[2607.18438] Relay-Bench: Evaluating LLMs on Multi-Domain Reasoning Chains
Skip to main content
Search arXiv
Press Enter to search · Advanced search
-->
Computer Science > Computation and Language
arXiv:2607.18438 (cs)
[Submitted on 20 Jul 2026]
Title:Relay-Bench: Evaluating LLMs on Multi-Domain Reasoning Chains
Authors:Liam Swayne<br>View a PDF of the paper titled Relay-Bench: Evaluating LLMs on Multi-Domain Reasoning Chains, by Liam Swayne
View PDF
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.
Comments:<br>21 pages, 7 figures
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as:<br>arXiv:2607.18438 [cs.CL]
(or<br>arXiv:2607.18438v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.18438
Focus to learn more
arXiv-issued DOI via DataCite (pending registration)
Submission history<br>From: Liam Swayne [view email]<br>[v1]<br>Mon, 20 Jul 2026 18:46:17 UTC (2,313 KB)
Full-text links:<br>Access Paper:
View a PDF of the paper titled Relay-Bench: Evaluating LLMs on Multi-Domain Reasoning Chains, by Liam Swayne<br>View PDF<br>TeX Source
view license
Current browse context:
cs.CL
next >
new<br>recent<br>| 2026-07
Change to browse by:
cs<br>cs.AI<br>cs.LG
References & Citations
NASA ADS<br>Google Scholar
Semantic Scholar
export BibTeX citation<br>Loading...
BibTeX formatted citation
×
loading...
Data provided by:
Bookmark
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .
Which authors of this paper are endorsers? |<br>Disable MathJax (What is MathJax?)
Major funding support from