Agents for Experiments, Experiments for Agents: A Design Grammar for AI-Enabled Experimental Science
The paper discusses the role of AI systems in experimental science, emphasizing their interaction with humans and workflows. It introduces a framework called SEED, which represents experimental conditions as actor-flow graphs to improve design processes. The authors highlight the importance of governance and accountability in AI-enabled knowledge production.
- ▪AI systems are becoming active participants in organizational and knowledge work.
- ▪The SEED framework supports describing conditions, evaluating structural novelty, and generating candidate designs.
- ▪A feasibility test showed that SEED-guided designs had clearer actor-flow changes and governance checks.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.17746 (cs) [Submitted on 18 May 2026] Title:Agents for Experiments, Experiments for Agents: A Design Grammar for AI-Enabled Experimental Science Authors:Yingjie Zhang, Chun Feng, Weizhang Zhu, Tianshu Sun View a PDF of the paper titled Agents for Experiments, Experiments for Agents: A Design Grammar for AI-Enabled Experimental Science, by Yingjie Zhang and 3 other authors View PDF HTML (experimental) Abstract:AI systems are becoming active participants in organizational and knowledge work. They increasingly interact with humans, coordinate workflows, and operate in multi-agent arrangements.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
Discussion
0 commentsMore from arXiv cs.AI
