Semantic Agreement Enables Efficient Open-Ended LLM Cascades

• URL: http://arxiv.org/abs/2509.21837v3
• Date: Mon, 27 Oct 2025 18:59:37 GMT
• Title: Semantic Agreement Enables Efficient Open-Ended LLM Cascades
• Authors: Duncan Soiffer, Steven Kolawole, Virginia Smith,
• Abstract summary: Cascade systems route computational requests to smaller models when possible and defer to larger models only when necessary.<n>We propose semantic agreement as a training-free signal for reliable deferral.<n>We find that semantic cascades match or surpass target-model quality at 40% of the cost and reduce latency by up to 60%.
• Score: 18.119677655287607
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cascade systems route computational requests to smaller models when possible and defer to larger models only when necessary, offering a promising approach to balance cost and quality in LLM deployment. However, they face a fundamental challenge in open-ended text generation: determining output reliability when generation quality lies on a continuous spectrum, often with multiple valid responses. To address this, we propose semantic agreement -- meaning-level consensus between ensemble outputs -- as a training-free signal for reliable deferral. We show that when diverse model outputs agree semantically, their consensus is a stronger reliability signal than token-level confidence. Evaluated from 500M to 70B-parameter models, we find that semantic cascades match or surpass target-model quality at 40% of the cost and reduce latency by up to 60%. Our method requires no model internals, works across black-box APIs, and remains robust to model updates, making it a practical baseline for real-world LLM deployment.

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