While large language models (LLMs) are equipped with longer text input capabilities than
before, they are struggling to seek correct information in long contexts. The" lost in the …

The advent of large language models (LLMs) has opened up new opportunities in the field
of mobile task automation. Their superior language understanding and reasoning …

Learning from demonstrations is a common way for users to teach robots, but it is prone to
spurious feature correlations. Recent work constructs state abstractions, ie visual …

Mathematics is one of the most powerful conceptual systems developed and used by the
human species. Dreams of automated mathematicians have a storied history in artificial …

People can easily evoke previously learned concepts, compose them, and apply the result
to solve novel tasks on the first attempt. The aim of this paper is to improve our …

Making plans for upcoming actions is a computationally demanding process. To mitigate
these demands, agents can build representations–of states, actions, and their sequential …

Recent investigation on reinforcement learning (RL) has demonstrated considerable
flexibility in dealing with various problems. However, such models often experience difficulty …

Real-world multi-modal problems are rarely solved by a single machine learning model, and
often require multi-step computational plans that involve stitching several models. Tool …

Human behavior is inherently hierarchical, resulting from the decomposition of a task into
subtasks or an abstract action into concrete actions. However, behavior is typically …

A large program of research has aimed to ground large-scale cultural phenomena in
processes taking place within individual minds. For example, investigating whether …