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This paper introduces a tree-based formal framework for modeling complementarity in multi-agent human-AI interactions, proving that complementarity is attainable in regression but obstructed in classification under natural conditions on local aggregation and loss functions.
Context-Agent proposes a novel framework that models multi-turn dialogue history as dynamic tree structures rather than flat sequences, better capturing the hierarchical and branching nature of natural conversation. The paper introduces the NTM benchmark for evaluating non-linear dialogue scenarios and demonstrates improved task completion rates and token efficiency across various LLMs.