[2604.08588] Act or Escalate? Evaluating Escalation Behavior in Automation with Language Models

Computer Science > Machine Learning arXiv:2604.08588 (cs) [Submitted on 31 Mar 2026] Title:Act or Escalate? Evaluating Escalation Behavior in Automation with Language Models Authors:Matthew DosSantos DiSorbo, Harang Ju View a PDF of the paper titled Act or Escalate? Evaluating Escalation Behavior in Automation with Language Models, by Matthew DosSantos DiSorbo and 1 other authors View PDF HTML (experimental) Abstract:Effective automation hinges on deciding when to act and when to escalate. We model this as a decision under uncertainty: an LLM forms a prediction, estimates its probability of being correct, and compares the expected costs of acting and escalating. Using this framework across five domains of recorded human decisions-demand forecasting, content recommendation, content moderation, loan approval, and autonomous driving-and across multiple model families, we find marked differences in the implicit thresholds models use to trade off these costs. These thresholds vary substantially and are not predicted by architecture or scale, while self-estimates are miscalibrated in model-specific ways. We then test interventions that target this decision process by varying cost ratios, providing accuracy signals, and training models to follow the desired escalation rule. Prompting helps mainly for reasoning models. SFT on chain-of-thought targets yields the most robust policies, which generalize across datasets, cost ratios, prompt framings, and held-out domains. These results...