Multi-agent decision pipelines can outperform single agent workflows when complementarity holds, i.e., different agents bring unique information to the table to inform a final decision. We propose ComplLLM, a post-training framework based on decision theory that fine-tunes a decision-assistant LLM using complementary information as reward to output signals that complement existing agent decisions. We validate ComplLLM on synthetic and real-world tasks involving domain experts, demonstrating how the approach recovers known complementary information and produces plausible explanations of complementary signals to support downstream decision-makers.
@article{arxiv.2602.19458,
title = {ComplLLM: Fine-tuning LLMs to Discover Complementary Signals for Decision-making},
author = {Ziyang Guo and Yifan Wu and Jason Hartline and Kenneth Holstein and Jessica Hullman},
journal= {arXiv preprint arXiv:2602.19458},
year = {2026}
}