This page shows the most relevant public items for Agentic AI, 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 29, 2026 • arxiv.org • Haoming Huang, Pongchai Jaisri, Shota Shimizu, Lingfeng Chen, Sota Nakashima, Gema Rodríguez-Pérez
Large Language Model (LLM) Agents are advancing quickly, with the increasing leveraging of LLM Agents to assist in development tasks such as code generation. While LLM Agents accelerate code genera...
Paper • Jan 29, 2026 • arxiv.org • Johannes Kirmayr, Lukas Stappen, Elisabeth André
Existing benchmarks for Large Language Model (LLM) agents focus on task completion under idealistic settings but overlook reliability in real-world, user-facing applications. In domains, such as in...
Paper • Jan 29, 2026 • arxiv.org • Evelien Riddell, James Riddell, Gengyi Sun, Michał Antkiewicz, Krzysztof Czarnecki
Root cause analysis (RCA) is essential for diagnosing failures within complex software systems to ensure system reliability. The highly distributed and interdependent nature of modern cloud-based s...
Paper • Jan 29, 2026 • arxiv.org • Sahil Garg, Brad Cheezum, Sridhar Dutta, Vishal Agarwal
Judge agents are fundamental to agentic AI frameworks: they provide automated evaluation, and enable iterative self-refinement of reasoning processes. We introduce JAF: Judge Agent Forest, a framew...
Paper • Jan 29, 2026 • arxiv.org • Khairul Alam, Saikat Mondal, Banani Roy
Autonomous coding agents (e.g., OpenAI Codex, Devin, GitHub Copilot) are increasingly used to generate fix-related pull requests (PRs) in real world software repositories. However, their practical ...
Paper • Jan 30, 2026 • arxiv.org • Yunpeng Xiong, Ting Zhang
Static Application Security Testing (SAST) tools are essential for identifying software vulnerabilities, but they often produce a high volume of false positives (FPs), imposing a substantial manual...
Paper • Jan 30, 2026 • arxiv.org • Roham Koohestani, Ateş Görpelioğlu, Egor Klimov, Burcu Kulahcioglu Ozkan, Maliheh Izadi
Agentic AI systems act through tools and evolve their behavior over long, stochastic interaction traces. This setting complicates assurance, because behavior depends on nondeterministic environment...
Paper • Jan 31, 2026 • arxiv.org • Amanda Dsouza, Ramya Ramakrishnan, Charles Dickens, Bhavishya Pohani, Christopher M Glaze
As AI agents integrate into enterprise applications, their evaluation demands benchmarks that reflect the complexity of real-world operations. Instead, existing benchmarks overemphasize open-domain...
Paper • Jan 31, 2026 • arxiv.org • Xuan Liu, Haoyang Shang, Zizhang Liu, Xinyan Liu, Yunze Xiao, Yiwen Tu, Haojian Jin
Large language models (LLMs) are increasingly used as simulated participants in social science experiments, but their behavior is often unstable and highly sensitive to design choices. Prior evalua...
Paper • Feb 2, 2026 • arxiv.org • Tiantian Chen, Jiaqi Lu, Ying Shen, Lin Zhang
Large Language Models (LLMs) have shown strong potential as conversational agents. Yet, their effectiveness remains limited by deficiencies in robust long-term memory, particularly in complex, long...
Paper • Feb 5, 2026 • arxiv.org • Chris Zhu, Sasha Cui, Will Sanok Dufallo, Runzhi Jin, Zhen Xu, Linjun Zhang, Daylian Cain
We present an in-depth evaluation of LLMs' ability to negotiate, a central business task that requires strategic reasoning, theory of mind, and economic value creation. To do so, we introduce PieAr...
Paper • Feb 5, 2026 • arxiv.org • Shahin Honarvar, Amber Gorzynski, James Lee-Jones, Harry Coppock, Marek Rei, Joseph Ryan, Alastair F. Donaldson
Agentic large language models (LLMs) are increasingly evaluated on cybersecurity tasks using capture-the-flag (CTF) benchmarks. However, existing pointwise benchmarks have limited ability to shed l...
Paper • Feb 5, 2026 • arxiv.org • Stephen Pilli, Vivek Nallur
Cognitive biases often shape human decisions. While large language models (LLMs) have been shown to reproduce well-known biases, a more critical question is whether LLMs can predict biases at the i...
Paper • Feb 6, 2026 • arxiv.org • Yibing Liu, Chong Zhang, Zhongyi Han, Hansong Liu, Yong Wang, Yang Yu, Xiaoyan Wang, Yilong Yin
We address the problem of runtime trajectory anomaly detection, a critical capability for enabling trustworthy LLM agents. Current safety measures predominantly focus on static input/output filteri...
Paper • Feb 6, 2026 • arxiv.org • Minjeong Ban, Jeonghwan Choi, Hyangsuk Min, Nicole Hee-Yeon Kim, Minseok Kim, Jae-Gil Lee, Hwanjun Song
Information retrieval (IR) evaluation remains challenging due to incomplete IR benchmark datasets that contain unlabeled relevant chunks. While LLMs and LLM-human hybrid strategies reduce costly hu...
Paper • Feb 6, 2026 • arxiv.org • Lanbo Lin, Jiayao Liu, Tianyuan Yang, Li Cai, Yuanwu Xu, Lei Wei, Sicong Xie, Guannan Zhang
Evaluating agentic AI on open-ended professional tasks faces a fundamental dilemma between rigor and flexibility. Static rubrics provide rigorous, reproducible assessment but fail to accommodate di...
Paper • Feb 6, 2026 • arxiv.org • Alisia Lupidi, Bhavul Gauri, Thomas Simon Foster, Bassel Al Omari, Despoina Magka, Alberto Pepe, Alexis Audran-Reiss, Muna Aghamelu, Nicolas Baldwin, Lucia Cipolina-Kun, Jean-Christophe Gagnon-Audet, Chee Hau Leow, Sandra Lefdal, Hossam Mossalam, Abhinav Moudgil, Saba Nazir, Emanuel Tewolde, Isabel Urrego, Jordi Armengol Estape, Amar Budhiraja, Gaurav Chaurasia, Abhishek Charnalia, Derek Dunfield, Karen Hambardzumyan, Daniel Izcovich, Martin Josifoski, Ishita Mediratta, Kelvin Niu, Parth Pathak, Michael Shvartsman, Edan Toledo, Anton Protopopov, Roberta Raileanu, Alexander Miller, Tatiana Shavrina, Jakob Foerster, Yoram Bachrach
LLM agents hold significant promise for advancing scientific research. To accelerate this progress, we introduce AIRS-Bench (the AI Research Science Benchmark), a suite of 20 tasks sourced from sta...
Paper • Feb 6, 2026 • arxiv.org • Jean Kaddour, Srijan Patel, Gbètondji Dovonon, Leo Richter, Pasquale Minervini, Matt J. Kusner
Can AI agents predict whether they will succeed at a task? We study agentic uncertainty by eliciting success probability estimates before, during, and after task execution. All results exhibit agen...
Paper • Feb 6, 2026 • arxiv.org • Sindhuja Chaduvula, Jessee Ho, Kina Kim, Aravind Narayanan, Mahshid Alinoori, Muskan Garg, Dhanesh Ramachandram, Shaina Raza
Over the last decade, explainable AI has primarily focused on interpreting individual model predictions, producing post-hoc explanations that relate inputs to outputs under a fixed decision structu...
Paper • Jan 29, 2026 • arxiv.org • Jiaqi Liu, Yaofeng Su, Peng Xia, Siwei Han, Zeyu Zheng, Cihang Xie, Mingyu Ding, Huaxiu Yao
To support long-term interaction in complex environments, LLM agents require memory systems that manage historical experiences. Existing approaches either retain full interaction histories via pass...
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