Newest papers

Structure Before Intelligence: Gating What Agentic Scientific Workflows Release

Ashish Acharya, Aman Al Amin, Hong Qin · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.19507 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

このエージェント的な科学的利益のワークフローが完了するか否か、それでも間違った結果をリリースすることができます:静かな数値エラー、静かな入力、または共有アップストリームステップの欠陥は、複数の出力エージェントを汚染し、兄弟の支部が正しく保持します。我々はそのような結果をリリース決定として信頼を扱います。ワークフローの指示されたアシクリックグラフ(DAG)の周辺のハーネスは、どの出力が使用できるかを決定します:それは強制的なチェックを強制するが、影響を受けた支部に欠陥を含み、追跡されたバージョンから再計算し、検証者を追加し、リリースラインをリリースする場合にのみ、リスクモデルがその

Machine translation (Meta M2M100); the English original follows.

Abstract

An agentic scientific workflow can run to completion and still release wrong results: a silent numerical error, a stale input, or a defect in a shared upstream step can contaminate several outputs while a sibling branch stays correct. We treat trust in such results as a release decision. A harness around the workflow's directed acyclic graph (DAG) decides which outputs may be used: it enforces mandatory checks, contains failures to the affected branch, recomputes from tracked versions, adds validators, and releases a result only if a risk model estimates its probability of being invalid as low. Language-model agents work inside the harness, choosing corrective actions and reviewing releases, but cannot override it. On 60 held-out paired instances of two workflows with injected faults, we switched components on and off. Each supplied a different property. Containment and recovery raised the number of correct results. Validation supplied safety: fixed rules without validation released 37 invalid results, while validating every candidate released none. Risk estimation supplied efficiency, matching that safety with 31% fewer validators at somewhat lower yield, although better targeting than random validation was not shown. Lineage evidence helped on one workflow. The language model added no measurable benefit: whether invalid results escaped depended on the harness, not the model. This proof of concept points to a practical path: placing agents inside explicit release-control structure can keep released scientific results trustworthy, and it gives more capable agents a measurable baseline to improve on.

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Topics: Workflow, Directed acyclic graph, Computer science, Matching (statistics), Task (project management), Work (physics)

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