arXiv:2505.24069v4 Announce Type: replace Abstract: Large language models (LLMs) are deployed on increasingly complex tasks that require multi-step decision-making. Understanding their algorithmic reasoning abilities is therefore crucial. However, we lack a diagnostic benchmark for evaluating these capabilities. We propose to use data structures as a principled lens: as fundamental building blocks of algorithms, they naturally probe structural reasoning - the ability to understand and manipulate relationships such as order, hierarchy, and connectivity that underpin algorithmic reasoning. We in
The rapid deployment of LLMs in complex tasks necessitates a deeper understanding of their fundamental reasoning capabilities, which current benchmarks inadequately address.
Evaluating LLMs' structural reasoning through data structures provides a crucial diagnostic tool for developing more robust and reliable AI, impacting the quality and trust in AI-driven decision-making.
The introduction of a 'data structures as a lens' benchmark provides a more rigorous method to assess algorithmic reasoning, guiding future LLM development towards improved foundational intelligence.
- · AI researchers
- · LLM developers
- · Companies deploying complex AI systems
- · LLMs lacking strong structural reasoning
- · Developers relying on superficial benchmarks
Improved diagnostic tools lead to a more nuanced understanding of LLM limitations.
Enhanced benchmarking drives the development of next-generation LLM architectures with superior reasoning abilities.
More capable LLMs accelerate automation in white-collar tasks, potentially collapsing existing workflow structures.
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Read at arXiv cs.LG