KISS - Knowledge Infrastructure for Scientific Simulation: A Scaffolding for Agentic Earth Science
The article discusses the development of the Knowledge Infrastructure for Scientific Simulation (KISS), aimed at improving access to process-based simulation models in Earth sciences. It highlights the effectiveness of KISS in enabling agents to produce verifiable simulations, significantly outperforming those without the infrastructure. The authors propose that KISS can facilitate collaboration and knowledge sharing across various scientific disciplines.
- ▪KISS is designed to make process-based simulation models more accessible to communities at risk from climate change and resource scarcity.
- ▪Agents equipped with KISS achieved successful simulations in up to 84% of trials, compared to less than 40% for those without it.
- ▪The Knowledge Dissection Toolkit (KDT) allows for the autonomous creation of KIs for various Earth-science models.
arXiv cs.AI files mainly under ai research. We currently carry 1,128 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.17856 |
| Publication time | Tue, 19 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-19T04:04:57.272Z |
| Last seen | 2026-05-19T04:04:57.272Z |
| 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 | Qw0rdzHemFm1 |
| 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 |
Rights status (four layers)
WeSearch handling by dimension
| 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:2605.17856 (cs) [Submitted on 18 May 2026] Title:KISS - Knowledge Infrastructure for Scientific Simulation: A Scaffolding for Agentic Earth Science Authors:Ziwei Li, Liujun Zhu, Yuchen Liu, Yichen Zhao, Birk Li, Ruiqi Wu, Junliang Jin, Jianyun Zhang View a PDF of the paper titled KISS - Knowledge Infrastructure for Scientific Simulation: A Scaffolding for Agentic Earth Science, by Ziwei Li and 7 other authors View PDF HTML (experimental) Abstract:Process-based simulation models encode decades of scientific understanding across the Earth sciences, yet the communities most exposed to climate risk and resource scarcity are the least able to use them.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
Discussion
0 commentsMore from arXiv cs.AI
