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Related Articles from SNS
How Users Understand Robot Foundation Model Performance through Task Success Rates and Beyond
Announce Type: replace Abstract: Robot Foundation Models (RFMs) represent a promising approach to developing general-purpose home robots. Given the broad capabilities of RFMs, users will inevitably ask an RFM-based robot to perform tasks that the RFM was not trained or evaluated on. In these cases, it is crucial that users understand the risks associated with attempting novel tasks due to the relatively high cost of failure.
Tonight, we’re staying in: Patrick Bruel cancels his summer shows
The 67-year-old actor and singer faces a growing number of rape and assault complaints. Several French mayors had urged him not to perform in their towns, and three Quebec shows scheduled for December were cancelled on 19 May. The discontent of municipalities and of performances disrupted by feminist activists (source in French), not to mention a rising tide of outrage on social media, have finally prevailed.
OpenRFM: Dissecting Relational In-Context Learning
arXiv:2606.04320v1 Announce Type: new Abstract: Relational Foundation Models (RFMs) promise a single pre-trained predictor that, given any relational database, returns predictions in one forward pass via relational in-context learning (ICL). Yet a substantial gap separates open RFMs from their commercial counterparts, and the origin of this gap has not been systematically understood. We dissect a representative framework, the Relational Transformer (RT), from two perspectives.