Poster

LongCoT: Benchmarking Long-Horizon Chain-of-Thought Reasoning

Sumeet Motwani ⋅ Daniel Nichols ⋅ Charles London ⋅ Peggy Li ⋅ Fabio Pizzati ⋅ Acer Blake ⋅ Hasan Hammoud ⋅ Tavish McDonald ⋅ Akshat Naik ⋅ Alesia Ivanova ⋅ Vignesh Baskaran ⋅ Ivan Laptev ⋅ Ruben Glatt ⋅ Tal Ben-Nun ⋅ Phil Torr ⋅ Ameya Pandurang Prabhu ⋅ Brian Bartoldson ⋅ Bhavya Kailkhura ⋅ Christian Schroeder de Witt

Abstract

As language models are increasingly deployed for complex autonomous tasks, their ability to reason accurately over longer horizons becomes critical. An essential component of this ability is planning and managing a long, complex chain-of-thought (CoT). We introduce LongCoT, a scalable benchmark of 2,500 expert-designed problems spanning chemistry, mathematics, computer science, chess, and logic to isolate and directly measure the long-horizon CoT reasoning capabilities of frontier models. Problems consist of a short input with a verifiable answer; solving them requires navigating a graph of interdependent steps that span tens to hundreds of thousands of reasoning tokens. Each local step is individually tractable for frontier models, so failures reflect long-horizon reasoning limitations. At release, the best models achieve <10% accuracy (GPT 5.2: 9.8%; Gemini 3 Pro: 6.1%) on LongCoT, revealing a substantial gap in current capabilities. Overall, LongCoT provides a rigorous measure of long-horizon reasoning, tracking the ability of frontier models to reason reliably over extended periods.