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Argo-Bench: Evaluating Data Agents on Enterprise-Scale Workflows

Paper recorded by Signals 4 on 2026-10-01 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-01 on arXiv · recorded by Signals 4 on 2026-10-02

Category: cs.AI · 人工智能 · first seen 2026-10-02

Abstract

Real-world enterprise data science and analytics workflows require reasoning across dozens of tables, performing statistical analyses, and acting on the results. Established text-to-SQL benchmarks evaluate query generation alone, and audits have found their answer keys frequently wrong. Because real enterprise warehouses are too sensitive to release, these benchmarks are built on public datasets w

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#19 most recent of 500 cs.AI papers we have recorded · ↑ newer: Local Support Learning · ↓ older: Where-OPD: Spatially Guided On-Policy Self-Distillation of MLLMs with
Cite this page: Argo-Bench: Evaluating Data Agents on Enterprise-Scale Workflows: the #19 most recent of 500 cs.AI papers we have recorded (as of 2026-10-01). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/argo-bench-evaluating-data-agents-on-enterprise-scale-workflows.html
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