3 citations · 12 across the 7 of their papers we have counts for
7 papers
Twin: Playing an Unknown Game with a Test-Time Digital Twin
Alexy Skoutnev, Kirill Acharya, Gaston Longhitano +3
We present a Test-time World-model Inference (Twin) system, in which a frontier coding agent writes an executable world model for completing continual learning tasks, such as ARC-A…
Programmatic Video Prediction Using Large Language Models
Hao Tang, Kevin Ellis, Suhas Lohit +2
The task of estimating the world model describing the dynamics of a real world process assumes immense importance for anticipating and preparing for future outcomes. For applicatio…
Challenges and Paths Towards AI for Software Engineering
Alex Gu, Naman Jain, Wen-Ding Li +7
AI for software engineering has made remarkable progress recently, becoming a notable success within generative AI. Despite this, there are still many challenges that need to be ad…
Combining Induction and Transduction for Abstract Reasoning
Wen-Ding Li, Keya Hu, Carter Larsen +11
When learning an input-output mapping from very few examples, is it better to first infer a latent function that explains the examples, or is it better to directly predict new test…
Is Programming by Example solved by LLMs?
Wen-Ding Li, Kevin Ellis
Programming-by-Examples (PBE) aims to generate an algorithm from input-output examples. Such systems are practically and theoretically important: from an end-user perspective, they…
Code Repair with LLMs gives an Exploration-Exploitation Tradeoff
Hao Tang, Keya Hu, Jin Peng Zhou +4
Iteratively improving and repairing source code with large language models (LLMs), known as refinement, has emerged as a popular way of generating programs that would be too comple…
WorldCoder, a Model-Based LLM Agent: Building World Models by Writing Code and Interacting with the Environment
Hao Tang, Darren Key, Kevin Ellis
We give a model-based agent that builds a Python program representing its knowledge of the world based on its interactions with the environment. The world model tries to explain it…