15 citations · 26 across the 6 of their papers we have counts for
7 papers
When Successful Strategies Fail: Adaptation to Environmental Novelty in Terminal Agents
Janvijay Singh, Vaishnavi Shrivastava, Dilek Hakkani-Tur +2
LLM agents increasingly solve long-horizon tasks by autonomously interacting with their environment. In doing so, their strategies rely on assumptions about that environment: which…
Echoverse: Deep, Evolving Environments for Training Computer-Use Agents at Scale
Yash Pandya, Sahil Gupta, Sarthak Harne +10
Computer-use agents learn from what their actions change, so training one needs applications it can act on, break and reset. The applications that matter most are login-gated and s…
The Collaboration Gap: Exploration and Benchmarking of Open-World Agentic Cooperation
Tim R. Davidson, Adam Fourney, Saleema Amershi +3
The trajectory of AI development suggests that we will increasingly rely on agent-based systems powered by language models, composed of independently developed agents with differen…
Magentic-UI: Towards Human-in-the-loop Agentic Systems
Hussein Mozannar, Gagan Bansal, Cheng Tan +17
AI agents powered by large language models are increasingly capable of autonomously completing complex, multi-step tasks using external tools. Yet, they still fall short of human-l…
Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks
Adam Fourney, Gagan Bansal, Hussein Mozannar +17
Modern AI agents, driven by advances in large foundation models, promise to enhance our productivity and transform our lives by augmenting our knowledge and capabilities. To achiev…
Teaching Language Models to Hallucinate Less with Synthetic Tasks
Erik Jones, Hamid Palangi, Clarisse Simões +5
Large language models (LLMs) frequently hallucinate on abstractive summarization tasks such as document-based question-answering, meeting summarization, and clinical report generat…
Attention Satisfies: A Constraint-Satisfaction Lens on Factual Errors of Language Models
Mert Yuksekgonul, Varun Chandrasekaran, Erik Jones +5
We investigate the internal behavior of Transformer-based Large Language Models (LLMs) when they generate factually incorrect text. We propose modeling factual queries as constrain…