Apple's SSR Reformulates Agent Reasoning as Selection to Cut Small-Model Inference Cost
Apple ML Research · rss · 2026-10-15
Apple proposes Selection-based Structured Reasoning (SSR)
Apple ML Research published a paper tackling the reasoning inefficiency of multimodal agents with a new framework, SSR.
Problem: Multimodal agents typically generate freeform reasoning before each action. For small models, limited capacity yields lengthy reasoning that offers little guidance for action generation while incurring substantial inference cost.
Approach: SSR reformulates reasoning from open-ended generation into selection—recurring high-level reasoning patterns are pre-specified as reusable natural-language candidates, and at each turn the model simply selects from these candidates rather than generating from scratch. This cuts inference overhead while preserving reasoning utility, targeting efficient multimodal search agents.
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