Jeremiah Greer — accepted AI-conference papers

2026

e3: Learning to Explore Enables Extrapolation of Test-Time Compute for LLMs

Amrith Setlur, Matthew Y. R. Yang, Charlie Victor Snell, Jeremiah Greer, Ian Wu, Virginia Smith +2

ICLR 2026poster

Test-time scaling offers a promising path to improve LLM reasoning by utilizing more compute at inference time; however, the true promise of this paradigm lies in extrapolation (i.e., improvement in performance on hard problems as LLMs keep "thinking" for longer, beyond the maximum token budget they…

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2025

Can a Single Model Master Both Multi-turn Conversations and Tool Use? CoALM: A Unified Conversational Agentic Language Model

Emre Can Acikgoz, Jeremiah Greer, Akul Datta, Ze Yang, William Zeng, Oussama Elachqar +3

ACL 2025long

Large Language Models (LLMs) with API-calling capabilities enabled building effective Language Agents (LA), while also revolutionizing the conventional task-oriented dialogue (TOD) paradigm. However, current approaches face a critical dilemma: TOD systems are often trained on a limited set of target…

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