Agentic self-driving microscopy benchmarks support qualification but do not necessarily generalize to unseen tasks

arXiv:2608.05266 · cs.AI, cond-mat.mtrl-sci, cs.LG · Submitted 2026-08-05 · Read on arXiv

Nathan S Johnson, Ian Abshire

cs.AI, cond-mat.mtrl-sci, cs.LG

Submitted: 2026-08-05

Comments: 20 pages, 8 figures

Code: https://github.com/natertott/agentic

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 64× in this paper · explained in 2 episodes
• RAG — 57× in this paper · explained in 3 episodes
• Trace — 14× in this paper · explained in 2 episodes
• AUC — 6× in this paper · explained in 2 episodes
• Pruning — 6× in this paper · explained in 4 episodes
• Benchmarking — 5× in this paper · explained in 4 episodes
• Model Context Protocol — 4× in this paper
• Surrogate Model — 4× in this paper · explained in 3 episodes
• Surrogate Models — 4× in this paper · explained in 3 episodes
• Calibration — 3× in this paper · explained in 9 episodes
• Generalization — 3× in this paper · explained in 14 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Agentic System — 2× in this paper · explained in 2 episodes
• Multi-Agent System — 2× in this paper · explained in 7 episodes
• Retrieval-Augmented Generation (RAG) — 2× in this paper · explained in 21 episodes
• AI Agents — 1× in this paper · explained in 2 episodes
• Active Learning — 1× in this paper · explained in 6 episodes
• Adam — 1× in this paper · explained in 3 episodes
• Editor — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Feature Importance — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Large Language Model (LLM) — 1× in this paper · explained in 3 episodes
• Model Context Protocol (MCP) — 1× in this paper · explained in 8 episodes

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