{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Start with one repeated product task that has a measurable failure or cost.",
    "change": "Read the methods and identify one change you can isolate in that workflow.",
    "baseline": "Your current implementation on the same held-out tasks.",
    "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": "Successful outcomes, response time, human rework, and total cost per successful task.",
    "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": "2609.18359",
  "title": "RecMorph: Topology-Guided Spatial Recurrence for Generalized Morphology Control",
  "summary": "RecMorph replaces per-morphology specialist MLP controllers and message-passing or attention-based cross-limb communication with a single shared policy that runs bidirectional recurrent transitions along a depth-first traversal of the robot's kinematic tree. It costs recurrent sequential computation over limbs (linear in token count at fixed width and depth), plus stabilization components (residuals, RMSNorm, input-dependent channel modulation), and requires reinforcement-learning training on procedural or physical robot platforms. It can fail when deployed morphologies differ structurally from training bodies beyond the demonstrated 30-limb range, when sim-to-real transfer gaps exceed those observed on Go1/Go2, and it has no demonstrated relevance outside robot locomotion and morphology control.",
  "rationale": "The method targets reinforcement-learned robot locomotion across morphologies, which is outside the LLM inference, agent, and training-efficiency work this reader's constrained-infrastructure production practice concerns.",
  "technique": "Topology-guided spatial recurrence for robot morphology control",
  "family": "outro",
  "recommendation": "nao_aplica",
  "published": "2026-09-16",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.18359",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 1,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "RecMorph replaces per-morphology specialist MLP controllers and message-passing or attention-based cross-limb communication with a single shared policy that runs bidirectional recurrent transitions along a depth-first traversal of the robot's kinematic tree. It costs recurrent sequential computation over limbs (linear in token count at fixed width and depth), plus stabilization components (residuals, RMSNorm, input-dependent channel modulation), and requires reinforcement-learning training on procedural or physical robot platforms. It can fail when deployed morphologies differ structurally from training bodies beyond the demonstrated 30-limb range, when sim-to-real transfer gaps exceed those observed on Go1/Go2, and it has no demonstrated relevance outside robot locomotion and morphology control.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.18359",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Reduces nominal velocity RMSE by 43.5% relative to specialist MLPs in a four-platform quadruped setting; one shared policy completes 40 physical Go1/Go2 trials without falls.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.18359",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
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      "finding": "",
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      "next_check": "",
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    },
    {
      "dimension": "compute",
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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",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
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      "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": ""
}