AI agents are a risky business. Even when stuck inside the chatbox window, LLMs will make mistakes and behave badly. Once they have tools that they can use to interact with the outside world, such as web browsers and email addresses, the consequences of those mistakes become far more serious. That might explain why the first breakthrough LLM personal assistant came not from one of the major AI labs, which have to worry about reputation and liability, but from an independent software engineer, Peter Steinberger. In November of 2025, Steinberger uploaded his tool, now called OpenClaw, to GitHub, and in late January the project went viral.

Here, θ() is the clique coveringnumberofthegraph. Online learning models a repeated interaction between a learner and an environment.

These metrics admit differentiable sample estimates, making it easy to incorporate a calibration objective into empirical risk minimization.

Severe data imbalance naturally exists among web-scale vision-language datasets.

However, these works face some limitations and challenges.

Firstly, SGTs ( i.e., simple GNNs) are still powerful and can "distinguish almost all non-isomorphic graphs" [ VQ-V AE (Table 3b) emphasizes the impact of pretraining methods on the tokenizer's performance. We leave the investigation of how to effectively pretrain GNN-based tokenizers as future works. We have included the literature review of MGM in the main body of the paper. However, a closer inspection reveals several critical distinctions between MGM and these methods. Finally, MGM employs remask decoding to constrain the encoder's ability on This code uses a single-layer SGT of GIN as an example.