TY - JOUR
T1 - Dual-stream Expert Reasoning for Maintaining Focus in Chinese Counseling Dialogues
AU - Deng, Yifan
AU - Li, Donghao
AU - Zhang, Jiarui
AU - Weng, Jinta
AU - Zhang, Xingsheng
AU - Hu, Yue
AU - Liu, Yanbing
AU - Huang, Heyan
N1 - Publisher Copyright:
© 2026 IEEE.
PY - 2026
Y1 - 2026
N2 - Sustaining dialogue focus remains a bottleneck for LLM-based counseling, where conventional linear generation paradigms conflate peripheral narrative noise with essential therapeutic topic, leading to narrative drift. To address this, we propose Dual-stream Expert Reasoning (DER), a framework that decomposes etiology into two orthogonal streams: the 4Ps framework (external) and the Insight Triangle (internal). Unlike serial reasoning, this parallel architecture isolates situational stressors from intrapsychic conflicts, effectively preventing information interference and error propagation. To synthesize these decoupled insights, a cross-stream fusion module cross-validates divergent perspectives to strictly anchor the dialogue response to the primary distress. To enable privacy-preserving local deployment, we further introduce a reasoning-aware distillation strategy that explicitly internalizes the expert's dual-stream reasoning trajectories into a parameter-efficient student model. Empirical results demonstrate that DER establishes a new state-of-the-art in focus maintenance, while the distilled model retains over 90% reasoning fidelity for efficient on-device inference.
AB - Sustaining dialogue focus remains a bottleneck for LLM-based counseling, where conventional linear generation paradigms conflate peripheral narrative noise with essential therapeutic topic, leading to narrative drift. To address this, we propose Dual-stream Expert Reasoning (DER), a framework that decomposes etiology into two orthogonal streams: the 4Ps framework (external) and the Insight Triangle (internal). Unlike serial reasoning, this parallel architecture isolates situational stressors from intrapsychic conflicts, effectively preventing information interference and error propagation. To synthesize these decoupled insights, a cross-stream fusion module cross-validates divergent perspectives to strictly anchor the dialogue response to the primary distress. To enable privacy-preserving local deployment, we further introduce a reasoning-aware distillation strategy that explicitly internalizes the expert's dual-stream reasoning trajectories into a parameter-efficient student model. Empirical results demonstrate that DER establishes a new state-of-the-art in focus maintenance, while the distilled model retains over 90% reasoning fidelity for efficient on-device inference.
KW - Data Distillation
KW - Dialogue Generation
KW - Dual-stream Reasoning
KW - Focus
KW - Large Language Models
UR - https://www.scopus.com/pages/publications/105044870486
U2 - 10.1109/CSCWD68734.2026.11581699
DO - 10.1109/CSCWD68734.2026.11581699
M3 - Conference article
AN - SCOPUS:105044870486
SN - 2835-639X
SP - 3539
EP - 3544
JO - Proceedings of the International Conference on Computer Supported Cooperative Work in Design, CSCWD
JF - Proceedings of the International Conference on Computer Supported Cooperative Work in Design, CSCWD
IS - 2026
T2 - 29th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2026
Y2 - 13 May 2026 through 15 May 2026
ER -