Declarative Data Services: Structured Agentic Discovery for Composing Data Systems
The article discusses a new framework called Declarative Data Services (DDS) aimed at improving the composition of data systems through structured agentic discovery. It highlights the challenges faced by existing methods in converging on effective data stacks and proposes a solution that breaks down the search process into manageable parts. The authors present DDS as a prototype that has shown promise in real-world applications, particularly in trading-backend workloads.
- ▪Declarative Data Services (DDS) is designed for structured agentic discovery of data-system compositions.
- ▪The framework addresses the challenges of heterogeneous search spaces and uneven composition knowledge.
- ▪DDS has demonstrated success in converging on working stacks where previous methods failed.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20690 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
| 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 | PR60DNUfL5ED |
| 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.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.20690 (cs) [Submitted on 20 May 2026] Title:Declarative Data Services: Structured Agentic Discovery for Composing Data Systems Authors:Shanshan Ye, Duo Lu View a PDF of the paper titled Declarative Data Services: Structured Agentic Discovery for Composing Data Systems, by Shanshan Ye and 1 other authors View PDF HTML (experimental) Abstract:Agentic discovery has shown that LLM-driven search can find novel algorithms, designs, and code under benchmark conditions. Translating the paradigm to multi-system data backends surfaces a harder problem: the search space is heterogeneous, the verifier is whether a deployed stack actually runs, and composition knowledge is unevenly captured in pretraining.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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