Bias Busters: Teaching Language Agents to Think Like Scientists
In the latest paper “Language Agents Mirror Human Causal Reasoning Biases” (Chen et al., 2025), researchers uncovered a persistent issue affecting even the most advanced language model (LM) agents: a disjunctive bias—a tendency to prefer “OR”-type causal explanations over equally valid or even stronger “AND”-type ones. Surprisingly, this mirrors adult human reasoning patterns and undermines the agents’ ability to draw correct conclusions in scientific-style causal discovery tasks. ...