activity
20232025
most citedLoose LIPS Sink Ships: Asking Questions in Battleship with Language-Informed Program Sampling

2 citations · 4 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CL2025

Shoot First, Ask Questions Later? Building Rational Agents that Explore and Act Like People

Gabriel Grand, Valerio Pepe, Jacob Andreas +1

Many emerging applications of AI--from scientific discovery to medical diagnosis--require agents to seek information strategically: forming hypotheses, asking targeted questions, a…

cs.CL2025

Self-Steering Language Models

Gabriel Grand, Joshua B. Tenenbaum, Vikash K. Mansinghka +2

While test-time reasoning enables language models (LMs) to tackle complex tasks, searching or planning in natural language can be slow, costly, and error-prone. But even when LMs s…

cs.LG2024

Learning Linear Attention in Polynomial Time

Morris Yau, Ekin Akyürek, Jiayuan Mao +3

Previous research has explored the computational expressivity of Transformer models in simulating Boolean circuits or Turing machines. However, the learnability of these simulators…

cs.RO2024

Learning with Language-Guided State Abstractions

Andi Peng, Ilia Sucholutsky, Belinda Z. Li +4

We describe a framework for using natural language to design state abstractions for imitation learning. Generalizable policy learning in high-dimensional observation spaces is faci…

cs.CL2024★ 2 cited

Loose LIPS Sink Ships: Asking Questions in Battleship with Language-Informed Program Sampling

Gabriel Grand, Valerio Pepe, Jacob Andreas +1

Questions combine our mastery of language with our remarkable facility for reasoning about uncertainty. How do people navigate vast hypothesis spaces to pose informative questions…

cs.AI2023

Learning adaptive planning representations with natural language guidance

Lionel Wong, Jiayuan Mao, Pratyusha Sharma +5

Effective planning in the real world requires not only world knowledge, but the ability to leverage that knowledge to build the right representation of the task at hand. Decades of…

cs.CL2023★ 2 cited

LILO: Learning Interpretable Libraries by Compressing and Documenting Code

Gabriel Grand, Lionel Wong, Maddy Bowers +4

While large language models (LLMs) now excel at code generation, a key aspect of software development is the art of refactoring: consolidating code into libraries of reusable and r…