← 研踪 PaperTrailarXiv:2602.19458 · v2
ComplLLM: Fine-tuning LLMs to Discover Complementary Signals for Decision-making
首次提交 2026-02-23 11:01
版本更新 2026-11-08 02:52
雷达观测 2026-11-10 11:14
摘要
Multi-agent decision pipelines can outperform single agent workflows when complementarity holds, i.e., different agents bring unique information to inform a final decision. We propose ComplLLM, a post-training framework based on decision theory that fine-tunes a decision-assistant LLM to output signals that complement existing agent decisions, using complementary information as reward. We validate ComplLLM on synthetic and real-world tasks involving domain experts, demonstrating how the approach recovers known complementary information, produces plausible explanations of complementary signals to support downstream decision-makers, and improves human and LLM agent decision performance in controlled studies.
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v2 · 2026-11-08 02:52 · 最新观测版本
ComplLLM: Fine-tuning LLMs to Discover Complementary Signals for Decision-making
Multi-agent decision pipelines can outperform single agent workflows when complementarity holds, i.e., different agents bring unique information to inform a final decision. We propose ComplLLM, a post-training framework based on decision theory that fine-tunes a decision-assistant LLM to output signals that complement existing agent decisions, using complementary information as reward. We validate ComplLLM on synthetic and real-world tasks involving domain experts, demonstrating how the approach recovers known complementary information, produces plausible explanations of complementary signals to support downstream decision-makers, and improves human and LLM agent decision performance in controlled studies.
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