agent/papers/items/2026-2607-06413-an-experimental-design-approach-to-evaluating-agentic-ai-s-autonomous-model-disc.md

Paper: An Experimental Design Approach to Evaluating Agentic AI's Autonomous Model Discovery

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type: paper title: "An Experimental Design Approach to Evaluating Agentic AI's Autonomous Model Discovery" authors: Hao He, Xueying Liu, Chris J. Kuhlman, Xinwei Deng year: 2026 venue: arXiv url: https://arxiv.org/abs/2607.06413 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-07-07 updated_at: 2026-07-07 status: queued relevance: high topics:

• agent-evaluation
• coding-agent
• reasoning methods:

benchmarks:

models:

datasets:

• stat.ME
• cs.AI related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 16 collection_queries: agentic-ai, coding-agent

One-line Takeaway

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

Why Collected

• matched queries: agentic-ai, coding-agent
• inferred topics: agent-evaluation, coding-agent, reasoning
• arXiv categories: stat.ME, cs.AI
• collection score: 16

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/2607.06413