Beyond Gemini-3-Pro: Revisiting LLM Routing and Aggregation at Scale

• URL: http://arxiv.org/abs/2601.01330v1
• Date: Sun, 04 Jan 2026 02:05:52 GMT
• Title: Beyond Gemini-3-Pro: Revisiting LLM Routing and Aggregation at Scale
• Authors: Shengji Tang, Weihao Lin, Jingqi Ye, Hao Li, Bo Zhang, Shuyue Hu, Tao Chen, Wangli Ouyang, Lei Bai, Peng Ye,
• Abstract summary: Large Language Models (LLMs) have rapidly advanced, with Gemini-3-Pro setting a new performance milestone.<n>We explore collective intelligence as an alternative to monolithic scaling, and demonstrate that open-source LLMs' collaboration can surpass Gemini-3-Pro.
• Score: 29.165198842583575
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have rapidly advanced, with Gemini-3-Pro setting a new performance milestone. In this work, we explore collective intelligence as an alternative to monolithic scaling, and demonstrate that open-source LLMs' collaboration can surpass Gemini-3-Pro. We first revisit LLM routing and aggregation at scale and identify three key bottlenecks: (1) current train-free routers are limited by a query-based paradigm focusing solely on textual similarity; (2) recent aggregation methods remain largely static, failing to select appropriate aggregators for different tasks;(3) the complementarity of routing and aggregation remains underutilized. To address these problems, we introduce JiSi, a novel framework designed to release the full potential of LLMs' collaboration through three innovations: (1) Query-Response Mixed Routing capturing both semantic information and problem difficulty; (2) Support-Set-based Aggregator Selection jointly evaluating the aggregation and domain capacity of aggregators; (3) Adaptive Routing-Aggregation Switch dynamically leveraging the advantages of routing and aggregation. Comprehensive experiments on nine benchmarks demonstrate that JiSi can surpass Gemini-3-Pro with only 47% costs by orchestrating ten open-source LLMs, while outperforming mainstream baselines. It suggests that collective intelligence represents a novel path towards Artificial General Intelligence (AGI).

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