LLM-WikiRace Benchmark: How Far Can LLMs Plan over Real-World Knowledge Graphs?

• URL: http://arxiv.org/abs/2602.16902v3
• Date: Mon, 23 Feb 2026 11:03:50 GMT
• Title: LLM-WikiRace Benchmark: How Far Can LLMs Plan over Real-World Knowledge Graphs?
• Authors: Juliusz Ziomek, William Bankes, Lorenz Wolf, Shyam Sundhar Ramesh, Xiaohang Tang, Ilija Bogunovic,
• Abstract summary: LLM-Wikirace is a benchmark for evaluating planning, reasoning, and world knowledge in large language models (LLMs)<n>We evaluate a broad set of open- and closed-source models, including Gemini-3, GPT-5, and Claude Opus 4.5.<n>Our analysis shows that world knowledge is a necessary ingredient for success, but only up to a point, planning and long-horizon reasoning capabilities become the dominant factors.
• Score: 15.465732997309182
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce LLM-Wikirace, a benchmark for evaluating planning, reasoning, and world knowledge in large language models (LLMs). In LLM-Wikirace, models must efficiently navigate Wikipedia hyperlinks step by step to reach a target page from a given source, requiring look-ahead planning and the ability to reason about how concepts are connected in the real world. We evaluate a broad set of open- and closed-source models, including Gemini-3, GPT-5, and Claude Opus 4.5, which achieve the strongest results on the easy level of the task and demonstrate superhuman performance. Despite this, performance drops sharply on hard difficulty: the best-performing model, Gemini-3, succeeds in only 23\% of hard games, highlighting substantial remaining challenges for frontier models. Our analysis shows that world knowledge is a necessary ingredient for success, but only up to a point, beyond this threshold, planning and long-horizon reasoning capabilities become the dominant factors. Trajectory-level analysis further reveals that even the strongest models struggle to replan after failure, frequently entering loops rather than recovering. LLM-Wikirace is a simple benchmark that reveals clear limitations in current reasoning systems, offering an open arena where planning-capable LLMs still have much to prove. Our code and leaderboard available at https:/llmwikirace.github.io.

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