This page shows the most relevant public items for RAG, 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 28, 2026 • arxiv.org • Kazuma Yamasaki, Joseph Ayobami Joshua, Tasha Settewong, Mahmoud Alfadel, Kazumasa Shimari, Kenichi Matsumoto
As software engineering moves toward SE3.0, AI agents are increasingly used to carry out development tasks and contribute changes to software projects. It is therefore important to understand the e...
Paper • Jan 28, 2026 • arxiv.org • Ling Tang, Jilin Mei, Dongrui Liu, Chen Qian, Dawei Cheng, Jing Shao, Xia Hu
Large language model-powered multi-agent systems have emerged as powerful tools for simulating complex human-like systems. The interactions within these systems often lead to extreme events whose o...
Paper • Jan 28, 2026 • arxiv.org • Jonathan Rystrøm, Chris Schmitz, Karolina Korgul, Jan Batzner, Chris Russell
Deploying Large Language Model-based agents (LLM agents) in the public sector requires assuring that they meet the stringent legal, procedural, and structural requirements of public-sector institut...
Paper • Jan 28, 2026 • arxiv.org • S M Mahedy Hasan, Md Fazle Rabbi, Minhaz Zibran
We present the first empirical study of AI-generated pull requests that are 'silent,' meaning no comments or discussions accompany them. This absence of any comments or discussions associated with ...
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...
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