{ "schema_version": 1, "builder_plan": { "scenario": "Your coding assistant makes changes that still need substantial repair before merging.", "change": "Try the technique on held-out issues from a repository with reliable tests.", "baseline": "The same coding agent, repository snapshot, tools, and token 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": "Correct patches, regression failures, human repair time, and total cost per accepted change.", "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": "2609.15096", "title": "OpenAI4S: Code as Action, Science as Sessions", "summary": "OpenAI4S replaces ad hoc LLM coding assistants for computational research by executing complete code cells in persistent Python/R kernels with an append-only action ledger, per-cell execution records, versioned artifacts, and workspace checkpoints. The cost is the operational overhead of running sandboxed persistent kernels, session and provenance management, and the engineering effort to integrate it into an existing research codebase. It can fail at environment specification and full rerunnability, which the authors state remains weak for all evaluated systems including their own, and the score advantage may not transfer to domains outside the 36 benchmarked scenarios.", "rationale": "The system is open source under MIT and runs on third-party LLM APIs within the reader's budget, but the demonstrated benefit is a modest point gain on a specific scientific benchmark, so workload-specific validation is warranted before production adoption.", "technique": "Scientific agent with persistent runtime and action ledger", "family": "agentes_de_codigo", "recommendation": "testar", "published": "2026-09-14", "as_of": "2026-10-18", "editorial_status": "indexed", "source_url": "https://arxiv.org/abs/2609.15096", "independent_implementations": 1, "total_implementations": 2, "stars": 606, "citations": 0, "claims": [ { "claim_id": "claim-01", "statement": "OpenAI4S replaces ad hoc LLM coding assistants for computational research by executing complete code cells in persistent Python/R kernels with an append-only action ledger, per-cell execution records, versioned artifacts, and workspace checkpoints. The cost is the operational overhead of running sandboxed persistent kernels, session and provenance management, and the engineering effort to integrate it into an existing research codebase. It can fail at environment specification and full rerunnability, which the authors state remains weak for all evaluated systems including their own, and the score advantage may not transfer to domains outside the 36 benchmarked scenarios.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2609.15096", "source_page": null, "source_excerpt": "" }, { "claim_id": "claim-02", "statement": "Overall score of 7.83 versus 5.7-6.4 for a general-purpose coding harness with three frontier models across 36 scientific scenarios; gains are point differences on an evaluation scale, not a multiplicative factor.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2609.15096", "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": [ { "anchor": "S4.Ex1", "label": "", "section": "4 Experiments", "role": "metric", "latex": "\\mathrm{Score}=10\\,\\bigl(W_{\\mathrm{acc}}\\cdot\\mathrm{Score}_{\\mathrm{acc}}+W_{\\mathrm{wf}}\\cdot\\mathrm{Score}_{\\mathrm{wf}}+W_{\\mathrm{rep}}\\cdot\\mathrm{Score}_{\\mathrm{rep}}\\bigr).", "mathml": "Score=10​(Wacc⋅Scoreacc+Wwf⋅Scorewf+Wrep⋅Scorerep).", "context": "nary code generation. A good solution reaches a correct final answer through a scientifically valid procedure and leaves enough information behind for later inspection and reproduction. We score each generated repository on three dimensions. Scientific Task Accuracy measures whether the target task is completed with valid, scientifically grounded results. Scientific Workflow Completeness measures whether the repository implements the essential procedure, from data preparation and method selection through computation and intermediate analysis to the final conclusion. Scientific Reproducibility measures whether the environment, intermediate artifacts, execution trajectory and continuation state are preserved well enough for another researcher to understand, reproduce or extend the experiment. 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