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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.
Proposes a framework for selecting complementary LLMs as proposers in ensemble systems, reformulating proposer selection as a combinatorial problem and exploring greedy algorithms for efficient performance-cost trade-offs.
This paper derives tight theoretical bounds for human-AI teams, proving when confidence-based aggregation leads to complementarity and establishing impossibility results under specific error correlations.