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.
Agreement-based cascading for efficient inference
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ExecTune trains guide models via acceptance sampling, supervised fine-tuning, and structure-aware RL to boost executability of strategies for black-box LLMs, yielding up to 9.2% higher accuracy and 22.4% lower cost on math and code tasks.
AutoRelAnnotator routes queries through fine-tuned classifier cascades with isotonic calibration to deliver high-accuracy relevance labels at roughly half the compute cost while adding a small accuracy gain.
An adaptive edge system with FSM-guided tiering, multi-model YOLO consensus, and diurnal sensor fusion improves standing water detection performance while using less energy and maintaining bounded latency than static baselines.
citing papers explorer
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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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ExecTune: Effective Steering of Black-Box LLMs with Guide Models
ExecTune trains guide models via acceptance sampling, supervised fine-tuning, and structure-aware RL to boost executability of strategies for black-box LLMs, yielding up to 9.2% higher accuracy and 22.4% lower cost on math and code tasks.
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AutoRelAnnotator: Calibrated Model Cascades for Cost-Efficient Relevance Evaluation in Sponsored Search
AutoRelAnnotator routes queries through fine-tuned classifier cascades with isotonic calibration to deliver high-accuracy relevance labels at roughly half the compute cost while adding a small accuracy gain.
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Edge-Based Standing-Water Detection via FSM-Guided Tiering and Multi-Model Consensus
An adaptive edge system with FSM-guided tiering, multi-model YOLO consensus, and diurnal sensor fusion improves standing water detection performance while using less energy and maintaining bounded latency than static baselines.
- Optimal Query Allocation in Extractive QA with LLMs: A Learning-to-Defer Framework with Theoretical Guarantees