Samsung Galaxy Unpacked 2026 is Feb. 25 Valve's Steam Machine: Everything we know'Peter Steinberger is joining OpenAI to drive the next generation of personal agents.' Recently we were introduced to OpenClaw, an AI that allows users to create their own agents to control apps like email, Spotify and home controls. Now, Sam Altman has announced that OpenAI has absorbed OpenClaw by hiring developer Peter Steinberger to drive the next generation of personal agents, he wrote on X . Steinberger confirmed the news on his own blog . I'm joining OpenAI to work on bringing agents to everyone.

Before deploying outputs from foundation models in high-stakes tasks, it is imperative to ensure that they align with human values.

Transformer-based architectures have recently exhibited remarkable successes across different domains beyond just powering large language models. However, existing approaches typically focus on predictive accuracy and computational cost, largely ignoring certain other practical issues such as robustness to contaminated samples. In this paper, by re-interpreting the self-attention mechanism as a non-parametric kernel density estimator, we adapt classical robust kernel density estimation methods to develop novel classes of transformers that are resistant to adversarial attacks and data contamination. We first propose methods that down-weight outliers in RKHS when computing the self-attention operations. We empirically show that these methods produce improved performance over existing state-of-the-art methods, particularly on image data under adversarial attacks. Then we leverage the median-of-means principle to obtain another efficient approach that results in noticeably enhanced performance and robustness on language modeling and time series classification tasks. Our methods can be combined with existing transformers to augment their robust properties, thus promising to impact a wide variety of applications.

In this work, we investigate whether analogical reasoning can enable in-context composition over composable elements of visual stimuli.

We revisit this scenario as an experimental case study in Section 6.

These additional behavior tokens will be augmented to the vocabulary of pretrained Multimodal Language Models. With this encoder, we then pack long-term multimodal interactions involving task instructions, memories, thoughts, observations, textual responses, behavior trajectories, etc .