{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "An agent looks good in demos, but you cannot tell which product tasks it fails.",
    "change": "Use the study's evaluation idea to inspect a small set of tasks with human-checked answers.",
    "baseline": "Your current evaluator and a manually reviewed reference set.",
    "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": "Agreement with human review, missed failures, false alarms, and evaluation cost per 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": "2609.35639",
  "title": "GPUPhysBench: Benchmarking Coding Agents for Correct and Efficient GPU Physics Simulation",
  "summary": "GPUPhysBench is an evaluation benchmark of 50 GPU physics simulation tasks (fluids, deformable solids, granular materials) that replaces ad hoc or proxy assessments of coding agents with compile-test-optimize tasks scored for correctness and runtime against expert reference implementations. It is not a deployable method, so adoption cost is limited to evaluation compute and harness setup; using it requires GPU access and workloads that actually involve numerical GPU simulation. Findings can fail to transfer: correctness passes saturate (two model-harness pairs pass all 50 tasks) while efficiency lags badly (the fastest agent reaches at least 0.9x reference speed on only 22% of tasks, with the largest gaps in collision detection, constraint solving, and iterative solvers), so results here may not predict agent performance on the reader's own codebase.",
  "rationale": "This is a benchmark, not a production technique, and its relevance depends on whether the reader evaluates or relies on coding agents for performance-critical GPU numerical code, which warrants monitoring rather than adoption.",
  "technique": "Benchmark for coding agents on GPU physics simulation",
  "family": "avaliacao_de_agente",
  "recommendation": "observar",
  "published": "2026-09-28",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.35639",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
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      "result": "",
      "baseline": "",
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      "source_url": "https://arxiv.org/abs/2609.35639",
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      "source_excerpt": ""
    }
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      "dimension": "compatibility",
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    },
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      "dimension": "security",
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    },
    {
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  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
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  "equations": [],
  "equations_status": "not_fetched",
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  "equations_fetched_at": "",
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}