3 citations · 3 across the 3 of their papers we have counts for
3 papers
AlgoTrace: Algorithmic Primitives and Compositional Geometry of Reasoning in Language Models
Samuel Lippl, Thomas McGee, Kimberly Lopez +5
How do inference time and latent computations enable large language models (LLMs) to solve multi-step reasoning problems? We introduce AlgoTrace, a framework for tracing and steeri…
Position: We Need An Algorithmic Understanding of Generative AI
Oliver Eberle, Thomas McGee, Hamza Giaffar +2
What algorithms do LLMs actually learn and use to solve problems? Studies addressing this question are sparse, as research priorities are focused on improving performance through s…
Improving Planning with Large Language Models: A Modular Agentic Architecture
Taylor Webb, Shanka Subhra Mondal, Ida Momennejad
Large language models (LLMs) demonstrate impressive performance on a wide variety of tasks, but they often struggle with tasks that require multi-step reasoning or goal-directed pl…