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CascadeDebate: Multi-Agent Deliberation for Cost-Aware LLM Cascades
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 22:08
摘要
Cascaded LLM systems coordinate models of varying sizes with human experts to balance accuracy, cost, and abstention under uncertainty. However, single-model tiers at each stage falter on ambiguous queries, triggering premature escalations to costlier models or experts due to under-confidence and inefficient compute scaling. CascadeDebate addresses this critical gap by inserting multi-agent deliberation directly at each tier’s escalation boundary. Confidence-based routers activate lightweight agent ensembles only for uncertain cases, enabling consensus-driven resolution of ambiguities internally, without invoking higher-cost upgrades. Our unified architecture alternates single-model inference with selective multi-agent deliberation across model scales, culminating in human experts as final fallback. This design scales test-time compute dynamically to query difficulty. Across five benchmarks spanning science, medicine, and general knowledge, CascadeDebate outperforms strong single-model cascades and standalone multi-agent systems by up to 26.75%.An online threshold optimizer proves essential, boosting accuracy 20.98–52.33% relative improvement over fixed policies and enabling elastic adaptation to real-world distributions.
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记录 #1 · 2026-07 · 最新观测版本
CascadeDebate: Multi-Agent Deliberation for Cost-Aware LLM Cascades
Cascaded LLM systems coordinate models of varying sizes with human experts to balance accuracy, cost, and abstention under uncertainty. However, single-model tiers at each stage falter on ambiguous queries, triggering premature escalations to costlier models or experts due to under-confidence and inefficient compute scaling. CascadeDebate addresses this critical gap by inserting multi-agent deliberation directly at each tier’s escalation boundary. Confidence-based routers activate lightweight agent ensembles only for uncertain cases, enabling consensus-driven resolution of ambiguities internally, without invoking higher-cost upgrades. Our unified architecture alternates single-model inference with selective multi-agent deliberation across model scales, culminating in human experts as final fallback. This design scales test-time compute dynamically to query difficulty. Across five benchmarks spanning science, medicine, and general knowledge, CascadeDebate outperforms strong single-model cascades and standalone multi-agent systems by up to 26.75%.An online threshold optimizer proves essential, boosting accuracy 20.98–52.33% relative improvement over fixed policies and enabling elastic adaptation to real-world distributions.
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