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arXiv · 2508.02427

CABENCH: Benchmarking Composable AI for Solving Complex Tasks through Composing Ready-to-Use Models

Tung-Thuy Pham·Duy-Quan Luong·Minh-Quan Duong·Trung-Hieu Nguyen·Thu-Trang Nguyen·Son Nguyen·Hieu Dinh Vo
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Abstract

Composable AI offers a scalable and effective paradigm for tackling complex AI tasks by decomposing them into sub-tasks and solving each sub-task using ready-to-use well-trained models. However, systematically evaluating methods under this setting remains largely unexplored. In this paper, we introduce CABENCH, the first public benchmark comprising 70 realistic composable AI tasks, along with a curated pool of 700 models across multiple modalities and domains. We also propose an evaluation framework to enable end-to-end assessment of composable AI solutions. To establish initial baselines, we provide human-designed reference solutions and compare their performance with two LLM-based approaches. Our results illustrate the promise of composable AI in addressing complex real-world problems while highlighting the need for methods that can fully unlock its potential by automatically generating effective execution pipelines.

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Tung-Thuy Pham, Duy-Quan Luong, Minh-Quan Duong, Trung-Hieu Nguyen, Thu-Trang Nguyen, Son Nguyen, Hieu Dinh Vo. 2025-08-04. CABENCH: Benchmarking Composable AI for Solving Complex Tasks through Composing Ready-to-Use Models. https://arxiv.org/abs/2508.02427

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