From Intent to AI Pipelines: A Controlled Agentic Framework for Non-AI Expert Scientists
The paper introduces Domain-Driven Adaptable AI Pipelines (DDAP), a framework designed to assist non-expert scientists in creating AI solutions. DDAP guides users through a structured process that includes problem definition, environment specification, pipeline generation, and code generation. Experimental results indicate that DDAP can produce competitive AI models across various domains, although performance may vary by task type.
- ▪DDAP is a controlled, human-in-the-loop framework that utilizes large language models.
- ▪The framework is structured into four stages: problem definition, compute environment specification, pipeline generation, and code generation.
- ▪DDAP has been evaluated across multiple datasets in business, biology, and health science domains, achieving competitive results compared to expert-developed models.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.18764 |
| Publication time | Wed, 20 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-20T04:04:59.484Z |
| Last seen | 2026-05-20T04:04:59.484Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 6D0OKOHjRw51 |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Information Retrieval arXiv:2605.18764 (cs) [Submitted on 10 Apr 2026] Title:From Intent to AI Pipelines: A Controlled Agentic Framework for Non-AI Expert Scientists Authors:Hyacinth Ali, Jessie Galasso-Carbonnel, Houari Sahraoui View a PDF of the paper titled From Intent to AI Pipelines: A Controlled Agentic Framework for Non-AI Expert Scientists, by Hyacinth Ali and 2 other authors View PDF HTML (experimental) Abstract:Artificial Intelligence (AI) pipelines have become integral to modern research, supporting fields such as Medical Sciences, Agriculture, and Social Sciences, and enabling large-scale data analysis, predictive modeling, and the automation of complex tasks.
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