3 papers
cs.LG2026
Algorithmic Primitives and Compositional Geometry of Reasoning in Language Models
Samuel Lippl, Thomas McGee, Kimberly Lopez +5
How do latent and inference time computations enable large language models (LLMs) to solve multi-step reasoning? We introduce a framework for tracing and steering algorithmic primi…
cs.AI2025
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…
cs.AI2025
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…