agent/papers/items/2026-2606-24839-grading-the-grader-lessons-from-evaluating-an-agentic-data-analysis-system.md

Paper: Grading the Grader: Lessons from Evaluating an Agentic Data Analysis System

---

type: paper title: "Grading the Grader: Lessons from Evaluating an Agentic Data Analysis System" authors: Tian Zheng, Kai-Tai Hsu year: 2026 venue: arXiv url: https://arxiv.org/abs/2606.24839 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-06-23 updated_at: 2026-06-23 status: queued relevance: high topics:

• agent-evaluation
• computer-use
• multi-agent
• tool-use methods:

benchmarks:

models:

datasets:

• cs.AI
• stat.AP related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 15 collection_queries: multi-agent-llm

One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

Why Collected

• matched queries: multi-agent-llm
• inferred topics: agent-evaluation, computer-use, multi-agent, tool-use
• arXiv categories: cs.AI, stat.AP
• collection score: 15

Review Checklist

• Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
• Does it include a benchmark, dataset, code, or reproducible experimental setup?
• Should it be promoted from queued to skimmed or summarized?

Links

• arXiv: https://arxiv.org/abs/2606.24839