Papers by Kehan Jiang
ChatMap: Mining Human Thought Processes for Customer Service Chatbots via Multi-Agent Collaboration (2025.findings-acl)
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Xinyi Jiang, Tianyi Hu, Yuheng Qin, Guoming Wang, Zhou Huan, Kehan Chen, Gang Huang, Rongxing Lu, Siliang Tang
| Challenge: | Existing methods for enhancing dialogue performance rely on summarizing behavior . e-commerce chatbots need to align their dialogue strategies with human behavior to achieve coherent, human-like conversations with customers. |
| Approach: | They propose a method to extract core patterns from dialogue data and integrate them into models by mining service thought processes using a multi-agent aPproach. |
| Outcome: | The proposed method outperforms manual methods and outperfies baselines on Taobao in China. |
FoE: Forest of Errors Makes the First Solution the Best in Large Reasoning Models (2026.acl-long)
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| Challenge: | Recent Large Reasoning Models (LRMs) have demonstrated remarkable success in complex reasoning tasks. |
| Approach: | They propose a self-guided efficient reasoning framework that reduces FoE by pruning subs. |
| Outcome: | The proposed model outperforms eight competitive baselines while reducing token consumption by 37.7% 70.4%. |
NeuReasoner: Towards Explainable, Controllable, and Unified Reasoning via Mixture-of-Neurons (2026.acl-long)
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| Challenge: | Existing Large Reasoning Models (LRMs) lack explainability and controllability . Existing models target isolated levels without unification, while relying on RL . |
| Approach: | They propose an explainable, controllable, and unified reasoning framework driven by MoN. |
| Outcome: | The proposed framework achieves performance gains of 27.0% while reducing token consumption by 19.6% 63.3%. |