This page shows the most relevant public items for Awesome List: ai-agent-papers-2026, ranked by trend activity and review signal. Use weekly for fast changes, monthly for more stable patterns, and all-time for evergreen picks.
Paper • Jan 16, 2026 • arxiv.org • Zixu Wang, Bingbing Xu, Yige Yuan, Huawei Shen, Xueqi Cheng
Multi-Agent Systems (MAS) built on large language models typically solve complex tasks by coordinating multiple agents through workflows. Existing approaches generates workflows either at task leve...
Paper • Jan 17, 2026 • arxiv.org • Yichen Jiang, Peng Ye, Jiakang Yuan, Chongjun Tu, Lei Bai, Tao Chen
Effectively processing long contexts remains a fundamental yet unsolved challenge for large language models (LLMs). Existing single-LLM-based methods primarily reduce the context window or optimize...
Paper • Jan 24, 2026 • arxiv.org • Yin Cai, Zhouhong Gu, Juntao Zhang, Ping Chen
Humans face countless scenarios that require reasoning and judgment in daily life. However, existing large language model training methods primarily allow models to learn from existing textual cont...
Paper • Jan 20, 2026 • arxiv.org • Apoorva Adimulam, Rajesh Gupta, Sumit Kumar
Orchestrated multi-agent systems represent the next stage in the evolution of artificial intelligence, where autonomous agents collaborate through structured coordination and communication to achie...
Paper • Jan 20, 2026 • arxiv.org • Gopal Vijayaraghavan, Prasanth Jayachandran, Arun Murthy, Sunil Govindan, Vivek Subramanian
AI Agents can perform complex operations at great speed, but just like all the humans we have ever hired, their intelligence remains fallible. Miscommunications aren't noticed, systemic biases have...
Paper • Jan 20, 2026 • arxiv.org • Yiyang Wang, Yiqiao Jin, Alex Cabral, Josiah Hester
Multi-agent systems (MAS) have recently emerged as promising socio-collaborative companions for emotional and cognitive support. However, these systems frequently suffer from persona collapse--wher...
Paper • Jan 29, 2026 • arxiv.org • Zixuan Ke, Yifei Ming, Austin Xu, Ryan Chin, Xuan-Phi Nguyen, Prathyusha Jwalapuram, Jiayu Wang, Semih Yavuz, Caiming Xiong, Shafiq Joty
While multi-agent systems (MAS) promise elevated intelligence through coordination of agents, current approaches to automatic MAS design under-deliver. Such shortcomings stem from two key factors: ...
Paper • Jan 21, 2026 • arxiv.org • Christopher Scofield
Multi-agent systems (MAS) composed of large language models often exhibit improved problem-solving performance despite operating on identical information. In this work, we provide a formal explanat...
Paper • Jan 23, 2026 • arxiv.org • Tims Pecerskis, Aivars Smirnovs
This paper introduces the N-Way Self-Evaluating Deliberation (NSED) protocol, a Runtime Mixture-of-Models (MoM) architecture that constructs emergent composite models from a plurality of distinct e...
Paper • Jan 23, 2026 • arxiv.org • Inderjeet Singh, Eleonore Vissol-Gaudin, Andikan Otung, Motoyoshi Sekiya
Fine-tuning Large Language Models (LLMs) for specialized domains is constrained by a fundamental challenge: the need for diverse, cross-organizational data conflicts with the principles of data pri...
Paper • Jan 23, 2026 • arxiv.org • Miao Zhang, Junsik Kim, Siyuan Xiang, Jian Gao, Cheng Cao
Multi-agent large language model (LLM) and vision-language model (VLM) debate systems employ specialized roles for complex problem-solving, yet model specializations are not leveraged to decide whi...
Paper • Jan 24, 2026 • arxiv.org • Bang Liu, Linglong Kong, Jian Pei
Multi-agent systems can improve reliability, yet under a fixed inference budget they often help, saturate, or even collapse. We develop a minimal and calibratable theory that predicts these regimes...
Paper • Jan 27, 2026 • arxiv.org • Shanyv Liu, Xuyang Yuan, Tao Chen, Zijun Zhan, Zhu Han, Danyang Zheng, Weishan Zhang, Shaohua Cao
Graph-based Multi-Agent Systems (MAS) enable complex cyclic workflows but suffer from inefficient static model allocation, where deploying strong models uniformly wastes computation on trivial sub-...
Paper • Jan 29, 2026 • arxiv.org • Haoji Zhang, Yuzhe Li, Zhenqiang Liu, Chenyang Liu, Shenyang Zhang, Yi Zhou
While Large Language Models (LLMs) have catalyzed breakthroughs in automated code generation, Small Language Models (SLMs) often encounter reasoning bottlenecks and failure loops when addressing co...
Paper • Jan 29, 2026 • arxiv.org • Ruiwen Zhou, Maojia Song, Xiaobao Wu, Sitao Cheng, Xunjian Yin, Yuxi Xie, Zhuoqun Hao, Wenyue Hua, Liangming Pan, Soujanya Poria, Min-Yen Kan
Individual agents in multi-agent (MA) systems often lack robustness, tending to blindly conform to misleading peers. We show this weakness stems from both sycophancy and inadequate ability to evalu...
Paper • Jan 29, 2026 • arxiv.org • Jon Chun, Kathrine Elkins, Yong Suk Lee
We introduce AgenticSimLaw, a role-structured, multi-agent debate framework that provides transparent and controllable test-time reasoning for high-stakes tabular decision-making tasks. Unlike blac...
Paper • Feb 4, 2026 • arxiv.org • Shuo Liu, Tianle Chen, Ryan Amiri, Christopher Amato
Recent work has explored optimizing LLM collaboration through Multi-Agent Reinforcement Learning (MARL). However, most MARL fine-tuning approaches rely on predefined execution protocols, which ofte...
Paper • Jan 29, 2026 • arxiv.org • Xinyuan Song, Liang Zhao
Multi-agent systems (MAS) increasingly solve complex tasks by orchestrating agents and tools selected from rapidly growing marketplaces. As these marketplaces expand, many candidates become functio...
Paper • Jan 30, 2026 • arxiv.org • Wei Zhu, Zhiwen Tang, Kun Yue
Recent advancements have increasingly focused on leveraging large language models (LLMs) to construct autonomous agents for complex problem-solving tasks. However, existing approaches predominantly...
Paper • Jan 30, 2026 • arxiv.org • Wei Zhu, Lixing Yu, Hao-Ren Yao, Zhiwen Tang, Kun Yue
Large language models (LLMs) have shown strong capabilities across diverse decision-making tasks. However, existing approaches often overlook the specialization differences among available models, ...
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