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Frontier LLMs drop from 83% to 43% once reasoning has to chain across domains

A new benchmark called Relay-Bench shows that frontier large language models (LLMs) drop from an average of 83% on single-domain tasks to 43% when required to chain reasoning across multiple domains in a single prompt, according to a paper submitted to arXiv on July 20, 2026. The leading model, GPT-5.5 (xHigh), scored 43.3% on the benchmark, which tests visual reasoning, coding, math, information extraction, problem-solving, general knowledge, and data analysis through composite problems composed of two to thirteen subproblems.

read2 min views1 publishedJul 26, 2026
Frontier LLMs drop from 83% to 43% once reasoning has to chain across domains
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[Submitted on 20 Jul 2026]


[View PDF](/pdf/2607.18438)

Abstract:Introducing Relay-Bench, an unsaturated, holistic, text-only benchmark that measures LLMs' ability to complete an assortment of tasks from distinct domains in a single prompt. The leading model, GPT-5.5 (xHigh), scores 43.3%. The test set entirely consists of composite problems: groups of single-domain subproblems that are strung together into challenges that require reasoning across multiple domains in combination. Many of these problems then have layers of complexity added through prompt encoding and deliberate context bloat. Domains tested include visual reasoning, coding, math, information extraction (with a focus on web search), problem-solving, general knowledge, and data analysis. No restrictions are imposed outside of the model harness, and models are explicitly encouraged to leverage code-execution, web searches, and all available tools. All problems are composed of two to thirteen subproblems and do not require multi-modal input or output.

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