{
  "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": "2610.02163",
  "title": "AutoCompact: Learning When to Compact Context in Long-Horizon Coding Agents",
  "summary": "AutoCompact replaces heuristic or overflow-triggered context compaction with a compaction decision learned as part of the agent policy, trained via judge-corrected supervised fine-tuning followed by reinforcement learning with task-success rewards. Adoption requires the full training pipeline: rollouts of a base coding agent, a judge model to critique and correct compaction decisions, SFT infrastructure, and an RL stage with executable task environments. Failure modes include compaction policies that transfer poorly across models, repositories, or context budgets, judge errors propagating into corrected trajectories, and the added complexity of maintaining a training loop that a small team with one 24 GB GPU and no foundation-model training capacity cannot realistically reproduce.",
  "rationale": "The gains are meaningful but depend on SFT-plus-RL training of the agent itself, which is beyond the reader's constrained infrastructure, so the reader should track the work and apply its heuristic insights rather than attempt reproduction.",
  "technique": "Learned context-compaction policy for coding agents",
  "family": "memoria_e_contexto",
  "recommendation": "observar",
  "published": "2026-10-01",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.02163",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 131,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.02163",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Improves pass rates over the base model by 9.2 absolute points on SWE-bench Verified and 5.0 points on SWE-PolyBench Verified, consistent across inference budgets with 256K and 16K context windows.",
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      "source_url": "https://arxiv.org/abs/2610.02163",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
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      "dimension": "compute",
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      "dimension": "latency",
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
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      "source_page": null,
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    },
    {
      "dimension": "reproducibility",
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      "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": "",
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  "equations_source_sha256": "",
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}