MineTRACE: An Evidence-Grounded Interactive Reasoning System for Mineral Prospectivity
arXiv cs.AIen
arXiv:2609.02060v1 Announce Type: new Abstract: Mineral exploration requires integrating heterogeneous geochemical, geophysical, and geological evidence, yet existing prospectivity systems often provide only opaque scores or heatmaps. We present MineTRACE, a web-based system for evidence-grounded exploration of eight commodities: Cu, Au, Ni, W, Sn, Co, Ta, and Mn. Users can explore prospectivity maps, query locations or regions, inspect supporting evidence, and interact through natural language. A transparent expert tree, informed by geological knowledge and known deposits, combines multi-source evidence into interpretable prospectivity scores. For a new location, the conversational assistan
This is a short summary published by AI Global Wire. The full article is owned and hosted by arXiv cs.AI — open it there to read it in full.
Read the full story at arXiv cs.AI- Forskning
Related AI news
- Architecting Conversational Data Systems for Stateless LLM APIs: The Hydration Proxy PatternarXiv cs.AI · September 3, 2026
- FUSE: An Evaluating Framework for Dangerous Capabilities of LLMsarXiv cs.AI · September 3, 2026
- Looped Transformers under the Jacobian Lens: Does the Global Workspace Survive Recurrence?arXiv cs.AI · September 3, 2026
- When Does Information Sharing Improve Decentralized Discovery? Aggregation, Independent Rescue, and Equilibrium SelectionarXiv cs.AI · September 3, 2026
- READY or Not: Reliable Enterprise Agent DeploymentarXiv cs.AI · September 3, 2026
- When Can a Machine Trust a Statute? A Survival Certificate for Machine-Extracted Legal LogicarXiv cs.AI · September 3, 2026