Aligning the world model with the environment for the agent's specific task is

This enables more efficient data encoding and precise retrieval, significantly reducing prompt lengths and mitigating information loss. We have developed two new million-token benchmarks from the Arcade and BIRD-SQL datasets to thoroughly evaluate TableRAG's effectiveness at scale.

In this paper, we explore this novel task, which attempts to learn a generator directly from a pre-trained classifier, without the assistance of any training data.

In hacker spaces and at their homes, creative protesters are laser-cutting and 3D-printing tools to resist an occupation. As the US government's immigration crackdown expands across the country, anxious residents have mobilized to look out for each other. One way they're doing that is by finding ways to build the tools they need to be resilient against the surge of Immigration and Customs Enforcement agents empowered to kill with impunity . All over the country, makers are 3D-printing thousands of whistles to help people on the ground alert others to nearby ICE activity. But the whistles are far from the only tools being used to respond to the surge of federal agents.

In this work we study the problem of actively learning binary classifiers from a given concept class, i.e., learning by utilizing unlabeled data and submitting targeted queries about their labels to a domain expert. We evaluate the quality of our solutions by considering the learning curves they induce, i.e., the rate of