In many areas, we have well-founded insights about causal structure that would be useful to
bring into our trained models while still allowing them to learn in a data-driven fashion. To …

Large language models (LLMs) have achieved remarkable success across a wide spectrum
of tasks; however, they still face limitations in scenarios that demand long-term planning and …

Compositional generalization benchmarks for semantic parsing seek to assess whether
models can accurately compute meanings for novel sentences, but operationalize this in …

A fundamental component of human vision is our ability to parse complex visual scenes and
judge the relations between their constituent objects. AI benchmarks for visual reasoning …

Path planning is a fundamental scientific problem in robotics and autonomous navigation,
requiring the derivation of efficient routes from starting to destination points while avoiding …

The literature on text-to-image generation is plagued by issues of faithfully composing
entities with relations. But there lacks a formal understanding of how entity-relation …

Investigating the reasoning abilities of transformer models, and discovering new challenging
tasks for them, has been a topic of much interest. Recent studies have found these models to …

Humans can reason compositionally whilst grounding language utterances to the real world.
Recent benchmarks like ReaSCAN use navigation tasks grounded in a grid world to assess …

The notion of equality (identity) is simple and ubiquitous, making it a key case study for
broader questions about the representations supporting abstract relational reasoning …

Systematic compositionality, or the ability to adapt to novel situations by creating a mental
model of the world using reusable pieces of knowledge, remains a significant challenge in …