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arXiv · 2509.12423

Small Models, Big Results: Achieving Superior Intent Extraction through Decomposition

Danielle Cohen·Yoni Halpern·Noam Kahlon·Joel Oren·Omri Berkovitch·Sapir Caduri·Ido Dagan·Anatoly Efros
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Abstract

Understanding user intents from UI interaction trajectories remains a challenging, yet crucial, frontier in intelligent agent development. While massive, datacenter-based, multi-modal large language models (MLLMs) possess greater capacity to handle the complexities of such sequences, smaller models which can run on-device to provide a privacy-preserving, low-cost, and low-latency user experience, struggle with accurate intent inference. We address these limitations by introducing a novel decomposed approach: first, we perform structured interaction summarization, capturing key information from each user action. Second, we perform intent extraction using a fine-tuned model operating on the aggregated summaries. This method improves intent understanding in resource-constrained models, even surpassing the base performance of large MLLMs.

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Danielle Cohen, Yoni Halpern, Noam Kahlon, Joel Oren, Omri Berkovitch, Sapir Caduri, Ido Dagan, Anatoly Efros. 2025-09-15. Small Models, Big Results: Achieving Superior Intent Extraction through Decomposition. https://arxiv.org/abs/2509.12423

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