COMED: The Missing Middle Between Routing and Collaboration in Multi-LLM Inference

2026-09-22

AI Summary

为解决多大型语言模型推理中路由与协作之间的平衡问题，提出COMED方法，通过选择性跨模型协作来提高准确性并减少资源消耗。

Abstract

No single Large Language Model (LLM) is uniformly reliable across queries, motivating multi-model inference systems that either route among models or combine their outputs. However, routing stops after selecting an initial model, while dense collaboration invokes peers on every query. We show that collaboration is non-monotonic: peers can recover failures that no model solves alone, but can also corrupt initially correct answers. We introduce COMED (Controlled Model Escalation for Multi-LLM Deliberation), a post-anchor controller for selective cross-model collaboration. COMED uses anchor self-consistency, router margin, and a lightweight peer probe to accept confident answers, verify ambiguous cases, and escalate only when collaboration is likely beneficial. We formalize this trade-off with a rescue-harm decomposition showing that selective collaboration improves when rescued errors outweigh collaboration-induced harms. Across medical, scientific, and general reasoning benchmarks, COMED improves fixed and routed anchors in all 16 open-weight settings, with gains up to +10.7 percentage points on MedQA while invoking fewer models and using fewer decoded tokens than dense collaboration. On HLE with frontier models, COMED improves GPT-5.5 from 23.1% to 28.1%, outperforming dense collaboration and achieving the best results.

Problem

• multi-LLM inference
• routing
• collaboration
• reliability

Innovation

• Controlled Model Escalation
• Multi-LLM Deliberation
• Selective Collaboration
• Anchor Self-Consistency
• Router Margin