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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.04351 |
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| _version_ | 1866915917638664192 |
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| author | Ye, Wanghao Chen, Sihan Wang, Yiting He, Shwai Tian, Bowei Sun, Guoheng Wang, Ziyi Wang, Ziyao He, Yexiao Shen, Zheyu Liu, Meng Zhang, Yuning Feng, Meng Dong, Yifei Qian, Yanhong Wang, Yang Peng, Siyuan Dai, Yilong Duan, Zhenle Liu, Joshua Xiong, Lang Qin, Hanzhang Li, Ang |
| author_facet | Ye, Wanghao Chen, Sihan Wang, Yiting He, Shwai Tian, Bowei Sun, Guoheng Wang, Ziyi Wang, Ziyao He, Yexiao Shen, Zheyu Liu, Meng Zhang, Yuning Feng, Meng Dong, Yifei Qian, Yanhong Wang, Yang Peng, Siyuan Dai, Yilong Duan, Zhenle Liu, Joshua Xiong, Lang Qin, Hanzhang Li, Ang |
| contents | We present an LLM-powered social discovery platform that uses digital twins to autonomously evaluate interpersonal compatibility through behavioral simulation. The platform unifies three key pillars: (1) digital twins that engage in autonomous multi-turn conversations on behalf of users to estimate compatibility, (2) gamified territory conquest mechanics that incentivize real-world exploration and create organic settings for in-person encounters, and (3) AI companions that preserve persistent shared memory across devices. Built upon CogniPair's cognitive architecture (Ye et al., 2026), validated on the Columbia Speed Dating dataset (551 participants), our system extends prior simulation-only matching into a fully deployed social discovery environment. Through deployment, we derive empirical cost-quality baselines and identify fundamental scaling bottlenecks that remain hidden in component-level testing alone. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_04351 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Cognibit: From Digital Exhaustion to Real-World Connection Through Gamified Territory Control and LLM-Powered Twin Networking Ye, Wanghao Chen, Sihan Wang, Yiting He, Shwai Tian, Bowei Sun, Guoheng Wang, Ziyi Wang, Ziyao He, Yexiao Shen, Zheyu Liu, Meng Zhang, Yuning Feng, Meng Dong, Yifei Qian, Yanhong Wang, Yang Peng, Siyuan Dai, Yilong Duan, Zhenle Liu, Joshua Xiong, Lang Qin, Hanzhang Li, Ang Human-Computer Interaction We present an LLM-powered social discovery platform that uses digital twins to autonomously evaluate interpersonal compatibility through behavioral simulation. The platform unifies three key pillars: (1) digital twins that engage in autonomous multi-turn conversations on behalf of users to estimate compatibility, (2) gamified territory conquest mechanics that incentivize real-world exploration and create organic settings for in-person encounters, and (3) AI companions that preserve persistent shared memory across devices. Built upon CogniPair's cognitive architecture (Ye et al., 2026), validated on the Columbia Speed Dating dataset (551 participants), our system extends prior simulation-only matching into a fully deployed social discovery environment. Through deployment, we derive empirical cost-quality baselines and identify fundamental scaling bottlenecks that remain hidden in component-level testing alone. |
| title | Cognibit: From Digital Exhaustion to Real-World Connection Through Gamified Territory Control and LLM-Powered Twin Networking |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2604.04351 |