Reproduce “BaRe-Mem: Bayesian Reliability Memory for Robust and Adaptive Agent Consultation” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Peilin Feng, Zhengyang Huang, Soujanya Poria
BaRe-Mem helps an AI agent decide which peer advice to trust and whether to consult at all. It uses a frozen central model’s representations and an online Bayesian reliability memory to weight candidate answers and compare consultation with autonomous reasoning. On Qwen3-14B’s challenging suite with nominally 100% misleading advice, it reaches 68.6% accuracy, but this controlled result does not establish robustness in every deployment setting.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.