovery with an alternating agentic loop in which a discriminator generates adversarial inputs exposing weaknesses and a generator searches for algorithms that fix them. The cost is substantial: repeated agentic search over candidate algorithms, evaluation infrastructure for four inference tasks, and API or GPU budget well beyond a single 24 GB card for meaningful runs. It can fail by overfitting discovered algorithms to discriminator-generated data that does not reflect the reader's production distribution, and improvements on established benchmarks are not guaranteed to transfer.",
  "rationale": "This is a research-automation framework rather than a deployable inference optimization; a small team gains nothing directly, though the adversarial-evaluation idea can inform how they stress-test their own KV compression and context-extension choices.",
  "technique": "Adversarial generator-discriminator loop for automated algorithm discovery",
  "family": "orquestracao_multiagente",
  "recommendation": "observar",
  "published": "2026-10-08",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.11458",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 4,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "GAL replaces human-driven benchmark creation and method discovery with an alternating agentic loop in which a discriminator generates adversarial inputs exposing weaknesses and a generator searches for algorithms that fix them. The cost is substantial: repeated agentic search over candidate algorithms, evaluation infrastructure for four inference tasks, and API or GPU budget well beyond a single 24 GB card for meaningful runs. It can fail by overfitting discovered algorithms to discriminator-generated data that does not reflect the reader's production distribution, and improvements on established benchmarks are not guaranteed to transfer.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.11458",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Improved CompactorPress KV compression on Qwen3-4B at 4x from 0.35 to 0.97 on the discriminator dataset and +0.77 points on RULER-HARD; Dual Chunk Attention context extension improved from 0.20 to 0.90 on the discriminator dataset with +6 points on ScienceFiction and -0.33 PPL on PG19 32K.",
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      "result": "",
      "baseline": "",
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      "source_url": "https://arxiv.org/abs/2610.11458",
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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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      "source_page": null,
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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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      "next_check": "",
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      "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": "",
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}