Learning Calibrated Confidence for Multi-Agent Collaboration
arXiv:2610.39927v1 Announce Type: cross Abstract: Complex information seeking on the Web often requires collecting evidence from multiple documents and reasoning across them, as in multi-hop question answering. Multi-agent systems address such tasks by dividing work among specialized agents, where upstream agents produce intermediate answers and supporting evidence that downstream agents build upon. However, upstream answers vary in correctness, and most systems provide no quantitative signal a…