Apple 提出 SSR：基于选择的结构化推理，让多模态搜索智能体推理延迟降低 90% 以上
Selection-Based Structured Reasoning: Toward Efficient Multimodal Search Agents
Apple 研究团队提出 Selection-based Structured Reasoning（SSR），将多模态智能体的推理从开放式生成改为从预设自然语言候选推理中按似然度选择，并借助共享上下文 KV cache 实现并行打分。在 2B 和 4B 模型、七个多模态搜索基准上，SSR 平均成功率与同规模领先搜索智能体相当，单轮推理延迟降低超 90%，每题模型总推理延迟降低 28–54%。
AuthorsFeiyu 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
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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来源：Apple Machine Learning Research · machinelearning.apple.com