93 citations · 429 across the 40 of their papers we have counts for
8 papers · 1 filter
IDAT: A Multi-Modal Dataset and Toolkit for Building and Evaluating Interactive Task-Solving Agents
Shrestha Mohanty, Negar Arabzadeh, Andrea Tupini +5
Seamless interaction between AI agents and humans using natural language remains a key goal in AI research. This paper addresses the challenges of developing interactive agents cap…
Enhancing Agent Learning through World Dynamics Modeling
Zhiyuan Sun, Haochen Shi, Marc-Alexandre Côté +3
Large language models (LLMs) have been increasingly applied to tasks in language understanding and interactive decision-making, with their impressive performance largely attributed…
Can Language Models Serve as Text-Based World Simulators?
Ruoyao Wang, Graham Todd, Ziang Xiao +4
Virtual environments play a key role in benchmarking advances in complex planning and decision-making tasks but are expensive and complicated to build by hand. Can current language…
DISCOVERYWORLD: A Virtual Environment for Developing and Evaluating Automated Scientific Discovery Agents
Peter Jansen, Marc-Alexandre Côté, Tushar Khot +5
Automated scientific discovery promises to accelerate progress across scientific domains. However, developing and evaluating an AI agent's capacity for end-to-end scientific reason…
Sub-goal Distillation: A Method to Improve Small Language Agents
Maryam Hashemzadeh, Elias Stengel-Eskin, Sarath Chandar +1
While Large Language Models (LLMs) have demonstrated significant promise as agents in interactive tasks, their substantial computational requirements and restricted number of calls…
OPEx: A Component-Wise Analysis of LLM-Centric Agents in Embodied Instruction Following
Haochen Shi, Zhiyuan Sun, Xingdi Yuan +2
Embodied Instruction Following (EIF) is a crucial task in embodied learning, requiring agents to interact with their environment through egocentric observations to fulfill natural…