arXiv Machine Learning
CLIMATEAGENT: Multi-Agent Orchestration for Complex Climate Data Science Workflows
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv AI
Aug 12
DSAgentBench: Can Agents Automate End-to-End Data-Science Workflows in Real Computer Environments?
arXiv:2608. 10366v1 Announce Type: new Abstract: Real-world data science involves long-horizon workflows that span data wrangling, exploration, modeling, visualization, and validation, and require coordinated use of tools such as notebooks, IDEs, terminals, browsers, and databases within real operating environments.
arXiv AI
Jun 17
CMIP-Forge: An Agentic System that Retrieves, Computes, and Self-Reviews Climate Science
arXiv:2606. 17076v1 Announce Type: cross Abstract: The Coupled Model Intercomparison Project Phase 6 (CMIP6) has generated thousands of peer-reviewed publications documenting model configurations, evaluation procedures, emergent constraints, and projection uncertainties.
arXiv AI
Jun 12
TerraBench: Can Agents Reason Over Heterogeneous Earth-System Data?
arXiv:2606. 13148v1 Announce Type: new Abstract: Climate and environmental decision-making increasingly requires reasoning across heterogeneous inputs, including gridded physical data, satellite imagery, geospatial context, and simulator outputs.
arXiv AI
Sep 1
DS-Lighting: Making Agent Harnesses Explicit for Data-Science Automation
arXiv:2608.28590v1 Announce Type: new Abstract: Large Language Model (LLM) agents have shown promise for automating data-science workflows, yet their end-to-end performance depends critically on the...
arXiv AI
Jun 10
GCA Framework: A GCC Countries-Grounded Dataset and Agentic Pipeline for Climate Decision Support
arXiv:2604. 12306v3 Announce Type: replace-cross Abstract: Climate decision-making in the GCC states increasingly demands systems that can translate heterogeneous scientific and policy evidence into actionable guidance, yet general-purpose large language models (LLMs) remain weak both in region-specific climate knowledge and grounded interaction with geospatial and forecasting tools.
arXiv AI
1d ago
BobFlow: Towards Human-Agent Collaborative Data Science Using Dataflows
BobFlow proposes using dataflows as a shared computational abstraction to facilitate human-agent collaboration in data science, combining this with the ReAct paradigm to allow agents to iteratively build and run dataflows while analysts can inspect and provide feedback. The system demonstrates that analysts can better understand, inspect, and verify agent-generated analyses, and experimental results show accuracy gains of up to 18.6 points at roughly one‑third the inference cost of a script‑based ReAct agent.