DSR-Bench: Evaluating the Structural Reasoning Abilities of LLMs via Data Structures

• URL: http://arxiv.org/abs/2505.24069v1
• Date: Thu, 29 May 2025 23:24:53 GMT
• Title: DSR-Bench: Evaluating the Structural Reasoning Abilities of LLMs via Data Structures
• Authors: Yu He, Yingxi Li, Colin White, Ellen Vitercik,
• Abstract summary: Large language models (LLMs) are increasingly deployed for real-world tasks that fundamentally involve data manipulation.<n>A core requirement is the ability to perform structural reasoning--that is, to understand and reason about data relationships.<n>We introduce DSR-Bench, a novel benchmark evaluating LLMs' structural reasoning capabilities through data structures.
• Score: 20.596558700597644
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) are increasingly deployed for real-world tasks that fundamentally involve data manipulation. A core requirement across these tasks is the ability to perform structural reasoning--that is, to understand and reason about data relationships. For example, customer requests require a temporal ordering, which can be represented by data structures such as queues. However, existing benchmarks primarily focus on high-level, application-driven evaluations without isolating this fundamental capability. To address this gap, we introduce DSR-Bench, a novel benchmark evaluating LLMs' structural reasoning capabilities through data structures, which provide interpretable representations of data relationships. DSR-Bench includes 20 data structures, 35 operations, and 4,140 problem instances, organized hierarchically for fine-grained analysis of reasoning limitations. Our evaluation pipeline is fully automated and deterministic, eliminating subjective human or model-based judgments. Its synthetic nature also ensures scalability and minimizes data contamination risks. We benchmark nine state-of-the-art LLMs. Our analysis shows that instruction-tuned models struggle with basic multi-attribute and multi-hop reasoning. Furthermore, while reasoning-oriented models perform better, they remain fragile on complex and hybrid structures, with the best model achieving an average score of only 47% on the challenge subset. Crucially, models often perform poorly on multi-dimensional data and natural language task descriptions, highlighting a critical gap for real-world deployment.

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