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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Anticipating User Interpretations in Emotional Support Conversations via Inference-Time User-Side Simulation
%A Liu, Jiaqi
%A Xu, Jiakang
%A Du, Zhongqiang
%A Zheng, Haonan
%A Yang, Yankun
%A Jiang, Wenbin
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F liu-etal-2026-anticipating
%X 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.
%U https://aclanthology.org/2026.emnlp-main.207/
%P 4746-4779

Markdown (Informal)

[Anticipating User Interpretations in Emotional Support Conversations via Inference-Time User-Side Simulation](https://aclanthology.org/2026.emnlp-main.207/) (Liu et al., EMNLP 2026)

• Anticipating User Interpretations in Emotional Support Conversations via Inference-Time User-Side Simulation (Liu et al., EMNLP 2026)

ACL

• Jiaqi Liu, Jiakang Xu, Zhongqiang Du, Haonan Zheng, Yankun Yang, and Wenbin Jiang. 2026. Anticipating User Interpretations in Emotional Support Conversations via Inference-Time User-Side Simulation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 4746–4779, Budapest, Hungary. Association for Computational Linguistics.