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Structured Reasoning

1 post about Structured Reasoning. Every summary links to the original.

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Apple
Selection-Based Structured Reasoning: Toward Efficient Multimodal Search Agents
Multimodal search agents typically rely on freeform reasoning before acting, but small models often produce lengthy reasoning that offers little guidance and increases inference cost. The authors propose Selection-Based Structured Reasoning (SSR), which reframes reasoning as selecting from pre-specified natural-language candidates rather than open-ended generation. SSR enables parallel scoring by using teacher-forced prefilling and a shared context KV cache to compute token likelihoods across candidates simultaneously. Evaluated on seven multimodal search benchmarks with 2B and 4B models, SSR maintains task performance under various reinforcement learning objectives and supervised fine-tuning. The framework achieves search success rates competitive with leading agents of the same scale while reducing per-turn reasoning latency by over 90% and total per-question inference latency by 28–54%.

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Topic

1 post about Structured Reasoning. Every summary links to the original.

Paid picks we use ourselves. Links carry a referral code — a subscription through one earns us a commission at no extra cost to you.

Newest first

Apple
Selection-Based Structured Reasoning: Toward Efficient Multimodal Search Agents
Multimodal search agents typically rely on freeform reasoning before acting, but small models often produce lengthy reasoning that offers little guidance and increases inference cost. The authors propose Selection-Based Structured Reasoning (SSR), which reframes reasoning as selecting from pre-specified natural-language candidates rather than open-ended generation. SSR enables parallel scoring by using teacher-forced prefilling and a shared context KV cache to compute token likelihoods across candidates simultaneously. Evaluated on seven multimodal search benchmarks with 2B and 4B models, SSR maintains task performance under various reinforcement learning objectives and supervised fine-tuning. The framework achieves search success rates competitive with leading agents of the same scale while reducing per-turn reasoning latency by over 90% and total per-question inference latency by 28–54%.

The best engineering reads, once a week.