The authors built BaRe-Mem, an online Bayesian regression mechanism that updates reliability estimates as interactions accumulate.
Bayesian reliability memory helps agents decide when to trust advice
BaRe-Mem learns task-dependent advisor reliability from past interactions, then controls how strongly agents use advice and whether they consult at all.
Top University
Peilin Feng · Zhengyang Huang · Soujanya Poria
DeCLaRe Lab, Nanyang Technological University · Peking University
Research Digest··2 min read
Feng, Huang and Poria introduce an online Bayesian memory that estimates advisor reliability using a central model’s internal belief representations and interaction history.
Why this paper
From DeCLaRe Lab, Nanyang Technological University and Peking University
In one line
BaRe-Mem uses Bayesian updates from past interactions to estimate advisor reliability, which then guides consultation decisions and response weighting.
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