Interaction as Intelligence: Deep Research With Human-AI Partnership
Lyumanshan Ye, Xiaojie Cai, Xinkai Wang, Junfei Wang, Xiangkun Hu, Jiadi Su, Yang Nan, Sihan Wang, Bohan Zhang, Xiaoze Fan, Jinbin Luo, Yuxiang Zheng, Tianze Xu, Dayuan Fu, Yunze Wu, Pengrui Lu, Zengzhi Wang, Yiwei Qin, Zhen Huang, Yan Ma, Zhulin Hu, Haoyang Zou, Tiantian Mi

TL;DR
This paper proposes a new human-AI interaction paradigm called Deep Cognition, where interaction is integral to intelligence, enabling more flexible, transparent, and effective deep research processes through strategic human oversight.
Contribution
It introduces Deep Cognition, a system that transforms human roles into cognitive overseers with controllable, bidirectional interaction, improving research effectiveness over traditional input-output models.
Findings
Enhanced transparency (+20.0%)
Improved fine-grained interaction (+29.2%)
Significant performance gains on research tasks (31.8%-50.0%)
Abstract
This paper introduces "Interaction as Intelligence" research series, presenting a reconceptualization of human-AI relationships in deep research tasks. Traditional approaches treat interaction merely as an interface for accessing AI capabilities-a conduit between human intent and machine output. We propose that interaction itself constitutes a fundamental dimension of intelligence. As AI systems engage in extended thinking processes for research tasks, meaningful interaction transitions from an optional enhancement to an essential component of effective intelligence. Current deep research systems adopt an "input-wait-output" paradigm where users initiate queries and receive results after black-box processing. This approach leads to error cascade effects, inflexible research boundaries that prevent question refinement during investigation, and missed opportunities for expertise…
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