Adaptive Complementarity in Human-AI Systems: Architecture as a State-Shaping Choice

2026-09-07

AI Summary

论文提出自适应互补框架，通过调整信息暴露、任务分配等来优化人机交互架构，以提高当前和未来的性能。

Abstract

Human-AI interaction can improve current performance while changing the capabilities and relationships on which future performance depends. We develop adaptive complementarity, a framework for choosing interaction architecture with these state consequences in view. Access, information exposure, task allocation, timing, and communication can alter which arrangement will be valuable later; their settings can often be reset faster than the capabilities, search patterns, or conventions they create. Three mechanisms organize the argument: information exposure and collective search, delegation and capability evolution, and strategic interdependence and information governance. Their integration yields cross-mechanism implications, including conditions under which a loss of expertise heterogeneity increases the information differentiation required to preserve independent search. We distinguish strong human-AI complementarity from advantage over another workflow and from advantage over an evolving reference policy. A knowledge-coverage illustration shows how different interaction histories can reverse current workflow rankings even at equal human competence. It also separates that result from the incremental value of state feedback, which can be small when a well-chosen stable workflow anticipates learning. The framework directs evaluation toward the states present interaction creates, their consequences for later architectural fit, and the conditions under which observing and responding to them is worthwhile.

Problem

• Human-AI interaction
• adaptive complementarity
• interaction architecture
• state consequences
• performance

Innovation

• Adaptive Complementarity
• Interaction Architecture
• State Consequences
• Mechanism Integration
• Workflow Evaluation