FlowCompile performs compile-time design space exploration on structured LLM workflows to produce reusable high-quality configuration sets that outperform routing baselines with up to 6.4x speedup.
Solving quantitative reasoning problems with language models.Advances in neural information processing systems, 35:3843–3857, 2022a
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A large model generates a compact reasoning signal that a small model uses to solve tasks, reducing the large model's output tokens by up to 60% on benchmarks like AIME and GPQA.
DiffAdapt detects problem difficulty via entropy in reasoning traces and applies one of three fixed inference strategies per question, cutting token usage up to 22.4% with comparable or better accuracy across five models and eight benchmarks.
citing papers explorer
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FlowCompile: An Optimizing Compiler for Structured LLM Workflows
FlowCompile performs compile-time design space exploration on structured LLM workflows to produce reusable high-quality configuration sets that outperform routing baselines with up to 6.4x speedup.
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When Less is Enough: Efficient Inference via Collaborative Reasoning
A large model generates a compact reasoning signal that a small model uses to solve tasks, reducing the large model's output tokens by up to 60% on benchmarks like AIME and GPQA.
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DiffAdapt: Difficulty-Adaptive Reasoning for Token-Efficient LLM Inference
DiffAdapt detects problem difficulty via entropy in reasoning traces and applies one of three fixed inference strategies per question, cutting token usage up to 22.4% with comparable or better accuracy across five models and eight benchmarks.