{ "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": "2609.15188", "title": "MUSE: A Theory-Harnessed Story Engine for Vibe Narrativizing", "summary": "Replaces zero-shot or single-pass LLM story generation with a multi-stage pipeline (design, character performance, scene composition, revision) in which rules distilled from Robert McKee's story theory are injected via context engineering and intermediate deliverables carry decisions across stages. Costs include multiple LLM calls per story (raising latency and API spend roughly in proportion to the number of stages and roles), plus substantial one-time knowledge-engineering effort to atomize and maintain the rule base; all work is prompt- and orchestration-level, so it runs on third-party APIs without extra GPU memory. It can fail through error propagation across stages, rule misapplication where judgments depend on aesthetic context, evaluation metrics that may not transfer to domains outside story writing, and dependence on the quality of the curated masterwork corpus.", "rationale": "The approach is infrastructure-feasible for a constrained team (prompt orchestration over API calls, no training), and the reported WritingBench and LongStoryEval gains are consistent across four base models, but its value is specific to long-form narrative generation, so it should be validated on the reader's actual workload before adoption.", "technique": "Theory-harnessed multi-stage story generation pipeline", "family": "orquestracao_multiagente", "recommendation": "testar", "published": "2026-09-14", "as_of": "2026-10-18", "editorial_status": "indexed", "source_url": "https://arxiv.org/abs/2609.15188", "independent_implementations": 0, "total_implementations": 1, "stars": 1, "citations": 0, "claims": [ { "claim_id": "claim-01", "statement": "Replaces zero-shot or single-pass LLM story generation with a multi-stage pipeline (design, character performance, scene composition, revision) in which rules distilled from Robert McKee's story theory are injected via context engineering and intermediate deliverables carry decisions across stages. Costs include multiple LLM calls per story (raising latency and API spend roughly in proportion to the number of stages and roles), plus substantial one-time knowledge-engineering effort to atomize and maintain the rule base; all work is prompt- and orchestration-level, so it runs on third-party APIs without extra GPU memory. It can fail through error propagation across stages, rule misapplication where judgments depend on aesthetic context, evaluation metrics that may not transfer to domains outside story writing, and dependence on the quality of the curated masterwork corpus.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2609.15188", "source_page": null, "source_excerpt": "" }, { "claim_id": "claim-02", "statement": "Improves WritingBench by 1.1 to 6.2 points over zero-shot generation across four base models, and raises LongStoryEval by more than ten points on three of the four models; ConStory-Bench consistency error density stays in the low single digits.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2609.15188", "source_page": null, "source_excerpt": "" } ], "exposure_map": [ { "dimension": "quality", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "compute", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "latency", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "operations", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "compatibility", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "security", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "data_and_training", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "reproducibility", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" } ], "risks": [], "minimum_test": [], "open_questions": [], "independent_tests": [], "lab_reproductions": [], "report_available": false, "validation_tier": "unknown", "evidence_tier": "unknown", "infrastructure_basis": "unknown", "equations": [], "equations_status": "selector_failed", "core_kind": "", "equations_fetched_at": "2026-10-06", "equations_source_url": "", "equations_source_sha256": "", "exposures_reviewed_at": "", "exposures_source_sha256": "" }