The paper reviews and categorizes large language models that combine step‑by‑step reasoning with interactive search, treating retrieval as an action within an iterative agentic loop. It analyzes these systems along four architectural axes—retrieval coupling, controller design, knowledge source, and grounding strictness—while also considering the learning signal as a cross‑cutting factor. The authors conduct controlled ablations and benchmark analyses to clarify how design choices affect search, stopping, and grounding behavior, aiming to provide a unified perspective for future research.
By Biswas Poudel, Nilson Chapagain, Amit Kumar, Xianshun Jiang
arXiv:2608.29596v1 Announce Type: new
Abstract: Autonomous large language model (LLM) agents increasingly face reliability, context consumption, and execution stability bottlenecks when deployed on c...
By Sanket Badhe, Deep Shah, Priyanka Tiwari, Nehal Kathrotia
The paper surveys recent advances in Retrieval Augmented Generation (RAG), a technique that integrates external retrieval into language model generation to reduce hallucinations and keep knowledge current. It introduces a four‑axis taxonomy—efficiency, defense, interactivity, and reasoning—to organize contemporary RAG research, covering retrieval methods, fusion strategies, embedding optimizations, and reinforcement learning policies. The survey also reviews evaluation practices, domain‑specific applications, and architectural variants, while highlighting ongoing challenges such as retrieval quality, reliability, domain adaptation, scalability, and explainability.
By Meghana Sunil, Shravya V, Shravan Venkatraman, Joe Dhanith PR
arXiv:2606. 07299v1 Announce Type: new Abstract: Deep Research (DR) has emerged as a new agentic paradigm to tackle complex, open-ended research tasks, demanding systems that can iteratively frame problems, acquire evidence, verify sources, and synthesize long-form reports.
By Lingyong Yan, Can Xu, Yukun Zhao, Wenxuan Li, Qingyang Chen, Jiulong Wu, Wenli Song, Xiangnan Li, Weixian Shi, Yiqun Chen, Xuchen Ma, Yuchen Li, Jiashu Zhao, Shuaiqiang Wang, Jianmin Wu, Dawei Yin
arXiv:2607. 07321v1 Announce Type: new Abstract: Tool utilization enables Large Language Model (LLM) agents to interact with the real world and resolve complex tasks.
By Haipeng Ding, Yuexiang Xie, Zhewei Wei, Yaliang Li, Bolin Ding
Large language model (LLM) agents are increasingly evaluated on their ability to use tools, plan multi-step tasks, coordinate with other agents, and operate over extended horizons. Reported benchmark gains often obscure recurring failure modes documented across otherwise unrelated evaluation efforts.