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 30, 2026 • arxiv.org • Shuai Shao, Yixiang Liu, Bingwei Lu, Weinan Zhang
In recent years, LLM-based multi-agent systems (MAS) have advanced rapidly, using a router to decompose tasks and delegate subtasks to specialized agents. A natural way to expand capability is to s...
Paper • Feb 4, 2026 • arxiv.org • Ed Li, Junyu Ren, Cat Yan
While multiagent systems have shown promise for tackling complex tasks via specialization, finetuning multiple agents simultaneously faces two key challenges: (1) credit assignment across agents, a...
Paper • Jan 31, 2026 • arxiv.org • Ujwal Kumar, Alice Saito, Hershraj Niranjani, Rayan Yessou, Phan Xuan Tan
Constitutional AI has focused on single-model alignment using fixed principles. However, multi-agent systems create novel alignment challenges through emergent social dynamics. We present Constitut...
Paper • Feb 9, 2026 • arxiv.org • Aneesh Pappu, Batu El, Hancheng Cao, Carmelo di Nolfo, Yanchao Sun, Meng Cao, James Zou
Multi-agent LLM systems are increasingly deployed as autonomous collaborators, where agents interact freely rather than execute fixed, pre-specified workflows. In such settings, effective coordinat...
Paper • Feb 7, 2026 • arxiv.org • Nikita Benkovich, Vitalii Valkov
Large language models have demonstrated strong capabilities in individual software engineering tasks, yet most autonomous systems still treat issue resolution as a monolithic or pipeline-based proc...
Paper • Feb 5, 2026 • arxiv.org • Jun-Min Lee, Meong Hi Son, Edward Choi
Hospital administration departments handle a wide range of operational tasks and, in large hospitals, process over 10,000 requests per day, driving growing interest in LLM-based automation. However...
Paper • Feb 2, 2026 • arxiv.org • Hanlin Zhou, Huah Yong Chan
Recent advances in large-scale language models (LLMs) have made multi-agent architectures attractive for challenging reasoning tasks. However, many existing systems rely on stochastic routing or ad...
Paper • Feb 2, 2026 • arxiv.org • Salaheddin Alzu'bi, Baran Nama, Arda Kaz, Anushri Eswaran, Weiyuan Chen, Sarvesh Khetan, Rishab Bala, Tu Vu, Sewoong Oh
Current agentic frameworks underperform on long-horizon tasks. As reasoning depth increases, sequential orchestration becomes brittle, context windows impose hard limits that degrade performance, a...
Paper • Feb 2, 2026 • arxiv.org • Faezeh Fadaei, Jenny Carla Moran, Taha Yasseri
Generative artificial intelligence and large language models (LLMs) are increasingly deployed in interactive settings, yet we know little about how their identity performance develops when they int...
Paper • Feb 5, 2026 • arxiv.org • Xianyang Liu, Shangding Gu, Dawn Song
Large language model (LLM)-based agents are increasingly expected to negotiate, coordinate, and transact autonomously, yet existing benchmarks lack principled settings for evaluating language-media...
Paper • Feb 5, 2026 • arxiv.org • Xiaopan Zhang, Zejin Wang, Zhixu Li, Jianpeng Yao, Jiachen Li
To complete assignments provided by humans in natural language, robots must interpret commands, generate and answer relevant questions for scene understanding, and manipulate target objects. Real-w...
Paper • Feb 5, 2026 • arxiv.org • Ziyao Zeng, Chen Liu, Tianyu Liu, Hao Wang, Xiatao Sun, Fengyu Yang, Xiaofeng Liu, Zhiwen Fan
Game balancing is a longstanding challenge requiring repeated playtesting, expert intuition, and extensive manual tuning. We introduce RuleSmith, the first framework that achieves automated game ba...
Paper • Feb 5, 2026 • arxiv.org • Yuxing Lu, Yucheng Hu, Xukai Zhao, Jiuxin Cao
Multi-agent systems built from prompted large language models can improve multi-round reasoning, yet most existing pipelines rely on fixed, trajectory-wide communication patterns that are poorly ma...
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