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 often struggle with ambiguous queries, triggering premature escalations to costlier models or experts due to under-confidence and inefficient compute scaling. CascadeDebate addresses this 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 infer...
| Authors | Raeyoung Chang, Dongwook Kwon, Jisoo Lee et al. |
|---|---|
| Primary category | cs.CL |
| Published | 14 April 2026 |
| Last updated at source | 14 April 2026 |
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