Dialogue systems typically optimize the immediate quality of a response, but rarely anticipate how that response will be interpreted by the user. This is especially important in emotional support conversations, where a locally plausible reply may still be perceived as judgmental, dismissive, pressuring, or misaligned with the user’s current need. We frame this as a problem of candidate response evaluation under user-side interpretation uncertainty. We propose Cognitive Look-ahead Simulation (CLS), an inference-time framework that previews likely user interpretations before committing to the final response. CLS expands single-pass generation into a Generate–Simulate–Evaluate–Revise pipeline: it first infers a Structured User-State Representation, then uses a Cognitive World Model as an operational one-step predictor of the user’s immediate inner thought and next-turn state under a candidate reply, and finally applies selective revision to high-risk drafts. We evaluate CLS on the SAGE benchmark using simulator-based outcomes and LLM-as-a-Judge capability profiling, with complementary human evaluations. Across multiple backbones, CLS improves dialogue outcomes and reduces severe failures compared with direct generation and self-refinement baselines. These results suggest that anticipating user interpretations provides a useful inference-time signal for identifying and revising high-risk responses in emotional support conversations.