Detection Limits, Comma Decimals and Other Geochemistry Data Traps for AI Agents
Geochemistry data cleaning for AI agents: why "<0.005" and comma decimals break code, and how our harness diagnoses data before cleaning, without guessing.

Geochemistry data cleaning for AI agents: why "<0.005" and comma decimals break code, and how our harness diagnoses data before cleaning, without guessing.

Georeferencing scanned maps with an affine fit from ground control points, and why a leave-one-out check catches a misread coordinate that least squares hides.

Agents with file and shell tools can reach the grading rubric on disk. What we measured across 50 questions, which fixes failed, and what the harness does now.

Why RL breaks down for recursive self-improvement at scale, and the two paths around it after six years building continuously-learning models.

OpenAI's Navier–Stokes proof drew accusations that it front-ran independent mathematicians. A warning about doing research on AI you don't control.

A self-improving AI agent architecture needs an actor, a memory store, an evaluator and a way to undo. Where each breaks, and why the evaluator rules.

Self-improving AI systems that revise their own harness, weights or data: an evidence audit of what has been shown by 2026, and what still has not.

Learn how to build a new world model via coherence mapping by connecting geological observations, reducing uncertainty, and finding relationships that explain mineral systems.

Best AI for Geology Testing. See how Groundtruth's dynamic benchmark tests models on your own data, comparing accuracy, cost, and performance.

Learn five harness design principles to avoid building a slop cannon, plus a real-world mining industry case study on improving AI agent output.