arXiv:2602.16902v4 Announce Type: replace-cross 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 d
The continuous development and benchmark-driven assessment of LLM capabilities are crucial as models aim for greater autonomy and real-world application.
This benchmark helps quantify the planning and reasoning abilities of advanced LLMs, which are critical for their deployment as effective AI agents.
The introduction of LLM-Wikirace provides a standardized, real-world knowledge graph environment to directly compare and drive improvements in LLM planning and reasoning.
- · AI research institutions
- · Developers of advanced LLMs
- · Cloud AI providers
- · LLMs lacking sophisticated planning algorithms
- · Companies relying on simpler, less 'intelligent' AI models
Benchmarks like LLM-Wikirace accelerate the development of more capable and reliable autonomous AI agents.
Improved planning and reasoning in LLMs enhance their utility across domains requiring complex problem-solving and navigation of information.
The demonstrated capabilities could lead to new use cases for AI in areas like scientific discovery, strategic analysis, and complex system management.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG