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Learning Calibrated Confidence for Multi-Agent Collaboration

Paper recorded by Signals 4 on 2026-10-29 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-29 on arXiv · recorded by Signals 4 on 2026-10-30

Category: cs.LG · 机器学习 · first seen 2026-10-30

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,

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#126 most recent of 737 cs.LG papers we have recorded · ↑ newer: Low-Bit GDN Recurrent-State Quantization: Global Gradient-aware Alloca · ↓ older: CovFedAvg: Local Low-Loss Curves and Federated Model Selection
Cite this page: Learning Calibrated Confidence for Multi-Agent Collaboration: the #126 most recent of 737 cs.LG papers we have recorded (as of 2026-10-29). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/learning-calibrated-confidence-for-multi-agent-collaboration.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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