Selection-Based Structured Reasoning: Toward Efficient Multimodal Search Agents

Selection-Based Structured Reasoning: Toward Efficient Multimodal Search Agents - Apple Machine Learning Research

research area

Methods and Algorithms

content type

paper

published

October 2026

Selection-Based Structured Reasoning: Toward Efficient Multimodal Search Agents

Authors Feiyu Gavin Zhu†, Xiaoyu Zhu, Jiqi Yang, Rui Yang‡*, Arnab Kumar Mondal, Yancheng Wang, Xinke Deng, Jean Oh†, Reid Simmons†, Joerg Liebelt, Xiang Kong, Zhongyu Jiang

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Multimodal agents commonly generate freeform reasoning before each action. For small models, limited model capacity can result in lengthy reasoning that provides little useful guidance for action generation while incurring substantial inference cost. To address this challenge, we introduce Selection-based Structured Reasoning (SSR), a framework that reformulates reasoning as selection instead of open-ended generation. SSR represents recurring high-level reasoning as pre-specified, reusable natural-language candidates. At each turn, the model selects from the reasoning candidates based on their likelihoods given the current context, without an auxiliary task head. Using pre-specified reasoning traces enables parallel scoring, where teacher-forced prefilling computes token likelihoods concurrently within and across candidates using a shared context KV cache. We evaluated SSR on seven multimodal search benchmarks using 2B and 4B models. Across multiple reinforcement learning objectives and supervised fine-tuning, SSR delivers significant efficiency gains without sacrificing task performance. SSR achieves an average success rate competitive with leading search agents of the same scale, while reducing per-turn reasoning latency by over 90% and total per-question model inference latency by 28–54%.

• †Carnegie Mellon University

• ‡ University of Illinois Urbana-Champaign

• ** Work partially done while at Apple

• *** Work done while at Apple

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