AgentCoMa: A Compositional Benchmark Mixing Commonsense and Mathematical Reasoning in Real-World Scenarios
arXiv:2508.19988v3 Announce Type: replace Abstract: Large Language Models (LLMs) have achieved high accuracy on complex commonsense and mathematical problems that involve the composition of multiple reasoning steps. However, current compositional benchmarks testing these skills tend to focus on either commonsense or math reasoning, whereas LLM agents solving real-world tasks would require a combination of both. In this work, we introduce an Agentic Commonsense and Math benchmark (AgentCoMa), where each compositional task requires a commonsense reasoning step and a math reasoning step. We test
The rapid advancement of LLMs necessitates more sophisticated and realistic benchmarks to evaluate their capabilities in practical, multi-faceted tasks beyond isolated reasoning types.
A benchmark like AgentCoMa directly addresses a critical limitation in current LLM evaluation, pushing models towards the integrated reasoning required for real-world agentic applications.
The introduction of AgentCoMa shifts the focus of LLM development and evaluation from specialized reasoning to the crucial integration of commonsense and mathematical skills within a single task.
- · AI researchers and developers
- · Companies building AI agents
- · SaaS platforms adopting AI agents
- · LLMs lacking strong composable reasoning
- · Benchmarks focusing on isolated reasoning skills
- · Companies relying on single-modality AI solutions
Improved benchmarks lead to more capable and robust AI models that can handle complex real-world scenarios.
The development of highly integrated AI agents begins to automate tasks requiring both abstract reasoning and practical understanding.
White-collar workflows are significantly transformed as AI agents handle multi-step tasks across diverse domains, reducing human intervention.
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