The paper investigates how to choose model pools for Multi-Agent Systems (MAS) that combine multiple model outputs to tackle complex reasoning tasks. It evaluates eight selection strategies—such as model size, accuracy, and answer diversity—across both before-generation (routing) and after-generation (majority-voting, LLM-as-a-judge) MAS architectures on scientific benchmarks. The study finds that expanding the candidate pool often harms performance, that selecting candidates within a single model family yields the best relative gains, and that indiscriminate addition of heterogeneous models can destabilize the system.
By Sara Vera Marjanovi\'c, Jiacheng Xu, Aleksandr Laptev, Grigor Nalbandyan, Erik Arakelyan, Evelina Bakhaturina
arXiv:2609.38816v1 Announce Type: new
Abstract: While multi-agent and model collaboration algorithms gain traction to combine the strengths of diverse Large Language Models (LLMs), existing systems r...
By Zongwan Cao, Ziyuan Yang, Shangbin Feng, Michael Duan, Skyler Hallinan, Bingbing Wen, Lucy Lu Wang, Yulia Tsvetkov
arXiv:2510. 15416v2 Announce Type: replace Abstract: We investigate a framework in which LoRA adapters are treated as callable tools that a base language model can dynamically select and invoke.
By Pavan C Shekar, Aswanth Krishnan
The paper introduces a method to improve test-time scaling (TTS) for large language models by using multi-agent systems (MAS) to split long reasoning chains into manageable contexts. A new dataset, M500, containing 500 multi-agent collaborative reasoning traces, is used to fine‑tune open‑source models, enabling them to learn collaborative patterns and outperform their base versions. An adaptive scaling strategy with a "CEO" agent is proposed to dynamically guide reasoning depth, and experiments in the AgentVerse framework confirm the effectiveness of the approach.
By Can Jin, Hongwu Peng, Qixin Zhang, Yujin Tang, Dimitris N. Metaxas, Tong Che
arXiv:2604. 02923v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated advanced capabilities but often suffer from factual inaccuracies (hallucinations) and systematic biases.
By Shuai Wu, Xue Li, Yanna Feng, Yufang Li, Zhijun Wang, Ran Wang
arXiv:2606. 13003v1 Announce Type: new Abstract: Prevailing wisdom posits that Multi-Agent Systems (MAS) are superior to Single-Agent Systems (SAS), citing advantages like context protection, parallel processing and distributed decision-making.
By Prathyusha Jwalapuram, Hehai Lin, Chuyuan Li, Fangkai Jiao, Sudong Wang, Yifei Ming, Zixuan Ke, Chengwei Qin, Giuseppe Carenini, Shafiq Joty
The paper argues that the architecture of multi‑agent large language model (LLM) frameworks, rather than just the intelligence of the underlying models, largely determines system performance. It introduces a taxonomy of architectural dimensions—such as orchestration, memory, planning interfaces, specialization, and communication topology—and presents MAFBench, a unified evaluation suite. An empirical study across nine frameworks, keeping the LLM constant, reveals six design principles and shows that choices like orchestration and communication topology can dramatically affect latency, accuracy, and coordination success.
By Abdelghny Orogat, Ana Rostam, Essam Mansour
arXiv:2606. 07603v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit strong reasoning capabilities, yet most LLM-based agents are statically deployed and unable to improve through task interactions.
By Bowen Ren, Heyan Huang, Yinghao Li, Yang Gao
arXiv:2505. 16988v2 Announce Type: replace-cross Abstract: LLM-based multi-agent systems (MAS) have demonstrated significant potential in enhancing single LLMs to address complex and diverse tasks in practical applications.
By Rui Ye, Keduan Huang, Qimin Wu, Yuzhu Cai, Tian Jin, Xianghe Pang, Xiangrui Liu, Jiaqi Su, Chen Qian, Bohan Tang, Kaiqu Liang, Jiaao Chen, Yue Hu, Zhenfei Yin, Rongye Shi, Bo An, Yang Gao, Wenjun Wu, Lei Bai, Siheng Chen
arXiv:2511. 02200v2 Announce Type: replace Abstract: The emergence of multi-agent systems powered by large language models (LLMs) has unlocked new frontiers in complex task-solving, enabling diverse agents to integrate unique expertise, collaborate flexibly, and address challenges unattainable for individual models.
By Jingbo Wang, Sendong Zhao, Haochun Wang, Yuzheng Fan, Ting Liu
arXiv:2606. 24437v1 Announce Type: new Abstract: Mixture-of-Agents (MoA) architectures improve inference-time scaling by organizing multiple LLM agents into layered reasoning pipelines.
By Heng Ping, Arijit Bhattacharjee, Peiyu Zhang, Shixuan Li, Wei Yang, Ali Jannesari, Nesreen Ahmed, Paul Bogdan
Mixture-of-Agents (MoA) architectures improve inference-time scaling by organizing multiple LLM agents into layered reasoning pipelines. However, existing MoA variants fail to sustain gains as depth increases, exhibiting degradation, early plateauing, or saturation.