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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T CascadeDebate: Multi-Agent Deliberation for Cost-Aware LLM Cascades
%A Chang, Raeyoung
%A Kwon, Dongwook
%A Lee, Jisoo
%A Verma, Nikhil
%Y Li, Yunyao
%Y Rehm, Georg
%Y Tu, Mei
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-394-4
%F chang-etal-2026-cascadedebate
%X 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.
%U https://aclanthology.org/2026.acl-industry.93/
%P 1329-1340

Markdown (Informal)

[CascadeDebate: Multi-Agent Deliberation for Cost-Aware LLM Cascades](https://aclanthology.org/2026.acl-industry.93/) (Chang et al., ACL 2026)

• CascadeDebate: Multi-Agent Deliberation for Cost-Aware LLM Cascades (Chang et al., ACL 2026)

ACL

• Raeyoung Chang, Dongwook Kwon, Jisoo Lee, and Nikhil Verma. 2026. CascadeDebate: Multi-Agent Deliberation for Cost-Aware LLM Cascades. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), pages 1329–1340, San Diego, California, USA. Association for Computational Linguistics.