This page shows the most relevant public items for AI Engineering, 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 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, ...
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...
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