{
  "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": "2610.00569",
  "title": "Scaling Collider Event Generation with Residual-Quantized Tokens",
  "summary": "The method replaces computationally expensive full detector simulation and reconstruction in high-energy physics with a fast autoregressive transformer surrogate trained on residual-quantized representations of particle-level event data. It costs the training of a domain-specific tokenizer and transformer on specialized collider datasets, plus validation effort to confirm that token-level loss correlates with physical fidelity. It can fail if the surrogate's learned distributions deviate from true detector physics in regions that matter for downstream analyses, and its applicability is limited to particle physics event generation rather than general language or code workloads.",
  "rationale": "This is a domain-specific scientific surrogate for collider simulation with no relevance to the reader's production LLM or ML engineering practice.",
  "technique": "Residual-quantized tokenized autoregressive event generation",
  "family": "outro",
  "recommendation": "nao_aplica",
  "published": "2026-09-30",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.00569",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces computationally expensive full detector simulation and reconstruction in high-energy physics with a fast autoregressive transformer surrogate trained on residual-quantized representations of particle-level event data. It costs the training of a domain-specific tokenizer and transformer on specialized collider datasets, plus validation effort to confirm that token-level loss correlates with physical fidelity. It can fail if the surrogate's learned distributions deviate from true detector physics in regions that matter for downstream analyses, and its applicability is limited to particle physics event generation rather than general language or code workloads.",
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      "source_url": "https://arxiv.org/abs/2610.00569",
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    },
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      "dimension": "reproducibility",
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  "risks": [],
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
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  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
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}