Reproduce “Streaming Failure Forecasting: Closed-Form Online Learning of VLA Failure Monitors Under Drift” 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
Amit Jadhav, Shaurya Beriwala, David Q. Liu, Yanfei Liu
SFF adapts a robot’s failure monitor after each episode by using its success or failure label to refit only the final linear head, while keeping the policy and hidden layers fixed; a separate threshold update after successes manages false alarms. On SmolVLA’s LIBERO-10 tasks, AUROC rises from 0.707 to 0.736, a qualified gain whose two-sided 95% interval reaches zero.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.