Inference-Time Self-Guidance for Conversational Safety in Delusion-Related Interactions

Background: Large language models (LLMs) are increasingly used in psychologically sensitive conversations, where repeated affirmation may reinforce delusion-related beliefs and associated risk. Inference-time self-guidance, in which a model generates conversation-specific guidance for subsequent responses without parameter updates, may offer an approach to conversational safety, but its utility in delusion-related interactions remains unclear.Objective: To develop and evaluate conversation-specific inference-time self-guidance for conversational safety in delusion-related interactions.Methods: In this preregistered study, Gemini 2.5 Pro, GPT-5, and DeepSeek V4 Flash completed 16 Psychosis-Bench scenarios under Baseline and three self-guidance conditions: response rules, contrastive examples, and short-horizon consequence simulation. The fully crossed design yielded 192 conversations and 2,304 assistant turns. A separate automated judge scored Delusion Confirmation (DCS), Harm Enablement (HES), and Safety Intervention (SIS) using published Psychosis-Bench rubrics. Analyses compared benchmark-defined Explicit and Implicit risk-cue scenarios and examined whether self-guidance reduced this gap.Results: Across models, all three self-guidance conditions significantly reduced delusion confirmation and harm enablement and increased safety intervention relative to Baseline (all nine PFDR<.001; dz = 1.10–2.17). At Baseline, explicit risk cues elicited significantly greater SIS than matched implicit cues, whereas cue-type differences in DCS and HES were not significant. Self-guidance did not significantly attenuate the explicit–implicit safety gap after correction for multiple comparisons.Conclusions: Model-generated inference-time self-guidance improved benchmark-defined safety behavior during standardized scripted escalation without parameter updates. These findings support further evaluation of inference-time self-guidance for delusion-related conversational safety, particularly when risk is communicated indirectly.