KISS - Knowledge Infrastructure for Scientific Simulation: A Scaffolding for Agentic Earth Science

• URL: http://arxiv.org/abs/2605.17856v1
• Date: Mon, 18 May 2026 04:57:25 GMT
• Title: KISS - Knowledge Infrastructure for Scientific Simulation: A Scaffolding for Agentic Earth Science
• Abstract summary: Knowledge infrastructure (KI) externalizes expertise into validated modelling operators.<n>Agents equipped with KI produced physically plausible, verifiable end-to-end simulations in up to 84% of trials.<n>Across all 119 KIs, modelling decisions and failure remedies converged despite different underlying physics.
• Score: 9.554707285564593
• License: http://creativecommons.org/licenses/by/4.0/
• 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. Here, we introduce knowledge infrastructure (KI), an agent-actionable scaffold that externalizes expertise into validated modelling operators, staged domain protocols, and diagnostic recovery mechanisms. Across a 3,000-trial coupled-hydrology benchmark, agents equipped with KI produced physically plausible, verifiable end-to-end simulations in up to 84% of trials, while agents without KI plateaued below 40%. KI generalizes across disciplines. We packaged its construction into a Knowledge Dissection Toolkit (KDT) that autonomously produced KI enabling end-to-end agent execution of 117 additional process-based models across 14 Earth-science domains. Across all 119 KIs, modelling decisions and failure remedies converged despite different underlying physics, showing that operational expertise is structured and extractable rather than ad hoc. Demonstrations show KI-equipped agents lowering both the access barrier between non-specialist users and process-based simulation, and the integration barrier between modelling communities. Through this scaffold, process-based science can then evolve as a living scientific commons, answerable to whoever needs to know and extendable by whoever can contribute.

Related papers

• EarthVerse: Benchmarking Scientific Agents Across Dynamic Earth Systems and Natural Hazards [73.12811119273206]
  We introduce EarthVerse, a benchmark that evaluates scientific agents through package-scoped investigations.<n>Agents inspect heterogeneous event packages, choose compatible evidence, execute transparent calculations, reconcile source differences, and preserve provenance in the final answer.<n>Across systems, the best mean answer-unit accuracy is 84.65%, while the highest Strict@95 is only 34.81%.
  arXiv  Detail & Related papers  (2026-08-24T17:29:16Z)
• Science Earth: Towards A Planet-Scale Operating System for AI-Native Scientific Discovery [51.70607714288616]
  Science Earth is a planet-scale scientific runtime in which any capability can connect to any other.<n>Its underlying EACN protocol lets capabilities discover one another, negotiate task ownership, and adjudicate across incompatible evidentiary standards.<n>Two runs validate this under structurally distinct conditions.
  arXiv  Detail & Related papers  (2026-05-31T16:05:41Z)
• SciResearcher: Scaling Deep Research Agents for Frontier Scientific Reasoning [54.194672921128785]
  We introduce SciResearcher, a fully automated agentic framework for frontier-science data construction.<n>SciResearcher synthesizes diverse conceptual and computational tasks grounded in academic evidence.<n>We develop SciResearcher-8B, an agent foundation model that achieves 19.46% on the HLE-Bio/Chem-Gold benchmark.
  arXiv  Detail & Related papers  (2026-05-02T15:26:45Z)
• AI scientists produce results without reasoning scientifically [3.100302590436282]
  Large language model (LLM)-based systems are increasingly deployed to conduct scientific research autonomously.<n>Here, we evaluate LLM-based scientific agents across eight domains, spanning execution to hypothesis-driven inquiry.
  arXiv  Detail & Related papers  (2026-04-20T20:23:42Z)
• CASCADE: Cumulative Agentic Skill Creation through Autonomous Development and Evolution [7.266404572341558]
  Large language model (LLM) agents currently depend on predefined tools or brittle tool generation.<n>We introduce CASCADE, a self-evolving agentic framework representing an early instantiation of the transition from "LLM + tool use" to "LLM + skill acquisition"
  arXiv  Detail & Related papers  (2025-12-29T21:50:23Z)
• Bohrium + SciMaster: Building the Infrastructure and Ecosystem for Agentic Science at Scale [82.20980951765891]
  We argue that scaling agentic science requires an infrastructure-and-ecosystem approach, instantiated Bohrium+SciMaster.<n>Bohrium acts as a managed, traceable hub for AI4S assets that turns diverse scientific data, software, compute, and laboratory systems into agent-ready capabilities.<n>SciMaster orchestrates these capabilities into long-horizon scientific, on which scientific agents can be composed and executed.
  arXiv  Detail & Related papers  (2025-12-23T16:04:41Z)
• Probing Scientific General Intelligence of LLMs with Scientist-Aligned Workflows [203.3527268311731]
  We present an operational SGI definition grounded in the Practical Inquiry Model (PIM)<n>We operationalize it via four scientist-aligned tasks: deep research, idea generation, dry/wet experiments, and experimental reasoning.<n>Our PIM-grounded definition, workflow-centric benchmark, and empirical insights establish a foundation for AI systems that genuinely participate in scientific discovery.
  arXiv  Detail & Related papers  (2025-12-18T12:44:36Z)
• TusoAI: Agentic Optimization for Scientific Methods [16.268579802762247]
  Large language models (LLMs) have demonstrated strong capabilities in synthesizing literature, reasoning with empirical data, and generating domain-specific code.<n>Here, we introduce TusoAI, an agentic AI system that takes a scientific task description with an evaluation function.<n>TusoAI integrates domain knowledge into a knowledge tree representation and performs iterative, domain-specific optimization and model diagnosis.
  arXiv  Detail & Related papers  (2025-09-28T17:30:44Z)
• ScienceBoard: Evaluating Multimodal Autonomous Agents in Realistic Scientific Workflows [82.07367406991678]
  Large Language Models (LLMs) have extended their impact beyond Natural Language Processing.<n>Among these, computer-using agents are capable of interacting with operating systems as humans do.<n>We introduce ScienceBoard, which encompasses a realistic, multi-domain environment featuring dynamic and visually rich scientific software.
  arXiv  Detail & Related papers  (2025-05-26T12:27:27Z)
• Kolb-Based Experiential Learning for Generalist Agents with Human-Level Kaggle Data Science Performance [81.05882480184587]
  We propose a computational framework of Kolb's learning cycle with Vygotsky's ZPD for autonomous agents.<n>Agent K is the 1st AI system to successfully integrate Kolb- and Vygotsky-inspired human cognitive learning.<n>With 9 gold, 8 silver, and 12 bronze medals level performance - including 4 gold and 4 silver on prize-awarding competitions - Agent K is the 1st AI system to successfully integrate Kolb- and Vygotsky-inspired human cognitive learning.
  arXiv  Detail & Related papers  (2024-11-05T23:55:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.